diff --git a/sdk/ai/azure-ai-resources-autogen/CHANGELOG.md b/sdk/ai/azure-ai-resources-autogen/CHANGELOG.md new file mode 100644 index 000000000000..628743d283a9 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/CHANGELOG.md @@ -0,0 +1,5 @@ +# Release History + +## 1.0.0b1 (1970-01-01) + +- Initial version diff --git a/sdk/ai/azure-ai-resources-autogen/LICENSE b/sdk/ai/azure-ai-resources-autogen/LICENSE new file mode 100644 index 000000000000..63447fd8bbbf --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/LICENSE @@ -0,0 +1,21 @@ +Copyright (c) Microsoft Corporation. + +MIT License + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/sdk/ai/azure-ai-resources-autogen/MANIFEST.in b/sdk/ai/azure-ai-resources-autogen/MANIFEST.in new file mode 100644 index 000000000000..2b2aa8740169 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/MANIFEST.in @@ -0,0 +1,8 @@ +include *.md +include LICENSE +include azure/ai/resources/autogen/py.typed +recursive-include tests *.py +recursive-include samples *.py *.md +include azure/__init__.py +include azure/ai/__init__.py +include azure/ai/resources/__init__.py \ No newline at end of file diff --git a/sdk/ai/azure-ai-resources-autogen/README.md b/sdk/ai/azure-ai-resources-autogen/README.md new file mode 100644 index 000000000000..32dfb1f3fefa --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/README.md @@ -0,0 +1,80 @@ + + +# Azure Ai Resources Autogen client library for Python + + +## Getting started + +### Install the package + +```bash +python -m pip install azure-ai-resources-autogen +``` + +#### Prequisites + +- Python 3.8 or later is required to use this package. +- You need an [Azure subscription][azure_sub] to use this package. +- An existing Azure Ai Resources Autogen instance. +#### Create with an Azure Active Directory Credential +To use an [Azure Active Directory (AAD) token credential][authenticate_with_token], +provide an instance of the desired credential type obtained from the +[azure-identity][azure_identity_credentials] library. + +To authenticate with AAD, you must first [pip][pip] install [`azure-identity`][azure_identity_pip] + +After setup, you can choose which type of [credential][azure_identity_credentials] from azure.identity to use. +As an example, [DefaultAzureCredential][default_azure_credential] can be used to authenticate the client: + +Set the values of the client ID, tenant ID, and client secret of the AAD application as environment variables: +`AZURE_CLIENT_ID`, `AZURE_TENANT_ID`, `AZURE_CLIENT_SECRET` + +Use the returned token credential to authenticate the client: + +```python +>>> from azure.ai.resources.autogen import MachineLearningServicesClient +>>> from azure.identity import DefaultAzureCredential +>>> client = MachineLearningServicesClient(endpoint='', credential=DefaultAzureCredential()) +``` + +## Examples + +```python +>>> from azure.ai.resources.autogen import MachineLearningServicesClient +>>> from azure.identity import DefaultAzureCredential +>>> from azure.core.exceptions import HttpResponseError + +>>> client = MachineLearningServicesClient(endpoint='', credential=DefaultAzureCredential()) +>>> try: + + except HttpResponseError as e: + print('service responds error: {}'.format(e.response.json())) + +``` + +## Contributing + +This project welcomes contributions and suggestions. Most contributions require +you to agree to a Contributor License Agreement (CLA) declaring that you have +the right to, and actually do, grant us the rights to use your contribution. +For details, visit https://cla.microsoft.com. + +When you submit a pull request, a CLA-bot will automatically determine whether +you need to provide a CLA and decorate the PR appropriately (e.g., label, +comment). Simply follow the instructions provided by the bot. You will only +need to do this once across all repos using our CLA. + +This project has adopted the +[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information, +see the Code of Conduct FAQ or contact opencode@microsoft.com with any +additional questions or comments. + + +[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/ +[authenticate_with_token]: https://docs.microsoft.com/azure/cognitive-services/authentication?tabs=powershell#authenticate-with-an-authentication-token +[azure_identity_credentials]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#credentials +[azure_identity_pip]: https://pypi.org/project/azure-identity/ +[default_azure_credential]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#defaultazurecredential +[pip]: https://pypi.org/project/pip/ +[azure_sub]: https://azure.microsoft.com/free/ + diff --git a/sdk/ai/azure-ai-resources-autogen/_meta.json b/sdk/ai/azure-ai-resources-autogen/_meta.json new file mode 100644 index 000000000000..5e694a7da046 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/_meta.json @@ -0,0 +1,6 @@ +{ + "commit": "5f1dcf05a1adc5595442b87eeb06b9c7a1831f63", + "repository_url": "https://github.com/Azure/azure-rest-api-specs", + "typespec_src": "specification/machinelearningservices/AzureAI.Unified", + "@azure-tools/typespec-python": "0.38.0" +} \ No newline at end of file diff --git a/sdk/ai/azure-ai-resources-autogen/azure/__init__.py b/sdk/ai/azure-ai-resources-autogen/azure/__init__.py new file mode 100644 index 000000000000..d55ccad1f573 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/__init__.py @@ -0,0 +1 @@ +__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/__init__.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/__init__.py new file mode 100644 index 000000000000..d55ccad1f573 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/__init__.py @@ -0,0 +1 @@ +__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/__init__.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/__init__.py new file mode 100644 index 000000000000..d55ccad1f573 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/__init__.py @@ -0,0 +1 @@ +__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/__init__.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/__init__.py new file mode 100644 index 000000000000..64bd396d90c1 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/__init__.py @@ -0,0 +1,32 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +# pylint: disable=wrong-import-position + +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from ._patch import * # pylint: disable=unused-wildcard-import + +from ._client import MachineLearningServicesClient # type: ignore +from ._version import VERSION + +__version__ = VERSION + +try: + from ._patch import __all__ as _patch_all + from ._patch import * +except ImportError: + _patch_all = [] +from ._patch import patch_sdk as _patch_sdk + +__all__ = [ + "MachineLearningServicesClient", +] +__all__.extend([p for p in _patch_all if p not in __all__]) # pyright: ignore + +_patch_sdk() diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_client.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_client.py new file mode 100644 index 000000000000..f582313ebd97 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_client.py @@ -0,0 +1,139 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from copy import deepcopy +from typing import Any, TYPE_CHECKING +from typing_extensions import Self + +from azure.core import PipelineClient +from azure.core.pipeline import policies +from azure.core.rest import HttpRequest, HttpResponse + +from ._configuration import MachineLearningServicesClientConfiguration +from ._serialization import Deserializer, Serializer +from .operations import ( + ConnectionsOperations, + DataOperations, + DataVersionsBaseOperations, + EvaluationsOperations, + IndexesOperations, + MachineLearningServicesClientOperationsMixin, + ModelContainersOperations, + ModelVersionsOperations, +) + +if TYPE_CHECKING: + from azure.core.credentials import TokenCredential + + +class MachineLearningServicesClient( + MachineLearningServicesClientOperationsMixin +): # pylint: disable=too-many-instance-attributes + """MachineLearningServicesClient. + + :ivar connections: ConnectionsOperations operations + :vartype connections: azure.ai.resources.autogen.operations.ConnectionsOperations + :ivar data: DataOperations operations + :vartype data: azure.ai.resources.autogen.operations.DataOperations + :ivar data_versions_base: DataVersionsBaseOperations operations + :vartype data_versions_base: azure.ai.resources.autogen.operations.DataVersionsBaseOperations + :ivar evaluations: EvaluationsOperations operations + :vartype evaluations: azure.ai.resources.autogen.operations.EvaluationsOperations + :ivar indexes: IndexesOperations operations + :vartype indexes: azure.ai.resources.autogen.operations.IndexesOperations + :ivar model_containers: ModelContainersOperations operations + :vartype model_containers: azure.ai.resources.autogen.operations.ModelContainersOperations + :ivar model_versions: ModelVersionsOperations operations + :vartype model_versions: azure.ai.resources.autogen.operations.ModelVersionsOperations + :param endpoint: Global endpoint in the form of: https://[hub-id]/api.ai.azure.com. Required. + :type endpoint: str + :param project_name: The name of the AI project. Required. + :type project_name: str + :param credential: Credential used to authenticate requests to the service. Required. + :type credential: ~azure.core.credentials.TokenCredential + :keyword api_version: The API version to use for this operation. Default value is + "2024-11-01-preview". Note that overriding this default value may result in unsupported + behavior. + :paramtype api_version: str + """ + + def __init__(self, endpoint: str, project_name: str, credential: "TokenCredential", **kwargs: Any) -> None: + _endpoint = "{endpoint}/projects/{projectName}" + self._config = MachineLearningServicesClientConfiguration( + endpoint=endpoint, project_name=project_name, credential=credential, **kwargs + ) + _policies = kwargs.pop("policies", None) + if _policies is None: + _policies = [ + policies.RequestIdPolicy(**kwargs), + self._config.headers_policy, + self._config.user_agent_policy, + self._config.proxy_policy, + policies.ContentDecodePolicy(**kwargs), + self._config.redirect_policy, + self._config.retry_policy, + self._config.authentication_policy, + self._config.custom_hook_policy, + self._config.logging_policy, + policies.DistributedTracingPolicy(**kwargs), + policies.SensitiveHeaderCleanupPolicy(**kwargs) if self._config.redirect_policy else None, + self._config.http_logging_policy, + ] + self._client: PipelineClient = PipelineClient(base_url=_endpoint, policies=_policies, **kwargs) + + self._serialize = Serializer() + self._deserialize = Deserializer() + self._serialize.client_side_validation = False + self.connections = ConnectionsOperations(self._client, self._config, self._serialize, self._deserialize) + self.data = DataOperations(self._client, self._config, self._serialize, self._deserialize) + self.data_versions_base = DataVersionsBaseOperations( + self._client, self._config, self._serialize, self._deserialize + ) + self.evaluations = EvaluationsOperations(self._client, self._config, self._serialize, self._deserialize) + self.indexes = IndexesOperations(self._client, self._config, self._serialize, self._deserialize) + self.model_containers = ModelContainersOperations( + self._client, self._config, self._serialize, self._deserialize + ) + self.model_versions = ModelVersionsOperations(self._client, self._config, self._serialize, self._deserialize) + + def send_request(self, request: HttpRequest, *, stream: bool = False, **kwargs: Any) -> HttpResponse: + """Runs the network request through the client's chained policies. + + >>> from azure.core.rest import HttpRequest + >>> request = HttpRequest("GET", "https://www.example.org/") + + >>> response = client.send_request(request) + + + For more information on this code flow, see https://aka.ms/azsdk/dpcodegen/python/send_request + + :param request: The network request you want to make. Required. + :type request: ~azure.core.rest.HttpRequest + :keyword bool stream: Whether the response payload will be streamed. Defaults to False. + :return: The response of your network call. Does not do error handling on your response. + :rtype: ~azure.core.rest.HttpResponse + """ + + request_copy = deepcopy(request) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + + request_copy.url = self._client.format_url(request_copy.url, **path_format_arguments) + return self._client.send_request(request_copy, stream=stream, **kwargs) # type: ignore + + def close(self) -> None: + self._client.close() + + def __enter__(self) -> Self: + self._client.__enter__() + return self + + def __exit__(self, *exc_details: Any) -> None: + self._client.__exit__(*exc_details) diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_configuration.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_configuration.py new file mode 100644 index 000000000000..8c969f1b611e --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_configuration.py @@ -0,0 +1,69 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from typing import Any, TYPE_CHECKING + +from azure.core.pipeline import policies + +from ._version import VERSION + +if TYPE_CHECKING: + from azure.core.credentials import TokenCredential + + +class MachineLearningServicesClientConfiguration: # pylint: disable=too-many-instance-attributes,name-too-long + """Configuration for MachineLearningServicesClient. + + Note that all parameters used to create this instance are saved as instance + attributes. + + :param endpoint: Global endpoint in the form of: https://[hub-id]/api.ai.azure.com. Required. + :type endpoint: str + :param project_name: The name of the AI project. Required. + :type project_name: str + :param credential: Credential used to authenticate requests to the service. Required. + :type credential: ~azure.core.credentials.TokenCredential + :keyword api_version: The API version to use for this operation. Default value is + "2024-11-01-preview". Note that overriding this default value may result in unsupported + behavior. + :paramtype api_version: str + """ + + def __init__(self, endpoint: str, project_name: str, credential: "TokenCredential", **kwargs: Any) -> None: + api_version: str = kwargs.pop("api_version", "2024-11-01-preview") + + if endpoint is None: + raise ValueError("Parameter 'endpoint' must not be None.") + if project_name is None: + raise ValueError("Parameter 'project_name' must not be None.") + if credential is None: + raise ValueError("Parameter 'credential' must not be None.") + + self.endpoint = endpoint + self.project_name = project_name + self.credential = credential + self.api_version = api_version + self.credential_scopes = kwargs.pop("credential_scopes", ["https://ai.azure.com/.default"]) + kwargs.setdefault("sdk_moniker", "ai-resources-autogen/{}".format(VERSION)) + self.polling_interval = kwargs.get("polling_interval", 30) + self._configure(**kwargs) + + def _configure(self, **kwargs: Any) -> None: + self.user_agent_policy = kwargs.get("user_agent_policy") or policies.UserAgentPolicy(**kwargs) + self.headers_policy = kwargs.get("headers_policy") or policies.HeadersPolicy(**kwargs) + self.proxy_policy = kwargs.get("proxy_policy") or policies.ProxyPolicy(**kwargs) + self.logging_policy = kwargs.get("logging_policy") or policies.NetworkTraceLoggingPolicy(**kwargs) + self.http_logging_policy = kwargs.get("http_logging_policy") or policies.HttpLoggingPolicy(**kwargs) + self.custom_hook_policy = kwargs.get("custom_hook_policy") or policies.CustomHookPolicy(**kwargs) + self.redirect_policy = kwargs.get("redirect_policy") or policies.RedirectPolicy(**kwargs) + self.retry_policy = kwargs.get("retry_policy") or policies.RetryPolicy(**kwargs) + self.authentication_policy = kwargs.get("authentication_policy") + if self.credential and not self.authentication_policy: + self.authentication_policy = policies.BearerTokenCredentialPolicy( + self.credential, *self.credential_scopes, **kwargs + ) diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_model_base.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_model_base.py new file mode 100644 index 000000000000..7f73b97b23ef --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_model_base.py @@ -0,0 +1,1175 @@ +# pylint: disable=too-many-lines +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- +# pylint: disable=protected-access, broad-except + +import copy +import calendar +import decimal +import functools +import sys +import logging +import base64 +import re +import typing +import enum +import email.utils +from datetime import datetime, date, time, timedelta, timezone +from json import JSONEncoder +import xml.etree.ElementTree as ET +from typing_extensions import Self +import isodate +from azure.core.exceptions import DeserializationError +from azure.core import CaseInsensitiveEnumMeta +from azure.core.pipeline import PipelineResponse +from azure.core.serialization import _Null + +if sys.version_info >= (3, 9): + from collections.abc import MutableMapping +else: + from typing import MutableMapping + +_LOGGER = logging.getLogger(__name__) + +__all__ = ["SdkJSONEncoder", "Model", "rest_field", "rest_discriminator"] + +TZ_UTC = timezone.utc +_T = typing.TypeVar("_T") + + +def _timedelta_as_isostr(td: timedelta) -> str: + """Converts a datetime.timedelta object into an ISO 8601 formatted string, e.g. 'P4DT12H30M05S' + + Function adapted from the Tin Can Python project: https://github.com/RusticiSoftware/TinCanPython + + :param timedelta td: The timedelta to convert + :rtype: str + :return: ISO8601 version of this timedelta + """ + + # Split seconds to larger units + seconds = td.total_seconds() + minutes, seconds = divmod(seconds, 60) + hours, minutes = divmod(minutes, 60) + days, hours = divmod(hours, 24) + + days, hours, minutes = list(map(int, (days, hours, minutes))) + seconds = round(seconds, 6) + + # Build date + date_str = "" + if days: + date_str = "%sD" % days + + if hours or minutes or seconds: + # Build time + time_str = "T" + + # Hours + bigger_exists = date_str or hours + if bigger_exists: + time_str += "{:02}H".format(hours) + + # Minutes + bigger_exists = bigger_exists or minutes + if bigger_exists: + time_str += "{:02}M".format(minutes) + + # Seconds + try: + if seconds.is_integer(): + seconds_string = "{:02}".format(int(seconds)) + else: + # 9 chars long w/ leading 0, 6 digits after decimal + seconds_string = "%09.6f" % seconds + # Remove trailing zeros + seconds_string = seconds_string.rstrip("0") + except AttributeError: # int.is_integer() raises + seconds_string = "{:02}".format(seconds) + + time_str += "{}S".format(seconds_string) + else: + time_str = "" + + return "P" + date_str + time_str + + +def _serialize_bytes(o, format: typing.Optional[str] = None) -> str: + encoded = base64.b64encode(o).decode() + if format == "base64url": + return encoded.strip("=").replace("+", "-").replace("/", "_") + return encoded + + +def _serialize_datetime(o, format: typing.Optional[str] = None): + if hasattr(o, "year") and hasattr(o, "hour"): + if format == "rfc7231": + return email.utils.format_datetime(o, usegmt=True) + if format == "unix-timestamp": + return int(calendar.timegm(o.utctimetuple())) + + # astimezone() fails for naive times in Python 2.7, so make make sure o is aware (tzinfo is set) + if not o.tzinfo: + iso_formatted = o.replace(tzinfo=TZ_UTC).isoformat() + else: + iso_formatted = o.astimezone(TZ_UTC).isoformat() + # Replace the trailing "+00:00" UTC offset with "Z" (RFC 3339: https://www.ietf.org/rfc/rfc3339.txt) + return iso_formatted.replace("+00:00", "Z") + # Next try datetime.date or datetime.time + return o.isoformat() + + +def _is_readonly(p): + try: + return p._visibility == ["read"] + except AttributeError: + return False + + +class SdkJSONEncoder(JSONEncoder): + """A JSON encoder that's capable of serializing datetime objects and bytes.""" + + def __init__(self, *args, exclude_readonly: bool = False, format: typing.Optional[str] = None, **kwargs): + super().__init__(*args, **kwargs) + self.exclude_readonly = exclude_readonly + self.format = format + + def default(self, o): # pylint: disable=too-many-return-statements + if _is_model(o): + if self.exclude_readonly: + readonly_props = [p._rest_name for p in o._attr_to_rest_field.values() if _is_readonly(p)] + return {k: v for k, v in o.items() if k not in readonly_props} + return dict(o.items()) + try: + return super(SdkJSONEncoder, self).default(o) + except TypeError: + if isinstance(o, _Null): + return None + if isinstance(o, decimal.Decimal): + return float(o) + if isinstance(o, (bytes, bytearray)): + return _serialize_bytes(o, self.format) + try: + # First try datetime.datetime + return _serialize_datetime(o, self.format) + except AttributeError: + pass + # Last, try datetime.timedelta + try: + return _timedelta_as_isostr(o) + except AttributeError: + # This will be raised when it hits value.total_seconds in the method above + pass + return super(SdkJSONEncoder, self).default(o) + + +_VALID_DATE = re.compile(r"\d{4}[-]\d{2}[-]\d{2}T\d{2}:\d{2}:\d{2}" + r"\.?\d*Z?[-+]?[\d{2}]?:?[\d{2}]?") +_VALID_RFC7231 = re.compile( + r"(Mon|Tue|Wed|Thu|Fri|Sat|Sun),\s\d{2}\s" + r"(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)\s\d{4}\s\d{2}:\d{2}:\d{2}\sGMT" +) + + +def _deserialize_datetime(attr: typing.Union[str, datetime]) -> datetime: + """Deserialize ISO-8601 formatted string into Datetime object. + + :param str attr: response string to be deserialized. + :rtype: ~datetime.datetime + :returns: The datetime object from that input + """ + if isinstance(attr, datetime): + # i'm already deserialized + return attr + attr = attr.upper() + match = _VALID_DATE.match(attr) + if not match: + raise ValueError("Invalid datetime string: " + attr) + + check_decimal = attr.split(".") + if len(check_decimal) > 1: + decimal_str = "" + for digit in check_decimal[1]: + if digit.isdigit(): + decimal_str += digit + else: + break + if len(decimal_str) > 6: + attr = attr.replace(decimal_str, decimal_str[0:6]) + + date_obj = isodate.parse_datetime(attr) + test_utc = date_obj.utctimetuple() + if test_utc.tm_year > 9999 or test_utc.tm_year < 1: + raise OverflowError("Hit max or min date") + return date_obj + + +def _deserialize_datetime_rfc7231(attr: typing.Union[str, datetime]) -> datetime: + """Deserialize RFC7231 formatted string into Datetime object. + + :param str attr: response string to be deserialized. + :rtype: ~datetime.datetime + :returns: The datetime object from that input + """ + if isinstance(attr, datetime): + # i'm already deserialized + return attr + match = _VALID_RFC7231.match(attr) + if not match: + raise ValueError("Invalid datetime string: " + attr) + + return email.utils.parsedate_to_datetime(attr) + + +def _deserialize_datetime_unix_timestamp(attr: typing.Union[float, datetime]) -> datetime: + """Deserialize unix timestamp into Datetime object. + + :param str attr: response string to be deserialized. + :rtype: ~datetime.datetime + :returns: The datetime object from that input + """ + if isinstance(attr, datetime): + # i'm already deserialized + return attr + return datetime.fromtimestamp(attr, TZ_UTC) + + +def _deserialize_date(attr: typing.Union[str, date]) -> date: + """Deserialize ISO-8601 formatted string into Date object. + :param str attr: response string to be deserialized. + :rtype: date + :returns: The date object from that input + """ + # This must NOT use defaultmonth/defaultday. Using None ensure this raises an exception. + if isinstance(attr, date): + return attr + return isodate.parse_date(attr, defaultmonth=None, defaultday=None) # type: ignore + + +def _deserialize_time(attr: typing.Union[str, time]) -> time: + """Deserialize ISO-8601 formatted string into time object. + + :param str attr: response string to be deserialized. + :rtype: datetime.time + :returns: The time object from that input + """ + if isinstance(attr, time): + return attr + return isodate.parse_time(attr) + + +def _deserialize_bytes(attr): + if isinstance(attr, (bytes, bytearray)): + return attr + return bytes(base64.b64decode(attr)) + + +def _deserialize_bytes_base64(attr): + if isinstance(attr, (bytes, bytearray)): + return attr + padding = "=" * (3 - (len(attr) + 3) % 4) # type: ignore + attr = attr + padding # type: ignore + encoded = attr.replace("-", "+").replace("_", "/") + return bytes(base64.b64decode(encoded)) + + +def _deserialize_duration(attr): + if isinstance(attr, timedelta): + return attr + return isodate.parse_duration(attr) + + +def _deserialize_decimal(attr): + if isinstance(attr, decimal.Decimal): + return attr + return decimal.Decimal(str(attr)) + + +def _deserialize_int_as_str(attr): + if isinstance(attr, int): + return attr + return int(attr) + + +_DESERIALIZE_MAPPING = { + datetime: _deserialize_datetime, + date: _deserialize_date, + time: _deserialize_time, + bytes: _deserialize_bytes, + bytearray: _deserialize_bytes, + timedelta: _deserialize_duration, + typing.Any: lambda x: x, + decimal.Decimal: _deserialize_decimal, +} + +_DESERIALIZE_MAPPING_WITHFORMAT = { + "rfc3339": _deserialize_datetime, + "rfc7231": _deserialize_datetime_rfc7231, + "unix-timestamp": _deserialize_datetime_unix_timestamp, + "base64": _deserialize_bytes, + "base64url": _deserialize_bytes_base64, +} + + +def get_deserializer(annotation: typing.Any, rf: typing.Optional["_RestField"] = None): + if annotation is int and rf and rf._format == "str": + return _deserialize_int_as_str + if rf and rf._format: + return _DESERIALIZE_MAPPING_WITHFORMAT.get(rf._format) + return _DESERIALIZE_MAPPING.get(annotation) # pyright: ignore + + +def _get_type_alias_type(module_name: str, alias_name: str): + types = { + k: v + for k, v in sys.modules[module_name].__dict__.items() + if isinstance(v, typing._GenericAlias) # type: ignore + } + if alias_name not in types: + return alias_name + return types[alias_name] + + +def _get_model(module_name: str, model_name: str): + models = {k: v for k, v in sys.modules[module_name].__dict__.items() if isinstance(v, type)} + module_end = module_name.rsplit(".", 1)[0] + models.update({k: v for k, v in sys.modules[module_end].__dict__.items() if isinstance(v, type)}) + if isinstance(model_name, str): + model_name = model_name.split(".")[-1] + if model_name not in models: + return model_name + return models[model_name] + + +_UNSET = object() + + +class _MyMutableMapping(MutableMapping[str, typing.Any]): # pylint: disable=unsubscriptable-object + def __init__(self, data: typing.Dict[str, typing.Any]) -> None: + self._data = data + + def __contains__(self, key: typing.Any) -> bool: + return key in self._data + + def __getitem__(self, key: str) -> typing.Any: + return self._data.__getitem__(key) + + def __setitem__(self, key: str, value: typing.Any) -> None: + self._data.__setitem__(key, value) + + def __delitem__(self, key: str) -> None: + self._data.__delitem__(key) + + def __iter__(self) -> typing.Iterator[typing.Any]: + return self._data.__iter__() + + def __len__(self) -> int: + return self._data.__len__() + + def __ne__(self, other: typing.Any) -> bool: + return not self.__eq__(other) + + def keys(self) -> typing.KeysView[str]: + return self._data.keys() + + def values(self) -> typing.ValuesView[typing.Any]: + return self._data.values() + + def items(self) -> typing.ItemsView[str, typing.Any]: + return self._data.items() + + def get(self, key: str, default: typing.Any = None) -> typing.Any: + try: + return self[key] + except KeyError: + return default + + @typing.overload + def pop(self, key: str) -> typing.Any: ... + + @typing.overload + def pop(self, key: str, default: _T) -> _T: ... + + @typing.overload + def pop(self, key: str, default: typing.Any) -> typing.Any: ... + + def pop(self, key: str, default: typing.Any = _UNSET) -> typing.Any: + if default is _UNSET: + return self._data.pop(key) + return self._data.pop(key, default) + + def popitem(self) -> typing.Tuple[str, typing.Any]: + return self._data.popitem() + + def clear(self) -> None: + self._data.clear() + + def update(self, *args: typing.Any, **kwargs: typing.Any) -> None: + self._data.update(*args, **kwargs) + + @typing.overload + def setdefault(self, key: str, default: None = None) -> None: ... + + @typing.overload + def setdefault(self, key: str, default: typing.Any) -> typing.Any: ... + + def setdefault(self, key: str, default: typing.Any = _UNSET) -> typing.Any: + if default is _UNSET: + return self._data.setdefault(key) + return self._data.setdefault(key, default) + + def __eq__(self, other: typing.Any) -> bool: + try: + other_model = self.__class__(other) + except Exception: + return False + return self._data == other_model._data + + def __repr__(self) -> str: + return str(self._data) + + +def _is_model(obj: typing.Any) -> bool: + return getattr(obj, "_is_model", False) + + +def _serialize(o, format: typing.Optional[str] = None): # pylint: disable=too-many-return-statements + if isinstance(o, list): + return [_serialize(x, format) for x in o] + if isinstance(o, dict): + return {k: _serialize(v, format) for k, v in o.items()} + if isinstance(o, set): + return {_serialize(x, format) for x in o} + if isinstance(o, tuple): + return tuple(_serialize(x, format) for x in o) + if isinstance(o, (bytes, bytearray)): + return _serialize_bytes(o, format) + if isinstance(o, decimal.Decimal): + return float(o) + if isinstance(o, enum.Enum): + return o.value + if isinstance(o, int): + if format == "str": + return str(o) + return o + try: + # First try datetime.datetime + return _serialize_datetime(o, format) + except AttributeError: + pass + # Last, try datetime.timedelta + try: + return _timedelta_as_isostr(o) + except AttributeError: + # This will be raised when it hits value.total_seconds in the method above + pass + return o + + +def _get_rest_field( + attr_to_rest_field: typing.Dict[str, "_RestField"], rest_name: str +) -> typing.Optional["_RestField"]: + try: + return next(rf for rf in attr_to_rest_field.values() if rf._rest_name == rest_name) + except StopIteration: + return None + + +def _create_value(rf: typing.Optional["_RestField"], value: typing.Any) -> typing.Any: + if not rf: + return _serialize(value, None) + if rf._is_multipart_file_input: + return value + if rf._is_model: + return _deserialize(rf._type, value) + if isinstance(value, ET.Element): + value = _deserialize(rf._type, value) + return _serialize(value, rf._format) + + +class Model(_MyMutableMapping): + _is_model = True + # label whether current class's _attr_to_rest_field has been calculated + # could not see _attr_to_rest_field directly because subclass inherits it from parent class + _calculated: typing.Set[str] = set() + + def __init__(self, *args: typing.Any, **kwargs: typing.Any) -> None: + class_name = self.__class__.__name__ + if len(args) > 1: + raise TypeError(f"{class_name}.__init__() takes 2 positional arguments but {len(args) + 1} were given") + dict_to_pass = { + rest_field._rest_name: rest_field._default + for rest_field in self._attr_to_rest_field.values() + if rest_field._default is not _UNSET + } + if args: # pylint: disable=too-many-nested-blocks + if isinstance(args[0], ET.Element): + existed_attr_keys = [] + model_meta = getattr(self, "_xml", {}) + + for rf in self._attr_to_rest_field.values(): + prop_meta = getattr(rf, "_xml", {}) + xml_name = prop_meta.get("name", rf._rest_name) + xml_ns = prop_meta.get("ns", model_meta.get("ns", None)) + if xml_ns: + xml_name = "{" + xml_ns + "}" + xml_name + + # attribute + if prop_meta.get("attribute", False) and args[0].get(xml_name) is not None: + existed_attr_keys.append(xml_name) + dict_to_pass[rf._rest_name] = _deserialize(rf._type, args[0].get(xml_name)) + continue + + # unwrapped element is array + if prop_meta.get("unwrapped", False): + # unwrapped array could either use prop items meta/prop meta + if prop_meta.get("itemsName"): + xml_name = prop_meta.get("itemsName") + xml_ns = prop_meta.get("itemNs") + if xml_ns: + xml_name = "{" + xml_ns + "}" + xml_name + items = args[0].findall(xml_name) # pyright: ignore + if len(items) > 0: + existed_attr_keys.append(xml_name) + dict_to_pass[rf._rest_name] = _deserialize(rf._type, items) + continue + + # text element is primitive type + if prop_meta.get("text", False): + if args[0].text is not None: + dict_to_pass[rf._rest_name] = _deserialize(rf._type, args[0].text) + continue + + # wrapped element could be normal property or array, it should only have one element + item = args[0].find(xml_name) + if item is not None: + existed_attr_keys.append(xml_name) + dict_to_pass[rf._rest_name] = _deserialize(rf._type, item) + + # rest thing is additional properties + for e in args[0]: + if e.tag not in existed_attr_keys: + dict_to_pass[e.tag] = _convert_element(e) + else: + dict_to_pass.update( + {k: _create_value(_get_rest_field(self._attr_to_rest_field, k), v) for k, v in args[0].items()} + ) + else: + non_attr_kwargs = [k for k in kwargs if k not in self._attr_to_rest_field] + if non_attr_kwargs: + # actual type errors only throw the first wrong keyword arg they see, so following that. + raise TypeError(f"{class_name}.__init__() got an unexpected keyword argument '{non_attr_kwargs[0]}'") + dict_to_pass.update( + { + self._attr_to_rest_field[k]._rest_name: _create_value(self._attr_to_rest_field[k], v) + for k, v in kwargs.items() + if v is not None + } + ) + super().__init__(dict_to_pass) + + def copy(self) -> "Model": + return Model(self.__dict__) + + def __new__(cls, *args: typing.Any, **kwargs: typing.Any) -> Self: + if f"{cls.__module__}.{cls.__qualname__}" not in cls._calculated: + # we know the last nine classes in mro are going to be 'Model', '_MyMutableMapping', 'MutableMapping', + # 'Mapping', 'Collection', 'Sized', 'Iterable', 'Container' and 'object' + mros = cls.__mro__[:-9][::-1] # ignore parents, and reverse the mro order + attr_to_rest_field: typing.Dict[str, _RestField] = { # map attribute name to rest_field property + k: v for mro_class in mros for k, v in mro_class.__dict__.items() if k[0] != "_" and hasattr(v, "_type") + } + annotations = { + k: v + for mro_class in mros + if hasattr(mro_class, "__annotations__") + for k, v in mro_class.__annotations__.items() + } + for attr, rf in attr_to_rest_field.items(): + rf._module = cls.__module__ + if not rf._type: + rf._type = rf._get_deserialize_callable_from_annotation(annotations.get(attr, None)) + if not rf._rest_name_input: + rf._rest_name_input = attr + cls._attr_to_rest_field: typing.Dict[str, _RestField] = dict(attr_to_rest_field.items()) + cls._calculated.add(f"{cls.__module__}.{cls.__qualname__}") + + return super().__new__(cls) # pylint: disable=no-value-for-parameter + + def __init_subclass__(cls, discriminator: typing.Optional[str] = None) -> None: + for base in cls.__bases__: + if hasattr(base, "__mapping__"): + base.__mapping__[discriminator or cls.__name__] = cls # type: ignore + + @classmethod + def _get_discriminator(cls, exist_discriminators) -> typing.Optional["_RestField"]: + for v in cls.__dict__.values(): + if isinstance(v, _RestField) and v._is_discriminator and v._rest_name not in exist_discriminators: + return v + return None + + @classmethod + def _deserialize(cls, data, exist_discriminators): + if not hasattr(cls, "__mapping__"): + return cls(data) + discriminator = cls._get_discriminator(exist_discriminators) + if discriminator is None: + return cls(data) + exist_discriminators.append(discriminator._rest_name) + if isinstance(data, ET.Element): + model_meta = getattr(cls, "_xml", {}) + prop_meta = getattr(discriminator, "_xml", {}) + xml_name = prop_meta.get("name", discriminator._rest_name) + xml_ns = prop_meta.get("ns", model_meta.get("ns", None)) + if xml_ns: + xml_name = "{" + xml_ns + "}" + xml_name + + if data.get(xml_name) is not None: + discriminator_value = data.get(xml_name) + else: + discriminator_value = data.find(xml_name).text # pyright: ignore + else: + discriminator_value = data.get(discriminator._rest_name) + mapped_cls = cls.__mapping__.get(discriminator_value, cls) # pyright: ignore + return mapped_cls._deserialize(data, exist_discriminators) + + def as_dict(self, *, exclude_readonly: bool = False) -> typing.Dict[str, typing.Any]: + """Return a dict that can be turned into json using json.dump. + + :keyword bool exclude_readonly: Whether to remove the readonly properties. + :returns: A dict JSON compatible object + :rtype: dict + """ + + result = {} + readonly_props = [] + if exclude_readonly: + readonly_props = [p._rest_name for p in self._attr_to_rest_field.values() if _is_readonly(p)] + for k, v in self.items(): + if exclude_readonly and k in readonly_props: # pyright: ignore + continue + is_multipart_file_input = False + try: + is_multipart_file_input = next( + rf for rf in self._attr_to_rest_field.values() if rf._rest_name == k + )._is_multipart_file_input + except StopIteration: + pass + result[k] = v if is_multipart_file_input else Model._as_dict_value(v, exclude_readonly=exclude_readonly) + return result + + @staticmethod + def _as_dict_value(v: typing.Any, exclude_readonly: bool = False) -> typing.Any: + if v is None or isinstance(v, _Null): + return None + if isinstance(v, (list, tuple, set)): + return type(v)(Model._as_dict_value(x, exclude_readonly=exclude_readonly) for x in v) + if isinstance(v, dict): + return {dk: Model._as_dict_value(dv, exclude_readonly=exclude_readonly) for dk, dv in v.items()} + return v.as_dict(exclude_readonly=exclude_readonly) if hasattr(v, "as_dict") else v + + +def _deserialize_model(model_deserializer: typing.Optional[typing.Callable], obj): + if _is_model(obj): + return obj + return _deserialize(model_deserializer, obj) + + +def _deserialize_with_optional(if_obj_deserializer: typing.Optional[typing.Callable], obj): + if obj is None: + return obj + return _deserialize_with_callable(if_obj_deserializer, obj) + + +def _deserialize_with_union(deserializers, obj): + for deserializer in deserializers: + try: + return _deserialize(deserializer, obj) + except DeserializationError: + pass + raise DeserializationError() + + +def _deserialize_dict( + value_deserializer: typing.Optional[typing.Callable], + module: typing.Optional[str], + obj: typing.Dict[typing.Any, typing.Any], +): + if obj is None: + return obj + if isinstance(obj, ET.Element): + obj = {child.tag: child for child in obj} + return {k: _deserialize(value_deserializer, v, module) for k, v in obj.items()} + + +def _deserialize_multiple_sequence( + entry_deserializers: typing.List[typing.Optional[typing.Callable]], + module: typing.Optional[str], + obj, +): + if obj is None: + return obj + return type(obj)(_deserialize(deserializer, entry, module) for entry, deserializer in zip(obj, entry_deserializers)) + + +def _deserialize_sequence( + deserializer: typing.Optional[typing.Callable], + module: typing.Optional[str], + obj, +): + if obj is None: + return obj + if isinstance(obj, ET.Element): + obj = list(obj) + return type(obj)(_deserialize(deserializer, entry, module) for entry in obj) + + +def _sorted_annotations(types: typing.List[typing.Any]) -> typing.List[typing.Any]: + return sorted( + types, + key=lambda x: hasattr(x, "__name__") and x.__name__.lower() in ("str", "float", "int", "bool"), + ) + + +def _get_deserialize_callable_from_annotation( # pylint: disable=too-many-return-statements, too-many-branches + annotation: typing.Any, + module: typing.Optional[str], + rf: typing.Optional["_RestField"] = None, +) -> typing.Optional[typing.Callable[[typing.Any], typing.Any]]: + if not annotation: + return None + + # is it a type alias? + if isinstance(annotation, str): + if module is not None: + annotation = _get_type_alias_type(module, annotation) + + # is it a forward ref / in quotes? + if isinstance(annotation, (str, typing.ForwardRef)): + try: + model_name = annotation.__forward_arg__ # type: ignore + except AttributeError: + model_name = annotation + if module is not None: + annotation = _get_model(module, model_name) # type: ignore + + try: + if module and _is_model(annotation): + if rf: + rf._is_model = True + + return functools.partial(_deserialize_model, annotation) # pyright: ignore + except Exception: + pass + + # is it a literal? + try: + if annotation.__origin__ is typing.Literal: # pyright: ignore + return None + except AttributeError: + pass + + # is it optional? + try: + if any(a for a in annotation.__args__ if a == type(None)): # pyright: ignore + if len(annotation.__args__) <= 2: # pyright: ignore + if_obj_deserializer = _get_deserialize_callable_from_annotation( + next(a for a in annotation.__args__ if a != type(None)), module, rf # pyright: ignore + ) + + return functools.partial(_deserialize_with_optional, if_obj_deserializer) + # the type is Optional[Union[...]], we need to remove the None type from the Union + annotation_copy = copy.copy(annotation) + annotation_copy.__args__ = [a for a in annotation_copy.__args__ if a != type(None)] # pyright: ignore + return _get_deserialize_callable_from_annotation(annotation_copy, module, rf) + except AttributeError: + pass + + # is it union? + if getattr(annotation, "__origin__", None) is typing.Union: + # initial ordering is we make `string` the last deserialization option, because it is often them most generic + deserializers = [ + _get_deserialize_callable_from_annotation(arg, module, rf) + for arg in _sorted_annotations(annotation.__args__) # pyright: ignore + ] + + return functools.partial(_deserialize_with_union, deserializers) + + try: + if annotation._name == "Dict": # pyright: ignore + value_deserializer = _get_deserialize_callable_from_annotation( + annotation.__args__[1], module, rf # pyright: ignore + ) + + return functools.partial( + _deserialize_dict, + value_deserializer, + module, + ) + except (AttributeError, IndexError): + pass + try: + if annotation._name in ["List", "Set", "Tuple", "Sequence"]: # pyright: ignore + if len(annotation.__args__) > 1: # pyright: ignore + entry_deserializers = [ + _get_deserialize_callable_from_annotation(dt, module, rf) + for dt in annotation.__args__ # pyright: ignore + ] + return functools.partial(_deserialize_multiple_sequence, entry_deserializers, module) + deserializer = _get_deserialize_callable_from_annotation( + annotation.__args__[0], module, rf # pyright: ignore + ) + + return functools.partial(_deserialize_sequence, deserializer, module) + except (TypeError, IndexError, AttributeError, SyntaxError): + pass + + def _deserialize_default( + deserializer, + obj, + ): + if obj is None: + return obj + try: + return _deserialize_with_callable(deserializer, obj) + except Exception: + pass + return obj + + if get_deserializer(annotation, rf): + return functools.partial(_deserialize_default, get_deserializer(annotation, rf)) + + return functools.partial(_deserialize_default, annotation) + + +def _deserialize_with_callable( + deserializer: typing.Optional[typing.Callable[[typing.Any], typing.Any]], + value: typing.Any, +): # pylint: disable=too-many-return-statements + try: + if value is None or isinstance(value, _Null): + return None + if isinstance(value, ET.Element): + if deserializer is str: + return value.text or "" + if deserializer is int: + return int(value.text) if value.text else None + if deserializer is float: + return float(value.text) if value.text else None + if deserializer is bool: + return value.text == "true" if value.text else None + if deserializer is None: + return value + if deserializer in [int, float, bool]: + return deserializer(value) + if isinstance(deserializer, CaseInsensitiveEnumMeta): + try: + return deserializer(value) + except ValueError: + # for unknown value, return raw value + return value + if isinstance(deserializer, type) and issubclass(deserializer, Model): + return deserializer._deserialize(value, []) + return typing.cast(typing.Callable[[typing.Any], typing.Any], deserializer)(value) + except Exception as e: + raise DeserializationError() from e + + +def _deserialize( + deserializer: typing.Any, + value: typing.Any, + module: typing.Optional[str] = None, + rf: typing.Optional["_RestField"] = None, + format: typing.Optional[str] = None, +) -> typing.Any: + if isinstance(value, PipelineResponse): + value = value.http_response.json() + if rf is None and format: + rf = _RestField(format=format) + if not isinstance(deserializer, functools.partial): + deserializer = _get_deserialize_callable_from_annotation(deserializer, module, rf) + return _deserialize_with_callable(deserializer, value) + + +def _failsafe_deserialize( + deserializer: typing.Any, + value: typing.Any, + module: typing.Optional[str] = None, + rf: typing.Optional["_RestField"] = None, + format: typing.Optional[str] = None, +) -> typing.Any: + try: + return _deserialize(deserializer, value, module, rf, format) + except DeserializationError: + _LOGGER.warning( + "Ran into a deserialization error. Ignoring since this is failsafe deserialization", exc_info=True + ) + return None + + +class _RestField: + def __init__( + self, + *, + name: typing.Optional[str] = None, + type: typing.Optional[typing.Callable] = None, # pylint: disable=redefined-builtin + is_discriminator: bool = False, + visibility: typing.Optional[typing.List[str]] = None, + default: typing.Any = _UNSET, + format: typing.Optional[str] = None, + is_multipart_file_input: bool = False, + xml: typing.Optional[typing.Dict[str, typing.Any]] = None, + ): + self._type = type + self._rest_name_input = name + self._module: typing.Optional[str] = None + self._is_discriminator = is_discriminator + self._visibility = visibility + self._is_model = False + self._default = default + self._format = format + self._is_multipart_file_input = is_multipart_file_input + self._xml = xml if xml is not None else {} + + @property + def _class_type(self) -> typing.Any: + return getattr(self._type, "args", [None])[0] + + @property + def _rest_name(self) -> str: + if self._rest_name_input is None: + raise ValueError("Rest name was never set") + return self._rest_name_input + + def __get__(self, obj: Model, type=None): # pylint: disable=redefined-builtin + # by this point, type and rest_name will have a value bc we default + # them in __new__ of the Model class + item = obj.get(self._rest_name) + if item is None: + return item + if self._is_model: + return item + return _deserialize(self._type, _serialize(item, self._format), rf=self) + + def __set__(self, obj: Model, value) -> None: + if value is None: + # we want to wipe out entries if users set attr to None + try: + obj.__delitem__(self._rest_name) + except KeyError: + pass + return + if self._is_model: + if not _is_model(value): + value = _deserialize(self._type, value) + obj.__setitem__(self._rest_name, value) + return + obj.__setitem__(self._rest_name, _serialize(value, self._format)) + + def _get_deserialize_callable_from_annotation( + self, annotation: typing.Any + ) -> typing.Optional[typing.Callable[[typing.Any], typing.Any]]: + return _get_deserialize_callable_from_annotation(annotation, self._module, self) + + +def rest_field( + *, + name: typing.Optional[str] = None, + type: typing.Optional[typing.Callable] = None, # pylint: disable=redefined-builtin + visibility: typing.Optional[typing.List[str]] = None, + default: typing.Any = _UNSET, + format: typing.Optional[str] = None, + is_multipart_file_input: bool = False, + xml: typing.Optional[typing.Dict[str, typing.Any]] = None, +) -> typing.Any: + return _RestField( + name=name, + type=type, + visibility=visibility, + default=default, + format=format, + is_multipart_file_input=is_multipart_file_input, + xml=xml, + ) + + +def rest_discriminator( + *, + name: typing.Optional[str] = None, + type: typing.Optional[typing.Callable] = None, # pylint: disable=redefined-builtin + visibility: typing.Optional[typing.List[str]] = None, + xml: typing.Optional[typing.Dict[str, typing.Any]] = None, +) -> typing.Any: + return _RestField(name=name, type=type, is_discriminator=True, visibility=visibility, xml=xml) + + +def serialize_xml(model: Model, exclude_readonly: bool = False) -> str: + """Serialize a model to XML. + + :param Model model: The model to serialize. + :param bool exclude_readonly: Whether to exclude readonly properties. + :returns: The XML representation of the model. + :rtype: str + """ + return ET.tostring(_get_element(model, exclude_readonly), encoding="unicode") # type: ignore + + +def _get_element( + o: typing.Any, + exclude_readonly: bool = False, + parent_meta: typing.Optional[typing.Dict[str, typing.Any]] = None, + wrapped_element: typing.Optional[ET.Element] = None, +) -> typing.Union[ET.Element, typing.List[ET.Element]]: + if _is_model(o): + model_meta = getattr(o, "_xml", {}) + + # if prop is a model, then use the prop element directly, else generate a wrapper of model + if wrapped_element is None: + wrapped_element = _create_xml_element( + model_meta.get("name", o.__class__.__name__), + model_meta.get("prefix"), + model_meta.get("ns"), + ) + + readonly_props = [] + if exclude_readonly: + readonly_props = [p._rest_name for p in o._attr_to_rest_field.values() if _is_readonly(p)] + + for k, v in o.items(): + # do not serialize readonly properties + if exclude_readonly and k in readonly_props: + continue + + prop_rest_field = _get_rest_field(o._attr_to_rest_field, k) + if prop_rest_field: + prop_meta = getattr(prop_rest_field, "_xml").copy() + # use the wire name as xml name if no specific name is set + if prop_meta.get("name") is None: + prop_meta["name"] = k + else: + # additional properties will not have rest field, use the wire name as xml name + prop_meta = {"name": k} + + # if no ns for prop, use model's + if prop_meta.get("ns") is None and model_meta.get("ns"): + prop_meta["ns"] = model_meta.get("ns") + prop_meta["prefix"] = model_meta.get("prefix") + + if prop_meta.get("unwrapped", False): + # unwrapped could only set on array + wrapped_element.extend(_get_element(v, exclude_readonly, prop_meta)) + elif prop_meta.get("text", False): + # text could only set on primitive type + wrapped_element.text = _get_primitive_type_value(v) + elif prop_meta.get("attribute", False): + xml_name = prop_meta.get("name", k) + if prop_meta.get("ns"): + ET.register_namespace(prop_meta.get("prefix"), prop_meta.get("ns")) # pyright: ignore + xml_name = "{" + prop_meta.get("ns") + "}" + xml_name # pyright: ignore + # attribute should be primitive type + wrapped_element.set(xml_name, _get_primitive_type_value(v)) + else: + # other wrapped prop element + wrapped_element.append(_get_wrapped_element(v, exclude_readonly, prop_meta)) + return wrapped_element + if isinstance(o, list): + return [_get_element(x, exclude_readonly, parent_meta) for x in o] # type: ignore + if isinstance(o, dict): + result = [] + for k, v in o.items(): + result.append( + _get_wrapped_element( + v, + exclude_readonly, + { + "name": k, + "ns": parent_meta.get("ns") if parent_meta else None, + "prefix": parent_meta.get("prefix") if parent_meta else None, + }, + ) + ) + return result + + # primitive case need to create element based on parent_meta + if parent_meta: + return _get_wrapped_element( + o, + exclude_readonly, + { + "name": parent_meta.get("itemsName", parent_meta.get("name")), + "prefix": parent_meta.get("itemsPrefix", parent_meta.get("prefix")), + "ns": parent_meta.get("itemsNs", parent_meta.get("ns")), + }, + ) + + raise ValueError("Could not serialize value into xml: " + o) + + +def _get_wrapped_element( + v: typing.Any, + exclude_readonly: bool, + meta: typing.Optional[typing.Dict[str, typing.Any]], +) -> ET.Element: + wrapped_element = _create_xml_element( + meta.get("name") if meta else None, meta.get("prefix") if meta else None, meta.get("ns") if meta else None + ) + if isinstance(v, (dict, list)): + wrapped_element.extend(_get_element(v, exclude_readonly, meta)) + elif _is_model(v): + _get_element(v, exclude_readonly, meta, wrapped_element) + else: + wrapped_element.text = _get_primitive_type_value(v) + return wrapped_element + + +def _get_primitive_type_value(v) -> str: + if v is True: + return "true" + if v is False: + return "false" + if isinstance(v, _Null): + return "" + return str(v) + + +def _create_xml_element(tag, prefix=None, ns=None): + if prefix and ns: + ET.register_namespace(prefix, ns) + if ns: + return ET.Element("{" + ns + "}" + tag) + return ET.Element(tag) + + +def _deserialize_xml( + deserializer: typing.Any, + value: str, +) -> typing.Any: + element = ET.fromstring(value) # nosec + return _deserialize(deserializer, element) + + +def _convert_element(e: ET.Element): + # dict case + if len(e.attrib) > 0 or len({child.tag for child in e}) > 1: + dict_result: typing.Dict[str, typing.Any] = {} + for child in e: + if dict_result.get(child.tag) is not None: + if isinstance(dict_result[child.tag], list): + dict_result[child.tag].append(_convert_element(child)) + else: + dict_result[child.tag] = [dict_result[child.tag], _convert_element(child)] + else: + dict_result[child.tag] = _convert_element(child) + dict_result.update(e.attrib) + return dict_result + # array case + if len(e) > 0: + array_result: typing.List[typing.Any] = [] + for child in e: + array_result.append(_convert_element(child)) + return array_result + # primitive case + return e.text diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_patch.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_patch.py new file mode 100644 index 000000000000..f7dd32510333 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_patch.py @@ -0,0 +1,20 @@ +# ------------------------------------ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. +# ------------------------------------ +"""Customize generated code here. + +Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize +""" +from typing import List + +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level + + +def patch_sdk(): + """Do not remove from this file. + + `patch_sdk` is a last resort escape hatch that allows you to do customizations + you can't accomplish using the techniques described in + https://aka.ms/azsdk/python/dpcodegen/python/customize + """ diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_serialization.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_serialization.py new file mode 100644 index 000000000000..b24ab2885450 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_serialization.py @@ -0,0 +1,2118 @@ +# pylint: disable=too-many-lines +# -------------------------------------------------------------------------- +# +# Copyright (c) Microsoft Corporation. All rights reserved. +# +# The MIT License (MIT) +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the ""Software""), to +# deal in the Software without restriction, including without limitation the +# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or +# sell copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in +# all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS +# IN THE SOFTWARE. +# +# -------------------------------------------------------------------------- + +# pyright: reportUnnecessaryTypeIgnoreComment=false + +from base64 import b64decode, b64encode +import calendar +import datetime +import decimal +import email +from enum import Enum +import json +import logging +import re +import sys +import codecs +from typing import ( + Dict, + Any, + cast, + Optional, + Union, + AnyStr, + IO, + Mapping, + Callable, + TypeVar, + MutableMapping, + Type, + List, +) + +try: + from urllib import quote # type: ignore +except ImportError: + from urllib.parse import quote +import xml.etree.ElementTree as ET + +import isodate # type: ignore + +from azure.core.exceptions import DeserializationError, SerializationError +from azure.core.serialization import NULL as CoreNull + +_BOM = codecs.BOM_UTF8.decode(encoding="utf-8") + +ModelType = TypeVar("ModelType", bound="Model") +JSON = MutableMapping[str, Any] + + +class RawDeserializer: + + # Accept "text" because we're open minded people... + JSON_REGEXP = re.compile(r"^(application|text)/([a-z+.]+\+)?json$") + + # Name used in context + CONTEXT_NAME = "deserialized_data" + + @classmethod + def deserialize_from_text(cls, data: Optional[Union[AnyStr, IO]], content_type: Optional[str] = None) -> Any: + """Decode data according to content-type. + + Accept a stream of data as well, but will be load at once in memory for now. + + If no content-type, will return the string version (not bytes, not stream) + + :param data: Input, could be bytes or stream (will be decoded with UTF8) or text + :type data: str or bytes or IO + :param str content_type: The content type. + :return: The deserialized data. + :rtype: object + """ + if hasattr(data, "read"): + # Assume a stream + data = cast(IO, data).read() + + if isinstance(data, bytes): + data_as_str = data.decode(encoding="utf-8-sig") + else: + # Explain to mypy the correct type. + data_as_str = cast(str, data) + + # Remove Byte Order Mark if present in string + data_as_str = data_as_str.lstrip(_BOM) + + if content_type is None: + return data + + if cls.JSON_REGEXP.match(content_type): + try: + return json.loads(data_as_str) + except ValueError as err: + raise DeserializationError("JSON is invalid: {}".format(err), err) from err + elif "xml" in (content_type or []): + try: + + try: + if isinstance(data, unicode): # type: ignore + # If I'm Python 2.7 and unicode XML will scream if I try a "fromstring" on unicode string + data_as_str = data_as_str.encode(encoding="utf-8") # type: ignore + except NameError: + pass + + return ET.fromstring(data_as_str) # nosec + except ET.ParseError as err: + # It might be because the server has an issue, and returned JSON with + # content-type XML.... + # So let's try a JSON load, and if it's still broken + # let's flow the initial exception + def _json_attemp(data): + try: + return True, json.loads(data) + except ValueError: + return False, None # Don't care about this one + + success, json_result = _json_attemp(data) + if success: + return json_result + # If i'm here, it's not JSON, it's not XML, let's scream + # and raise the last context in this block (the XML exception) + # The function hack is because Py2.7 messes up with exception + # context otherwise. + _LOGGER.critical("Wasn't XML not JSON, failing") + raise DeserializationError("XML is invalid") from err + elif content_type.startswith("text/"): + return data_as_str + raise DeserializationError("Cannot deserialize content-type: {}".format(content_type)) + + @classmethod + def deserialize_from_http_generics(cls, body_bytes: Optional[Union[AnyStr, IO]], headers: Mapping) -> Any: + """Deserialize from HTTP response. + + Use bytes and headers to NOT use any requests/aiohttp or whatever + specific implementation. + Headers will tested for "content-type" + + :param bytes body_bytes: The body of the response. + :param dict headers: The headers of the response. + :returns: The deserialized data. + :rtype: object + """ + # Try to use content-type from headers if available + content_type = None + if "content-type" in headers: + content_type = headers["content-type"].split(";")[0].strip().lower() + # Ouch, this server did not declare what it sent... + # Let's guess it's JSON... + # Also, since Autorest was considering that an empty body was a valid JSON, + # need that test as well.... + else: + content_type = "application/json" + + if body_bytes: + return cls.deserialize_from_text(body_bytes, content_type) + return None + + +_LOGGER = logging.getLogger(__name__) + +try: + _long_type = long # type: ignore +except NameError: + _long_type = int + + +class UTC(datetime.tzinfo): + """Time Zone info for handling UTC""" + + def utcoffset(self, dt): + """UTF offset for UTC is 0. + + :param datetime.datetime dt: The datetime + :returns: The offset + :rtype: datetime.timedelta + """ + return datetime.timedelta(0) + + def tzname(self, dt): + """Timestamp representation. + + :param datetime.datetime dt: The datetime + :returns: The timestamp representation + :rtype: str + """ + return "Z" + + def dst(self, dt): + """No daylight saving for UTC. + + :param datetime.datetime dt: The datetime + :returns: The daylight saving time + :rtype: datetime.timedelta + """ + return datetime.timedelta(hours=1) + + +try: + from datetime import timezone as _FixedOffset # type: ignore +except ImportError: # Python 2.7 + + class _FixedOffset(datetime.tzinfo): # type: ignore + """Fixed offset in minutes east from UTC. + Copy/pasted from Python doc + :param datetime.timedelta offset: offset in timedelta format + """ + + def __init__(self, offset) -> None: + self.__offset = offset + + def utcoffset(self, dt): + return self.__offset + + def tzname(self, dt): + return str(self.__offset.total_seconds() / 3600) + + def __repr__(self): + return "".format(self.tzname(None)) + + def dst(self, dt): + return datetime.timedelta(0) + + def __getinitargs__(self): + return (self.__offset,) + + +try: + from datetime import timezone + + TZ_UTC = timezone.utc +except ImportError: + TZ_UTC = UTC() # type: ignore + +_FLATTEN = re.compile(r"(? None: + self.additional_properties: Optional[Dict[str, Any]] = {} + for k in kwargs: # pylint: disable=consider-using-dict-items + if k not in self._attribute_map: + _LOGGER.warning("%s is not a known attribute of class %s and will be ignored", k, self.__class__) + elif k in self._validation and self._validation[k].get("readonly", False): + _LOGGER.warning("Readonly attribute %s will be ignored in class %s", k, self.__class__) + else: + setattr(self, k, kwargs[k]) + + def __eq__(self, other: Any) -> bool: + """Compare objects by comparing all attributes. + + :param object other: The object to compare + :returns: True if objects are equal + :rtype: bool + """ + if isinstance(other, self.__class__): + return self.__dict__ == other.__dict__ + return False + + def __ne__(self, other: Any) -> bool: + """Compare objects by comparing all attributes. + + :param object other: The object to compare + :returns: True if objects are not equal + :rtype: bool + """ + return not self.__eq__(other) + + def __str__(self) -> str: + return str(self.__dict__) + + @classmethod + def enable_additional_properties_sending(cls) -> None: + cls._attribute_map["additional_properties"] = {"key": "", "type": "{object}"} + + @classmethod + def is_xml_model(cls) -> bool: + try: + cls._xml_map # type: ignore + except AttributeError: + return False + return True + + @classmethod + def _create_xml_node(cls): + """Create XML node. + + :returns: The XML node + :rtype: xml.etree.ElementTree.Element + """ + try: + xml_map = cls._xml_map # type: ignore + except AttributeError: + xml_map = {} + + return _create_xml_node(xml_map.get("name", cls.__name__), xml_map.get("prefix", None), xml_map.get("ns", None)) + + def serialize(self, keep_readonly: bool = False, **kwargs: Any) -> JSON: + """Return the JSON that would be sent to server from this model. + + This is an alias to `as_dict(full_restapi_key_transformer, keep_readonly=False)`. + + If you want XML serialization, you can pass the kwargs is_xml=True. + + :param bool keep_readonly: If you want to serialize the readonly attributes + :returns: A dict JSON compatible object + :rtype: dict + """ + serializer = Serializer(self._infer_class_models()) + return serializer._serialize( # type: ignore # pylint: disable=protected-access + self, keep_readonly=keep_readonly, **kwargs + ) + + def as_dict( + self, + keep_readonly: bool = True, + key_transformer: Callable[[str, Dict[str, Any], Any], Any] = attribute_transformer, + **kwargs: Any + ) -> JSON: + """Return a dict that can be serialized using json.dump. + + Advanced usage might optionally use a callback as parameter: + + .. code::python + + def my_key_transformer(key, attr_desc, value): + return key + + Key is the attribute name used in Python. Attr_desc + is a dict of metadata. Currently contains 'type' with the + msrest type and 'key' with the RestAPI encoded key. + Value is the current value in this object. + + The string returned will be used to serialize the key. + If the return type is a list, this is considered hierarchical + result dict. + + See the three examples in this file: + + - attribute_transformer + - full_restapi_key_transformer + - last_restapi_key_transformer + + If you want XML serialization, you can pass the kwargs is_xml=True. + + :param bool keep_readonly: If you want to serialize the readonly attributes + :param function key_transformer: A key transformer function. + :returns: A dict JSON compatible object + :rtype: dict + """ + serializer = Serializer(self._infer_class_models()) + return serializer._serialize( # type: ignore # pylint: disable=protected-access + self, key_transformer=key_transformer, keep_readonly=keep_readonly, **kwargs + ) + + @classmethod + def _infer_class_models(cls): + try: + str_models = cls.__module__.rsplit(".", 1)[0] + models = sys.modules[str_models] + client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)} + if cls.__name__ not in client_models: + raise ValueError("Not Autorest generated code") + except Exception: # pylint: disable=broad-exception-caught + # Assume it's not Autorest generated (tests?). Add ourselves as dependencies. + client_models = {cls.__name__: cls} + return client_models + + @classmethod + def deserialize(cls: Type[ModelType], data: Any, content_type: Optional[str] = None) -> ModelType: + """Parse a str using the RestAPI syntax and return a model. + + :param str data: A str using RestAPI structure. JSON by default. + :param str content_type: JSON by default, set application/xml if XML. + :returns: An instance of this model + :raises: DeserializationError if something went wrong + :rtype: ModelType + """ + deserializer = Deserializer(cls._infer_class_models()) + return deserializer(cls.__name__, data, content_type=content_type) # type: ignore + + @classmethod + def from_dict( + cls: Type[ModelType], + data: Any, + key_extractors: Optional[Callable[[str, Dict[str, Any], Any], Any]] = None, + content_type: Optional[str] = None, + ) -> ModelType: + """Parse a dict using given key extractor return a model. + + By default consider key + extractors (rest_key_case_insensitive_extractor, attribute_key_case_insensitive_extractor + and last_rest_key_case_insensitive_extractor) + + :param dict data: A dict using RestAPI structure + :param function key_extractors: A key extractor function. + :param str content_type: JSON by default, set application/xml if XML. + :returns: An instance of this model + :raises: DeserializationError if something went wrong + :rtype: ModelType + """ + deserializer = Deserializer(cls._infer_class_models()) + deserializer.key_extractors = ( # type: ignore + [ # type: ignore + attribute_key_case_insensitive_extractor, + rest_key_case_insensitive_extractor, + last_rest_key_case_insensitive_extractor, + ] + if key_extractors is None + else key_extractors + ) + return deserializer(cls.__name__, data, content_type=content_type) # type: ignore + + @classmethod + def _flatten_subtype(cls, key, objects): + if "_subtype_map" not in cls.__dict__: + return {} + result = dict(cls._subtype_map[key]) + for valuetype in cls._subtype_map[key].values(): + result.update(objects[valuetype]._flatten_subtype(key, objects)) # pylint: disable=protected-access + return result + + @classmethod + def _classify(cls, response, objects): + """Check the class _subtype_map for any child classes. + We want to ignore any inherited _subtype_maps. + + :param dict response: The initial data + :param dict objects: The class objects + :returns: The class to be used + :rtype: class + """ + for subtype_key in cls.__dict__.get("_subtype_map", {}).keys(): + subtype_value = None + + if not isinstance(response, ET.Element): + rest_api_response_key = cls._get_rest_key_parts(subtype_key)[-1] + subtype_value = response.get(rest_api_response_key, None) or response.get(subtype_key, None) + else: + subtype_value = xml_key_extractor(subtype_key, cls._attribute_map[subtype_key], response) + if subtype_value: + # Try to match base class. Can be class name only + # (bug to fix in Autorest to support x-ms-discriminator-name) + if cls.__name__ == subtype_value: + return cls + flatten_mapping_type = cls._flatten_subtype(subtype_key, objects) + try: + return objects[flatten_mapping_type[subtype_value]] # type: ignore + except KeyError: + _LOGGER.warning( + "Subtype value %s has no mapping, use base class %s.", + subtype_value, + cls.__name__, + ) + break + else: + _LOGGER.warning("Discriminator %s is absent or null, use base class %s.", subtype_key, cls.__name__) + break + return cls + + @classmethod + def _get_rest_key_parts(cls, attr_key): + """Get the RestAPI key of this attr, split it and decode part + :param str attr_key: Attribute key must be in attribute_map. + :returns: A list of RestAPI part + :rtype: list + """ + rest_split_key = _FLATTEN.split(cls._attribute_map[attr_key]["key"]) + return [_decode_attribute_map_key(key_part) for key_part in rest_split_key] + + +def _decode_attribute_map_key(key): + """This decode a key in an _attribute_map to the actual key we want to look at + inside the received data. + + :param str key: A key string from the generated code + :returns: The decoded key + :rtype: str + """ + return key.replace("\\.", ".") + + +class Serializer: # pylint: disable=too-many-public-methods + """Request object model serializer.""" + + basic_types = {str: "str", int: "int", bool: "bool", float: "float"} + + _xml_basic_types_serializers = {"bool": lambda x: str(x).lower()} + days = {0: "Mon", 1: "Tue", 2: "Wed", 3: "Thu", 4: "Fri", 5: "Sat", 6: "Sun"} + months = { + 1: "Jan", + 2: "Feb", + 3: "Mar", + 4: "Apr", + 5: "May", + 6: "Jun", + 7: "Jul", + 8: "Aug", + 9: "Sep", + 10: "Oct", + 11: "Nov", + 12: "Dec", + } + validation = { + "min_length": lambda x, y: len(x) < y, + "max_length": lambda x, y: len(x) > y, + "minimum": lambda x, y: x < y, + "maximum": lambda x, y: x > y, + "minimum_ex": lambda x, y: x <= y, + "maximum_ex": lambda x, y: x >= y, + "min_items": lambda x, y: len(x) < y, + "max_items": lambda x, y: len(x) > y, + "pattern": lambda x, y: not re.match(y, x, re.UNICODE), + "unique": lambda x, y: len(x) != len(set(x)), + "multiple": lambda x, y: x % y != 0, + } + + def __init__(self, classes: Optional[Mapping[str, type]] = None) -> None: + self.serialize_type = { + "iso-8601": Serializer.serialize_iso, + "rfc-1123": Serializer.serialize_rfc, + "unix-time": Serializer.serialize_unix, + "duration": Serializer.serialize_duration, + "date": Serializer.serialize_date, + "time": Serializer.serialize_time, + "decimal": Serializer.serialize_decimal, + "long": Serializer.serialize_long, + "bytearray": Serializer.serialize_bytearray, + "base64": Serializer.serialize_base64, + "object": self.serialize_object, + "[]": self.serialize_iter, + "{}": self.serialize_dict, + } + self.dependencies: Dict[str, type] = dict(classes) if classes else {} + self.key_transformer = full_restapi_key_transformer + self.client_side_validation = True + + def _serialize( # pylint: disable=too-many-nested-blocks, too-many-branches, too-many-statements, too-many-locals + self, target_obj, data_type=None, **kwargs + ): + """Serialize data into a string according to type. + + :param object target_obj: The data to be serialized. + :param str data_type: The type to be serialized from. + :rtype: str, dict + :raises: SerializationError if serialization fails. + :returns: The serialized data. + """ + key_transformer = kwargs.get("key_transformer", self.key_transformer) + keep_readonly = kwargs.get("keep_readonly", False) + if target_obj is None: + return None + + attr_name = None + class_name = target_obj.__class__.__name__ + + if data_type: + return self.serialize_data(target_obj, data_type, **kwargs) + + if not hasattr(target_obj, "_attribute_map"): + data_type = type(target_obj).__name__ + if data_type in self.basic_types.values(): + return self.serialize_data(target_obj, data_type, **kwargs) + + # Force "is_xml" kwargs if we detect a XML model + try: + is_xml_model_serialization = kwargs["is_xml"] + except KeyError: + is_xml_model_serialization = kwargs.setdefault("is_xml", target_obj.is_xml_model()) + + serialized = {} + if is_xml_model_serialization: + serialized = target_obj._create_xml_node() # pylint: disable=protected-access + try: + attributes = target_obj._attribute_map # pylint: disable=protected-access + for attr, attr_desc in attributes.items(): + attr_name = attr + if not keep_readonly and target_obj._validation.get( # pylint: disable=protected-access + attr_name, {} + ).get("readonly", False): + continue + + if attr_name == "additional_properties" and attr_desc["key"] == "": + if target_obj.additional_properties is not None: + serialized.update(target_obj.additional_properties) + continue + try: + + orig_attr = getattr(target_obj, attr) + if is_xml_model_serialization: + pass # Don't provide "transformer" for XML for now. Keep "orig_attr" + else: # JSON + keys, orig_attr = key_transformer(attr, attr_desc.copy(), orig_attr) + keys = keys if isinstance(keys, list) else [keys] + + kwargs["serialization_ctxt"] = attr_desc + new_attr = self.serialize_data(orig_attr, attr_desc["type"], **kwargs) + + if is_xml_model_serialization: + xml_desc = attr_desc.get("xml", {}) + xml_name = xml_desc.get("name", attr_desc["key"]) + xml_prefix = xml_desc.get("prefix", None) + xml_ns = xml_desc.get("ns", None) + if xml_desc.get("attr", False): + if xml_ns: + ET.register_namespace(xml_prefix, xml_ns) + xml_name = "{{{}}}{}".format(xml_ns, xml_name) + serialized.set(xml_name, new_attr) # type: ignore + continue + if xml_desc.get("text", False): + serialized.text = new_attr # type: ignore + continue + if isinstance(new_attr, list): + serialized.extend(new_attr) # type: ignore + elif isinstance(new_attr, ET.Element): + # If the down XML has no XML/Name, + # we MUST replace the tag with the local tag. But keeping the namespaces. + if "name" not in getattr(orig_attr, "_xml_map", {}): + splitted_tag = new_attr.tag.split("}") + if len(splitted_tag) == 2: # Namespace + new_attr.tag = "}".join([splitted_tag[0], xml_name]) + else: + new_attr.tag = xml_name + serialized.append(new_attr) # type: ignore + else: # That's a basic type + # Integrate namespace if necessary + local_node = _create_xml_node(xml_name, xml_prefix, xml_ns) + local_node.text = str(new_attr) + serialized.append(local_node) # type: ignore + else: # JSON + for k in reversed(keys): # type: ignore + new_attr = {k: new_attr} + + _new_attr = new_attr + _serialized = serialized + for k in keys: # type: ignore + if k not in _serialized: + _serialized.update(_new_attr) # type: ignore + _new_attr = _new_attr[k] # type: ignore + _serialized = _serialized[k] + except ValueError as err: + if isinstance(err, SerializationError): + raise + + except (AttributeError, KeyError, TypeError) as err: + msg = "Attribute {} in object {} cannot be serialized.\n{}".format(attr_name, class_name, str(target_obj)) + raise SerializationError(msg) from err + return serialized + + def body(self, data, data_type, **kwargs): + """Serialize data intended for a request body. + + :param object data: The data to be serialized. + :param str data_type: The type to be serialized from. + :rtype: dict + :raises: SerializationError if serialization fails. + :raises: ValueError if data is None + :returns: The serialized request body + """ + + # Just in case this is a dict + internal_data_type_str = data_type.strip("[]{}") + internal_data_type = self.dependencies.get(internal_data_type_str, None) + try: + is_xml_model_serialization = kwargs["is_xml"] + except KeyError: + if internal_data_type and issubclass(internal_data_type, Model): + is_xml_model_serialization = kwargs.setdefault("is_xml", internal_data_type.is_xml_model()) + else: + is_xml_model_serialization = False + if internal_data_type and not isinstance(internal_data_type, Enum): + try: + deserializer = Deserializer(self.dependencies) + # Since it's on serialization, it's almost sure that format is not JSON REST + # We're not able to deal with additional properties for now. + deserializer.additional_properties_detection = False + if is_xml_model_serialization: + deserializer.key_extractors = [ # type: ignore + attribute_key_case_insensitive_extractor, + ] + else: + deserializer.key_extractors = [ + rest_key_case_insensitive_extractor, + attribute_key_case_insensitive_extractor, + last_rest_key_case_insensitive_extractor, + ] + data = deserializer._deserialize(data_type, data) # pylint: disable=protected-access + except DeserializationError as err: + raise SerializationError("Unable to build a model: " + str(err)) from err + + return self._serialize(data, data_type, **kwargs) + + def url(self, name, data, data_type, **kwargs): + """Serialize data intended for a URL path. + + :param str name: The name of the URL path parameter. + :param object data: The data to be serialized. + :param str data_type: The type to be serialized from. + :rtype: str + :returns: The serialized URL path + :raises: TypeError if serialization fails. + :raises: ValueError if data is None + """ + try: + output = self.serialize_data(data, data_type, **kwargs) + if data_type == "bool": + output = json.dumps(output) + + if kwargs.get("skip_quote") is True: + output = str(output) + output = output.replace("{", quote("{")).replace("}", quote("}")) + else: + output = quote(str(output), safe="") + except SerializationError as exc: + raise TypeError("{} must be type {}.".format(name, data_type)) from exc + return output + + def query(self, name, data, data_type, **kwargs): + """Serialize data intended for a URL query. + + :param str name: The name of the query parameter. + :param object data: The data to be serialized. + :param str data_type: The type to be serialized from. + :rtype: str, list + :raises: TypeError if serialization fails. + :raises: ValueError if data is None + :returns: The serialized query parameter + """ + try: + # Treat the list aside, since we don't want to encode the div separator + if data_type.startswith("["): + internal_data_type = data_type[1:-1] + do_quote = not kwargs.get("skip_quote", False) + return self.serialize_iter(data, internal_data_type, do_quote=do_quote, **kwargs) + + # Not a list, regular serialization + output = self.serialize_data(data, data_type, **kwargs) + if data_type == "bool": + output = json.dumps(output) + if kwargs.get("skip_quote") is True: + output = str(output) + else: + output = quote(str(output), safe="") + except SerializationError as exc: + raise TypeError("{} must be type {}.".format(name, data_type)) from exc + return str(output) + + def header(self, name, data, data_type, **kwargs): + """Serialize data intended for a request header. + + :param str name: The name of the header. + :param object data: The data to be serialized. + :param str data_type: The type to be serialized from. + :rtype: str + :raises: TypeError if serialization fails. + :raises: ValueError if data is None + :returns: The serialized header + """ + try: + if data_type in ["[str]"]: + data = ["" if d is None else d for d in data] + + output = self.serialize_data(data, data_type, **kwargs) + if data_type == "bool": + output = json.dumps(output) + except SerializationError as exc: + raise TypeError("{} must be type {}.".format(name, data_type)) from exc + return str(output) + + def serialize_data(self, data, data_type, **kwargs): + """Serialize generic data according to supplied data type. + + :param object data: The data to be serialized. + :param str data_type: The type to be serialized from. + :raises: AttributeError if required data is None. + :raises: ValueError if data is None + :raises: SerializationError if serialization fails. + :returns: The serialized data. + :rtype: str, int, float, bool, dict, list + """ + if data is None: + raise ValueError("No value for given attribute") + + try: + if data is CoreNull: + return None + if data_type in self.basic_types.values(): + return self.serialize_basic(data, data_type, **kwargs) + + if data_type in self.serialize_type: + return self.serialize_type[data_type](data, **kwargs) + + # If dependencies is empty, try with current data class + # It has to be a subclass of Enum anyway + enum_type = self.dependencies.get(data_type, data.__class__) + if issubclass(enum_type, Enum): + return Serializer.serialize_enum(data, enum_obj=enum_type) + + iter_type = data_type[0] + data_type[-1] + if iter_type in self.serialize_type: + return self.serialize_type[iter_type](data, data_type[1:-1], **kwargs) + + except (ValueError, TypeError) as err: + msg = "Unable to serialize value: {!r} as type: {!r}." + raise SerializationError(msg.format(data, data_type)) from err + return self._serialize(data, **kwargs) + + @classmethod + def _get_custom_serializers(cls, data_type, **kwargs): # pylint: disable=inconsistent-return-statements + custom_serializer = kwargs.get("basic_types_serializers", {}).get(data_type) + if custom_serializer: + return custom_serializer + if kwargs.get("is_xml", False): + return cls._xml_basic_types_serializers.get(data_type) + + @classmethod + def serialize_basic(cls, data, data_type, **kwargs): + """Serialize basic builting data type. + Serializes objects to str, int, float or bool. + + Possible kwargs: + - basic_types_serializers dict[str, callable] : If set, use the callable as serializer + - is_xml bool : If set, use xml_basic_types_serializers + + :param obj data: Object to be serialized. + :param str data_type: Type of object in the iterable. + :rtype: str, int, float, bool + :return: serialized object + """ + custom_serializer = cls._get_custom_serializers(data_type, **kwargs) + if custom_serializer: + return custom_serializer(data) + if data_type == "str": + return cls.serialize_unicode(data) + return eval(data_type)(data) # nosec # pylint: disable=eval-used + + @classmethod + def serialize_unicode(cls, data): + """Special handling for serializing unicode strings in Py2. + Encode to UTF-8 if unicode, otherwise handle as a str. + + :param str data: Object to be serialized. + :rtype: str + :return: serialized object + """ + try: # If I received an enum, return its value + return data.value + except AttributeError: + pass + + try: + if isinstance(data, unicode): # type: ignore + # Don't change it, JSON and XML ElementTree are totally able + # to serialize correctly u'' strings + return data + except NameError: + return str(data) + return str(data) + + def serialize_iter(self, data, iter_type, div=None, **kwargs): + """Serialize iterable. + + Supported kwargs: + - serialization_ctxt dict : The current entry of _attribute_map, or same format. + serialization_ctxt['type'] should be same as data_type. + - is_xml bool : If set, serialize as XML + + :param list data: Object to be serialized. + :param str iter_type: Type of object in the iterable. + :param str div: If set, this str will be used to combine the elements + in the iterable into a combined string. Default is 'None'. + Defaults to False. + :rtype: list, str + :return: serialized iterable + """ + if isinstance(data, str): + raise SerializationError("Refuse str type as a valid iter type.") + + serialization_ctxt = kwargs.get("serialization_ctxt", {}) + is_xml = kwargs.get("is_xml", False) + + serialized = [] + for d in data: + try: + serialized.append(self.serialize_data(d, iter_type, **kwargs)) + except ValueError as err: + if isinstance(err, SerializationError): + raise + serialized.append(None) + + if kwargs.get("do_quote", False): + serialized = ["" if s is None else quote(str(s), safe="") for s in serialized] + + if div: + serialized = ["" if s is None else str(s) for s in serialized] + serialized = div.join(serialized) + + if "xml" in serialization_ctxt or is_xml: + # XML serialization is more complicated + xml_desc = serialization_ctxt.get("xml", {}) + xml_name = xml_desc.get("name") + if not xml_name: + xml_name = serialization_ctxt["key"] + + # Create a wrap node if necessary (use the fact that Element and list have "append") + is_wrapped = xml_desc.get("wrapped", False) + node_name = xml_desc.get("itemsName", xml_name) + if is_wrapped: + final_result = _create_xml_node(xml_name, xml_desc.get("prefix", None), xml_desc.get("ns", None)) + else: + final_result = [] + # All list elements to "local_node" + for el in serialized: + if isinstance(el, ET.Element): + el_node = el + else: + el_node = _create_xml_node(node_name, xml_desc.get("prefix", None), xml_desc.get("ns", None)) + if el is not None: # Otherwise it writes "None" :-p + el_node.text = str(el) + final_result.append(el_node) + return final_result + return serialized + + def serialize_dict(self, attr, dict_type, **kwargs): + """Serialize a dictionary of objects. + + :param dict attr: Object to be serialized. + :param str dict_type: Type of object in the dictionary. + :rtype: dict + :return: serialized dictionary + """ + serialization_ctxt = kwargs.get("serialization_ctxt", {}) + serialized = {} + for key, value in attr.items(): + try: + serialized[self.serialize_unicode(key)] = self.serialize_data(value, dict_type, **kwargs) + except ValueError as err: + if isinstance(err, SerializationError): + raise + serialized[self.serialize_unicode(key)] = None + + if "xml" in serialization_ctxt: + # XML serialization is more complicated + xml_desc = serialization_ctxt["xml"] + xml_name = xml_desc["name"] + + final_result = _create_xml_node(xml_name, xml_desc.get("prefix", None), xml_desc.get("ns", None)) + for key, value in serialized.items(): + ET.SubElement(final_result, key).text = value + return final_result + + return serialized + + def serialize_object(self, attr, **kwargs): # pylint: disable=too-many-return-statements + """Serialize a generic object. + This will be handled as a dictionary. If object passed in is not + a basic type (str, int, float, dict, list) it will simply be + cast to str. + + :param dict attr: Object to be serialized. + :rtype: dict or str + :return: serialized object + """ + if attr is None: + return None + if isinstance(attr, ET.Element): + return attr + obj_type = type(attr) + if obj_type in self.basic_types: + return self.serialize_basic(attr, self.basic_types[obj_type], **kwargs) + if obj_type is _long_type: + return self.serialize_long(attr) + if obj_type is str: + return self.serialize_unicode(attr) + if obj_type is datetime.datetime: + return self.serialize_iso(attr) + if obj_type is datetime.date: + return self.serialize_date(attr) + if obj_type is datetime.time: + return self.serialize_time(attr) + if obj_type is datetime.timedelta: + return self.serialize_duration(attr) + if obj_type is decimal.Decimal: + return self.serialize_decimal(attr) + + # If it's a model or I know this dependency, serialize as a Model + if obj_type in self.dependencies.values() or isinstance(attr, Model): + return self._serialize(attr) + + if obj_type == dict: + serialized = {} + for key, value in attr.items(): + try: + serialized[self.serialize_unicode(key)] = self.serialize_object(value, **kwargs) + except ValueError: + serialized[self.serialize_unicode(key)] = None + return serialized + + if obj_type == list: + serialized = [] + for obj in attr: + try: + serialized.append(self.serialize_object(obj, **kwargs)) + except ValueError: + pass + return serialized + return str(attr) + + @staticmethod + def serialize_enum(attr, enum_obj=None): + try: + result = attr.value + except AttributeError: + result = attr + try: + enum_obj(result) # type: ignore + return result + except ValueError as exc: + for enum_value in enum_obj: # type: ignore + if enum_value.value.lower() == str(attr).lower(): + return enum_value.value + error = "{!r} is not valid value for enum {!r}" + raise SerializationError(error.format(attr, enum_obj)) from exc + + @staticmethod + def serialize_bytearray(attr, **kwargs): # pylint: disable=unused-argument + """Serialize bytearray into base-64 string. + + :param str attr: Object to be serialized. + :rtype: str + :return: serialized base64 + """ + return b64encode(attr).decode() + + @staticmethod + def serialize_base64(attr, **kwargs): # pylint: disable=unused-argument + """Serialize str into base-64 string. + + :param str attr: Object to be serialized. + :rtype: str + :return: serialized base64 + """ + encoded = b64encode(attr).decode("ascii") + return encoded.strip("=").replace("+", "-").replace("/", "_") + + @staticmethod + def serialize_decimal(attr, **kwargs): # pylint: disable=unused-argument + """Serialize Decimal object to float. + + :param decimal attr: Object to be serialized. + :rtype: float + :return: serialized decimal + """ + return float(attr) + + @staticmethod + def serialize_long(attr, **kwargs): # pylint: disable=unused-argument + """Serialize long (Py2) or int (Py3). + + :param int attr: Object to be serialized. + :rtype: int/long + :return: serialized long + """ + return _long_type(attr) + + @staticmethod + def serialize_date(attr, **kwargs): # pylint: disable=unused-argument + """Serialize Date object into ISO-8601 formatted string. + + :param Date attr: Object to be serialized. + :rtype: str + :return: serialized date + """ + if isinstance(attr, str): + attr = isodate.parse_date(attr) + t = "{:04}-{:02}-{:02}".format(attr.year, attr.month, attr.day) + return t + + @staticmethod + def serialize_time(attr, **kwargs): # pylint: disable=unused-argument + """Serialize Time object into ISO-8601 formatted string. + + :param datetime.time attr: Object to be serialized. + :rtype: str + :return: serialized time + """ + if isinstance(attr, str): + attr = isodate.parse_time(attr) + t = "{:02}:{:02}:{:02}".format(attr.hour, attr.minute, attr.second) + if attr.microsecond: + t += ".{:02}".format(attr.microsecond) + return t + + @staticmethod + def serialize_duration(attr, **kwargs): # pylint: disable=unused-argument + """Serialize TimeDelta object into ISO-8601 formatted string. + + :param TimeDelta attr: Object to be serialized. + :rtype: str + :return: serialized duration + """ + if isinstance(attr, str): + attr = isodate.parse_duration(attr) + return isodate.duration_isoformat(attr) + + @staticmethod + def serialize_rfc(attr, **kwargs): # pylint: disable=unused-argument + """Serialize Datetime object into RFC-1123 formatted string. + + :param Datetime attr: Object to be serialized. + :rtype: str + :raises: TypeError if format invalid. + :return: serialized rfc + """ + try: + if not attr.tzinfo: + _LOGGER.warning("Datetime with no tzinfo will be considered UTC.") + utc = attr.utctimetuple() + except AttributeError as exc: + raise TypeError("RFC1123 object must be valid Datetime object.") from exc + + return "{}, {:02} {} {:04} {:02}:{:02}:{:02} GMT".format( + Serializer.days[utc.tm_wday], + utc.tm_mday, + Serializer.months[utc.tm_mon], + utc.tm_year, + utc.tm_hour, + utc.tm_min, + utc.tm_sec, + ) + + @staticmethod + def serialize_iso(attr, **kwargs): # pylint: disable=unused-argument + """Serialize Datetime object into ISO-8601 formatted string. + + :param Datetime attr: Object to be serialized. + :rtype: str + :raises: SerializationError if format invalid. + :return: serialized iso + """ + if isinstance(attr, str): + attr = isodate.parse_datetime(attr) + try: + if not attr.tzinfo: + _LOGGER.warning("Datetime with no tzinfo will be considered UTC.") + utc = attr.utctimetuple() + if utc.tm_year > 9999 or utc.tm_year < 1: + raise OverflowError("Hit max or min date") + + microseconds = str(attr.microsecond).rjust(6, "0").rstrip("0").ljust(3, "0") + if microseconds: + microseconds = "." + microseconds + date = "{:04}-{:02}-{:02}T{:02}:{:02}:{:02}".format( + utc.tm_year, utc.tm_mon, utc.tm_mday, utc.tm_hour, utc.tm_min, utc.tm_sec + ) + return date + microseconds + "Z" + except (ValueError, OverflowError) as err: + msg = "Unable to serialize datetime object." + raise SerializationError(msg) from err + except AttributeError as err: + msg = "ISO-8601 object must be valid Datetime object." + raise TypeError(msg) from err + + @staticmethod + def serialize_unix(attr, **kwargs): # pylint: disable=unused-argument + """Serialize Datetime object into IntTime format. + This is represented as seconds. + + :param Datetime attr: Object to be serialized. + :rtype: int + :raises: SerializationError if format invalid + :return: serialied unix + """ + if isinstance(attr, int): + return attr + try: + if not attr.tzinfo: + _LOGGER.warning("Datetime with no tzinfo will be considered UTC.") + return int(calendar.timegm(attr.utctimetuple())) + except AttributeError as exc: + raise TypeError("Unix time object must be valid Datetime object.") from exc + + +def rest_key_extractor(attr, attr_desc, data): # pylint: disable=unused-argument + key = attr_desc["key"] + working_data = data + + while "." in key: + # Need the cast, as for some reasons "split" is typed as list[str | Any] + dict_keys = cast(List[str], _FLATTEN.split(key)) + if len(dict_keys) == 1: + key = _decode_attribute_map_key(dict_keys[0]) + break + working_key = _decode_attribute_map_key(dict_keys[0]) + working_data = working_data.get(working_key, data) + if working_data is None: + # If at any point while following flatten JSON path see None, it means + # that all properties under are None as well + return None + key = ".".join(dict_keys[1:]) + + return working_data.get(key) + + +def rest_key_case_insensitive_extractor( # pylint: disable=unused-argument, inconsistent-return-statements + attr, attr_desc, data +): + key = attr_desc["key"] + working_data = data + + while "." in key: + dict_keys = _FLATTEN.split(key) + if len(dict_keys) == 1: + key = _decode_attribute_map_key(dict_keys[0]) + break + working_key = _decode_attribute_map_key(dict_keys[0]) + working_data = attribute_key_case_insensitive_extractor(working_key, None, working_data) + if working_data is None: + # If at any point while following flatten JSON path see None, it means + # that all properties under are None as well + return None + key = ".".join(dict_keys[1:]) + + if working_data: + return attribute_key_case_insensitive_extractor(key, None, working_data) + + +def last_rest_key_extractor(attr, attr_desc, data): # pylint: disable=unused-argument + """Extract the attribute in "data" based on the last part of the JSON path key. + + :param str attr: The attribute to extract + :param dict attr_desc: The attribute description + :param dict data: The data to extract from + :rtype: object + :returns: The extracted attribute + """ + key = attr_desc["key"] + dict_keys = _FLATTEN.split(key) + return attribute_key_extractor(dict_keys[-1], None, data) + + +def last_rest_key_case_insensitive_extractor(attr, attr_desc, data): # pylint: disable=unused-argument + """Extract the attribute in "data" based on the last part of the JSON path key. + + This is the case insensitive version of "last_rest_key_extractor" + :param str attr: The attribute to extract + :param dict attr_desc: The attribute description + :param dict data: The data to extract from + :rtype: object + :returns: The extracted attribute + """ + key = attr_desc["key"] + dict_keys = _FLATTEN.split(key) + return attribute_key_case_insensitive_extractor(dict_keys[-1], None, data) + + +def attribute_key_extractor(attr, _, data): + return data.get(attr) + + +def attribute_key_case_insensitive_extractor(attr, _, data): + found_key = None + lower_attr = attr.lower() + for key in data: + if lower_attr == key.lower(): + found_key = key + break + + return data.get(found_key) + + +def _extract_name_from_internal_type(internal_type): + """Given an internal type XML description, extract correct XML name with namespace. + + :param dict internal_type: An model type + :rtype: tuple + :returns: A tuple XML name + namespace dict + """ + internal_type_xml_map = getattr(internal_type, "_xml_map", {}) + xml_name = internal_type_xml_map.get("name", internal_type.__name__) + xml_ns = internal_type_xml_map.get("ns", None) + if xml_ns: + xml_name = "{{{}}}{}".format(xml_ns, xml_name) + return xml_name + + +def xml_key_extractor(attr, attr_desc, data): # pylint: disable=unused-argument,too-many-return-statements + if isinstance(data, dict): + return None + + # Test if this model is XML ready first + if not isinstance(data, ET.Element): + return None + + xml_desc = attr_desc.get("xml", {}) + xml_name = xml_desc.get("name", attr_desc["key"]) + + # Look for a children + is_iter_type = attr_desc["type"].startswith("[") + is_wrapped = xml_desc.get("wrapped", False) + internal_type = attr_desc.get("internalType", None) + internal_type_xml_map = getattr(internal_type, "_xml_map", {}) + + # Integrate namespace if necessary + xml_ns = xml_desc.get("ns", internal_type_xml_map.get("ns", None)) + if xml_ns: + xml_name = "{{{}}}{}".format(xml_ns, xml_name) + + # If it's an attribute, that's simple + if xml_desc.get("attr", False): + return data.get(xml_name) + + # If it's x-ms-text, that's simple too + if xml_desc.get("text", False): + return data.text + + # Scenario where I take the local name: + # - Wrapped node + # - Internal type is an enum (considered basic types) + # - Internal type has no XML/Name node + if is_wrapped or (internal_type and (issubclass(internal_type, Enum) or "name" not in internal_type_xml_map)): + children = data.findall(xml_name) + # If internal type has a local name and it's not a list, I use that name + elif not is_iter_type and internal_type and "name" in internal_type_xml_map: + xml_name = _extract_name_from_internal_type(internal_type) + children = data.findall(xml_name) + # That's an array + else: + if internal_type: # Complex type, ignore itemsName and use the complex type name + items_name = _extract_name_from_internal_type(internal_type) + else: + items_name = xml_desc.get("itemsName", xml_name) + children = data.findall(items_name) + + if len(children) == 0: + if is_iter_type: + if is_wrapped: + return None # is_wrapped no node, we want None + return [] # not wrapped, assume empty list + return None # Assume it's not there, maybe an optional node. + + # If is_iter_type and not wrapped, return all found children + if is_iter_type: + if not is_wrapped: + return children + # Iter and wrapped, should have found one node only (the wrap one) + if len(children) != 1: + raise DeserializationError( + "Tried to deserialize an array not wrapped, and found several nodes '{}'. Maybe you should declare this array as wrapped?".format( # pylint: disable=line-too-long + xml_name + ) + ) + return list(children[0]) # Might be empty list and that's ok. + + # Here it's not a itertype, we should have found one element only or empty + if len(children) > 1: + raise DeserializationError("Find several XML '{}' where it was not expected".format(xml_name)) + return children[0] + + +class Deserializer: + """Response object model deserializer. + + :param dict classes: Class type dictionary for deserializing complex types. + :ivar list key_extractors: Ordered list of extractors to be used by this deserializer. + """ + + basic_types = {str: "str", int: "int", bool: "bool", float: "float"} + + valid_date = re.compile(r"\d{4}[-]\d{2}[-]\d{2}T\d{2}:\d{2}:\d{2}\.?\d*Z?[-+]?[\d{2}]?:?[\d{2}]?") + + def __init__(self, classes: Optional[Mapping[str, type]] = None) -> None: + self.deserialize_type = { + "iso-8601": Deserializer.deserialize_iso, + "rfc-1123": Deserializer.deserialize_rfc, + "unix-time": Deserializer.deserialize_unix, + "duration": Deserializer.deserialize_duration, + "date": Deserializer.deserialize_date, + "time": Deserializer.deserialize_time, + "decimal": Deserializer.deserialize_decimal, + "long": Deserializer.deserialize_long, + "bytearray": Deserializer.deserialize_bytearray, + "base64": Deserializer.deserialize_base64, + "object": self.deserialize_object, + "[]": self.deserialize_iter, + "{}": self.deserialize_dict, + } + self.deserialize_expected_types = { + "duration": (isodate.Duration, datetime.timedelta), + "iso-8601": (datetime.datetime), + } + self.dependencies: Dict[str, type] = dict(classes) if classes else {} + self.key_extractors = [rest_key_extractor, xml_key_extractor] + # Additional properties only works if the "rest_key_extractor" is used to + # extract the keys. Making it to work whatever the key extractor is too much + # complicated, with no real scenario for now. + # So adding a flag to disable additional properties detection. This flag should be + # used if your expect the deserialization to NOT come from a JSON REST syntax. + # Otherwise, result are unexpected + self.additional_properties_detection = True + + def __call__(self, target_obj, response_data, content_type=None): + """Call the deserializer to process a REST response. + + :param str target_obj: Target data type to deserialize to. + :param requests.Response response_data: REST response object. + :param str content_type: Swagger "produces" if available. + :raises: DeserializationError if deserialization fails. + :return: Deserialized object. + :rtype: object + """ + data = self._unpack_content(response_data, content_type) + return self._deserialize(target_obj, data) + + def _deserialize(self, target_obj, data): # pylint: disable=inconsistent-return-statements + """Call the deserializer on a model. + + Data needs to be already deserialized as JSON or XML ElementTree + + :param str target_obj: Target data type to deserialize to. + :param object data: Object to deserialize. + :raises: DeserializationError if deserialization fails. + :return: Deserialized object. + :rtype: object + """ + # This is already a model, go recursive just in case + if hasattr(data, "_attribute_map"): + constants = [name for name, config in getattr(data, "_validation", {}).items() if config.get("constant")] + try: + for attr, mapconfig in data._attribute_map.items(): # pylint: disable=protected-access + if attr in constants: + continue + value = getattr(data, attr) + if value is None: + continue + local_type = mapconfig["type"] + internal_data_type = local_type.strip("[]{}") + if internal_data_type not in self.dependencies or isinstance(internal_data_type, Enum): + continue + setattr(data, attr, self._deserialize(local_type, value)) + return data + except AttributeError: + return + + response, class_name = self._classify_target(target_obj, data) + + if isinstance(response, str): + return self.deserialize_data(data, response) + if isinstance(response, type) and issubclass(response, Enum): + return self.deserialize_enum(data, response) + + if data is None or data is CoreNull: + return data + try: + attributes = response._attribute_map # type: ignore # pylint: disable=protected-access + d_attrs = {} + for attr, attr_desc in attributes.items(): + # Check empty string. If it's not empty, someone has a real "additionalProperties"... + if attr == "additional_properties" and attr_desc["key"] == "": + continue + raw_value = None + # Enhance attr_desc with some dynamic data + attr_desc = attr_desc.copy() # Do a copy, do not change the real one + internal_data_type = attr_desc["type"].strip("[]{}") + if internal_data_type in self.dependencies: + attr_desc["internalType"] = self.dependencies[internal_data_type] + + for key_extractor in self.key_extractors: + found_value = key_extractor(attr, attr_desc, data) + if found_value is not None: + if raw_value is not None and raw_value != found_value: + msg = ( + "Ignoring extracted value '%s' from %s for key '%s'" + " (duplicate extraction, follow extractors order)" + ) + _LOGGER.warning(msg, found_value, key_extractor, attr) + continue + raw_value = found_value + + value = self.deserialize_data(raw_value, attr_desc["type"]) + d_attrs[attr] = value + except (AttributeError, TypeError, KeyError) as err: + msg = "Unable to deserialize to object: " + class_name # type: ignore + raise DeserializationError(msg) from err + additional_properties = self._build_additional_properties(attributes, data) + return self._instantiate_model(response, d_attrs, additional_properties) + + def _build_additional_properties(self, attribute_map, data): + if not self.additional_properties_detection: + return None + if "additional_properties" in attribute_map and attribute_map.get("additional_properties", {}).get("key") != "": + # Check empty string. If it's not empty, someone has a real "additionalProperties" + return None + if isinstance(data, ET.Element): + data = {el.tag: el.text for el in data} + + known_keys = { + _decode_attribute_map_key(_FLATTEN.split(desc["key"])[0]) + for desc in attribute_map.values() + if desc["key"] != "" + } + present_keys = set(data.keys()) + missing_keys = present_keys - known_keys + return {key: data[key] for key in missing_keys} + + def _classify_target(self, target, data): + """Check to see whether the deserialization target object can + be classified into a subclass. + Once classification has been determined, initialize object. + + :param str target: The target object type to deserialize to. + :param str/dict data: The response data to deserialize. + :return: The classified target object and its class name. + :rtype: tuple + """ + if target is None: + return None, None + + if isinstance(target, str): + try: + target = self.dependencies[target] + except KeyError: + return target, target + + try: + target = target._classify(data, self.dependencies) # type: ignore # pylint: disable=protected-access + except AttributeError: + pass # Target is not a Model, no classify + return target, target.__class__.__name__ # type: ignore + + def failsafe_deserialize(self, target_obj, data, content_type=None): + """Ignores any errors encountered in deserialization, + and falls back to not deserializing the object. Recommended + for use in error deserialization, as we want to return the + HttpResponseError to users, and not have them deal with + a deserialization error. + + :param str target_obj: The target object type to deserialize to. + :param str/dict data: The response data to deserialize. + :param str content_type: Swagger "produces" if available. + :return: Deserialized object. + :rtype: object + """ + try: + return self(target_obj, data, content_type=content_type) + except: # pylint: disable=bare-except + _LOGGER.debug( + "Ran into a deserialization error. Ignoring since this is failsafe deserialization", exc_info=True + ) + return None + + @staticmethod + def _unpack_content(raw_data, content_type=None): + """Extract the correct structure for deserialization. + + If raw_data is a PipelineResponse, try to extract the result of RawDeserializer. + if we can't, raise. Your Pipeline should have a RawDeserializer. + + If not a pipeline response and raw_data is bytes or string, use content-type + to decode it. If no content-type, try JSON. + + If raw_data is something else, bypass all logic and return it directly. + + :param obj raw_data: Data to be processed. + :param str content_type: How to parse if raw_data is a string/bytes. + :raises JSONDecodeError: If JSON is requested and parsing is impossible. + :raises UnicodeDecodeError: If bytes is not UTF8 + :rtype: object + :return: Unpacked content. + """ + # Assume this is enough to detect a Pipeline Response without importing it + context = getattr(raw_data, "context", {}) + if context: + if RawDeserializer.CONTEXT_NAME in context: + return context[RawDeserializer.CONTEXT_NAME] + raise ValueError("This pipeline didn't have the RawDeserializer policy; can't deserialize") + + # Assume this is enough to recognize universal_http.ClientResponse without importing it + if hasattr(raw_data, "body"): + return RawDeserializer.deserialize_from_http_generics(raw_data.text(), raw_data.headers) + + # Assume this enough to recognize requests.Response without importing it. + if hasattr(raw_data, "_content_consumed"): + return RawDeserializer.deserialize_from_http_generics(raw_data.text, raw_data.headers) + + if isinstance(raw_data, (str, bytes)) or hasattr(raw_data, "read"): + return RawDeserializer.deserialize_from_text(raw_data, content_type) # type: ignore + return raw_data + + def _instantiate_model(self, response, attrs, additional_properties=None): + """Instantiate a response model passing in deserialized args. + + :param Response response: The response model class. + :param dict attrs: The deserialized response attributes. + :param dict additional_properties: Additional properties to be set. + :rtype: Response + :return: The instantiated response model. + """ + if callable(response): + subtype = getattr(response, "_subtype_map", {}) + try: + readonly = [ + k + for k, v in response._validation.items() # pylint: disable=protected-access # type: ignore + if v.get("readonly") + ] + const = [ + k + for k, v in response._validation.items() # pylint: disable=protected-access # type: ignore + if v.get("constant") + ] + kwargs = {k: v for k, v in attrs.items() if k not in subtype and k not in readonly + const} + response_obj = response(**kwargs) + for attr in readonly: + setattr(response_obj, attr, attrs.get(attr)) + if additional_properties: + response_obj.additional_properties = additional_properties # type: ignore + return response_obj + except TypeError as err: + msg = "Unable to deserialize {} into model {}. ".format(kwargs, response) # type: ignore + raise DeserializationError(msg + str(err)) from err + else: + try: + for attr, value in attrs.items(): + setattr(response, attr, value) + return response + except Exception as exp: + msg = "Unable to populate response model. " + msg += "Type: {}, Error: {}".format(type(response), exp) + raise DeserializationError(msg) from exp + + def deserialize_data(self, data, data_type): # pylint: disable=too-many-return-statements + """Process data for deserialization according to data type. + + :param str data: The response string to be deserialized. + :param str data_type: The type to deserialize to. + :raises: DeserializationError if deserialization fails. + :return: Deserialized object. + :rtype: object + """ + if data is None: + return data + + try: + if not data_type: + return data + if data_type in self.basic_types.values(): + return self.deserialize_basic(data, data_type) + if data_type in self.deserialize_type: + if isinstance(data, self.deserialize_expected_types.get(data_type, tuple())): + return data + + is_a_text_parsing_type = lambda x: x not in [ # pylint: disable=unnecessary-lambda-assignment + "object", + "[]", + r"{}", + ] + if isinstance(data, ET.Element) and is_a_text_parsing_type(data_type) and not data.text: + return None + data_val = self.deserialize_type[data_type](data) + return data_val + + iter_type = data_type[0] + data_type[-1] + if iter_type in self.deserialize_type: + return self.deserialize_type[iter_type](data, data_type[1:-1]) + + obj_type = self.dependencies[data_type] + if issubclass(obj_type, Enum): + if isinstance(data, ET.Element): + data = data.text + return self.deserialize_enum(data, obj_type) + + except (ValueError, TypeError, AttributeError) as err: + msg = "Unable to deserialize response data." + msg += " Data: {}, {}".format(data, data_type) + raise DeserializationError(msg) from err + return self._deserialize(obj_type, data) + + def deserialize_iter(self, attr, iter_type): + """Deserialize an iterable. + + :param list attr: Iterable to be deserialized. + :param str iter_type: The type of object in the iterable. + :return: Deserialized iterable. + :rtype: list + """ + if attr is None: + return None + if isinstance(attr, ET.Element): # If I receive an element here, get the children + attr = list(attr) + if not isinstance(attr, (list, set)): + raise DeserializationError("Cannot deserialize as [{}] an object of type {}".format(iter_type, type(attr))) + return [self.deserialize_data(a, iter_type) for a in attr] + + def deserialize_dict(self, attr, dict_type): + """Deserialize a dictionary. + + :param dict/list attr: Dictionary to be deserialized. Also accepts + a list of key, value pairs. + :param str dict_type: The object type of the items in the dictionary. + :return: Deserialized dictionary. + :rtype: dict + """ + if isinstance(attr, list): + return {x["key"]: self.deserialize_data(x["value"], dict_type) for x in attr} + + if isinstance(attr, ET.Element): + # Transform value into {"Key": "value"} + attr = {el.tag: el.text for el in attr} + return {k: self.deserialize_data(v, dict_type) for k, v in attr.items()} + + def deserialize_object(self, attr, **kwargs): # pylint: disable=too-many-return-statements + """Deserialize a generic object. + This will be handled as a dictionary. + + :param dict attr: Dictionary to be deserialized. + :return: Deserialized object. + :rtype: dict + :raises: TypeError if non-builtin datatype encountered. + """ + if attr is None: + return None + if isinstance(attr, ET.Element): + # Do no recurse on XML, just return the tree as-is + return attr + if isinstance(attr, str): + return self.deserialize_basic(attr, "str") + obj_type = type(attr) + if obj_type in self.basic_types: + return self.deserialize_basic(attr, self.basic_types[obj_type]) + if obj_type is _long_type: + return self.deserialize_long(attr) + + if obj_type == dict: + deserialized = {} + for key, value in attr.items(): + try: + deserialized[key] = self.deserialize_object(value, **kwargs) + except ValueError: + deserialized[key] = None + return deserialized + + if obj_type == list: + deserialized = [] + for obj in attr: + try: + deserialized.append(self.deserialize_object(obj, **kwargs)) + except ValueError: + pass + return deserialized + + error = "Cannot deserialize generic object with type: " + raise TypeError(error + str(obj_type)) + + def deserialize_basic(self, attr, data_type): # pylint: disable=too-many-return-statements + """Deserialize basic builtin data type from string. + Will attempt to convert to str, int, float and bool. + This function will also accept '1', '0', 'true' and 'false' as + valid bool values. + + :param str attr: response string to be deserialized. + :param str data_type: deserialization data type. + :return: Deserialized basic type. + :rtype: str, int, float or bool + :raises: TypeError if string format is not valid. + """ + # If we're here, data is supposed to be a basic type. + # If it's still an XML node, take the text + if isinstance(attr, ET.Element): + attr = attr.text + if not attr: + if data_type == "str": + # None or '', node is empty string. + return "" + # None or '', node with a strong type is None. + # Don't try to model "empty bool" or "empty int" + return None + + if data_type == "bool": + if attr in [True, False, 1, 0]: + return bool(attr) + if isinstance(attr, str): + if attr.lower() in ["true", "1"]: + return True + if attr.lower() in ["false", "0"]: + return False + raise TypeError("Invalid boolean value: {}".format(attr)) + + if data_type == "str": + return self.deserialize_unicode(attr) + return eval(data_type)(attr) # nosec # pylint: disable=eval-used + + @staticmethod + def deserialize_unicode(data): + """Preserve unicode objects in Python 2, otherwise return data + as a string. + + :param str data: response string to be deserialized. + :return: Deserialized string. + :rtype: str or unicode + """ + # We might be here because we have an enum modeled as string, + # and we try to deserialize a partial dict with enum inside + if isinstance(data, Enum): + return data + + # Consider this is real string + try: + if isinstance(data, unicode): # type: ignore + return data + except NameError: + return str(data) + return str(data) + + @staticmethod + def deserialize_enum(data, enum_obj): + """Deserialize string into enum object. + + If the string is not a valid enum value it will be returned as-is + and a warning will be logged. + + :param str data: Response string to be deserialized. If this value is + None or invalid it will be returned as-is. + :param Enum enum_obj: Enum object to deserialize to. + :return: Deserialized enum object. + :rtype: Enum + """ + if isinstance(data, enum_obj) or data is None: + return data + if isinstance(data, Enum): + data = data.value + if isinstance(data, int): + # Workaround. We might consider remove it in the future. + try: + return list(enum_obj.__members__.values())[data] + except IndexError as exc: + error = "{!r} is not a valid index for enum {!r}" + raise DeserializationError(error.format(data, enum_obj)) from exc + try: + return enum_obj(str(data)) + except ValueError: + for enum_value in enum_obj: + if enum_value.value.lower() == str(data).lower(): + return enum_value + # We don't fail anymore for unknown value, we deserialize as a string + _LOGGER.warning("Deserializer is not able to find %s as valid enum in %s", data, enum_obj) + return Deserializer.deserialize_unicode(data) + + @staticmethod + def deserialize_bytearray(attr): + """Deserialize string into bytearray. + + :param str attr: response string to be deserialized. + :return: Deserialized bytearray + :rtype: bytearray + :raises: TypeError if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + return bytearray(b64decode(attr)) # type: ignore + + @staticmethod + def deserialize_base64(attr): + """Deserialize base64 encoded string into string. + + :param str attr: response string to be deserialized. + :return: Deserialized base64 string + :rtype: bytearray + :raises: TypeError if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + padding = "=" * (3 - (len(attr) + 3) % 4) # type: ignore + attr = attr + padding # type: ignore + encoded = attr.replace("-", "+").replace("_", "/") + return b64decode(encoded) + + @staticmethod + def deserialize_decimal(attr): + """Deserialize string into Decimal object. + + :param str attr: response string to be deserialized. + :return: Deserialized decimal + :raises: DeserializationError if string format invalid. + :rtype: decimal + """ + if isinstance(attr, ET.Element): + attr = attr.text + try: + return decimal.Decimal(str(attr)) # type: ignore + except decimal.DecimalException as err: + msg = "Invalid decimal {}".format(attr) + raise DeserializationError(msg) from err + + @staticmethod + def deserialize_long(attr): + """Deserialize string into long (Py2) or int (Py3). + + :param str attr: response string to be deserialized. + :return: Deserialized int + :rtype: long or int + :raises: ValueError if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + return _long_type(attr) # type: ignore + + @staticmethod + def deserialize_duration(attr): + """Deserialize ISO-8601 formatted string into TimeDelta object. + + :param str attr: response string to be deserialized. + :return: Deserialized duration + :rtype: TimeDelta + :raises: DeserializationError if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + try: + duration = isodate.parse_duration(attr) + except (ValueError, OverflowError, AttributeError) as err: + msg = "Cannot deserialize duration object." + raise DeserializationError(msg) from err + return duration + + @staticmethod + def deserialize_date(attr): + """Deserialize ISO-8601 formatted string into Date object. + + :param str attr: response string to be deserialized. + :return: Deserialized date + :rtype: Date + :raises: DeserializationError if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + if re.search(r"[^\W\d_]", attr, re.I + re.U): # type: ignore + raise DeserializationError("Date must have only digits and -. Received: %s" % attr) + # This must NOT use defaultmonth/defaultday. Using None ensure this raises an exception. + return isodate.parse_date(attr, defaultmonth=0, defaultday=0) + + @staticmethod + def deserialize_time(attr): + """Deserialize ISO-8601 formatted string into time object. + + :param str attr: response string to be deserialized. + :return: Deserialized time + :rtype: datetime.time + :raises: DeserializationError if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + if re.search(r"[^\W\d_]", attr, re.I + re.U): # type: ignore + raise DeserializationError("Date must have only digits and -. Received: %s" % attr) + return isodate.parse_time(attr) + + @staticmethod + def deserialize_rfc(attr): + """Deserialize RFC-1123 formatted string into Datetime object. + + :param str attr: response string to be deserialized. + :return: Deserialized RFC datetime + :rtype: Datetime + :raises: DeserializationError if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + try: + parsed_date = email.utils.parsedate_tz(attr) # type: ignore + date_obj = datetime.datetime( + *parsed_date[:6], tzinfo=_FixedOffset(datetime.timedelta(minutes=(parsed_date[9] or 0) / 60)) + ) + if not date_obj.tzinfo: + date_obj = date_obj.astimezone(tz=TZ_UTC) + except ValueError as err: + msg = "Cannot deserialize to rfc datetime object." + raise DeserializationError(msg) from err + return date_obj + + @staticmethod + def deserialize_iso(attr): + """Deserialize ISO-8601 formatted string into Datetime object. + + :param str attr: response string to be deserialized. + :return: Deserialized ISO datetime + :rtype: Datetime + :raises: DeserializationError if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + try: + attr = attr.upper() # type: ignore + match = Deserializer.valid_date.match(attr) + if not match: + raise ValueError("Invalid datetime string: " + attr) + + check_decimal = attr.split(".") + if len(check_decimal) > 1: + decimal_str = "" + for digit in check_decimal[1]: + if digit.isdigit(): + decimal_str += digit + else: + break + if len(decimal_str) > 6: + attr = attr.replace(decimal_str, decimal_str[0:6]) + + date_obj = isodate.parse_datetime(attr) + test_utc = date_obj.utctimetuple() + if test_utc.tm_year > 9999 or test_utc.tm_year < 1: + raise OverflowError("Hit max or min date") + except (ValueError, OverflowError, AttributeError) as err: + msg = "Cannot deserialize datetime object." + raise DeserializationError(msg) from err + return date_obj + + @staticmethod + def deserialize_unix(attr): + """Serialize Datetime object into IntTime format. + This is represented as seconds. + + :param int attr: Object to be serialized. + :return: Deserialized datetime + :rtype: Datetime + :raises: DeserializationError if format invalid + """ + if isinstance(attr, ET.Element): + attr = int(attr.text) # type: ignore + try: + attr = int(attr) + date_obj = datetime.datetime.fromtimestamp(attr, TZ_UTC) + except ValueError as err: + msg = "Cannot deserialize to unix datetime object." + raise DeserializationError(msg) from err + return date_obj diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_types.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_types.py new file mode 100644 index 000000000000..68a432429bf1 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_types.py @@ -0,0 +1,20 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from typing import List, TYPE_CHECKING, Union + +if TYPE_CHECKING: + from . import models as _models +AssistantsApiResponseFormatOption = Union[ + str, str, "_models.AssistantsApiResponseFormatMode", "_models.AssistantsApiResponseFormat" +] +CreateFileSearchToolResourceOptions = Union[List[str], List["_models.CreateFileSearchToolResourceVectorStoreOptions"]] +MessageAttachmentToolDefinition = Union["_models.CodeInterpreterToolDefinition", "_models.FileSearchToolDefinition"] +AssistantsApiToolChoiceOption = Union[ + str, str, "_models.AssistantsApiToolChoiceOptionMode", "_models.AssistantsNamedToolChoice" +] diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_vendor.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_vendor.py new file mode 100644 index 000000000000..f95c3445d0cf --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_vendor.py @@ -0,0 +1,66 @@ +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from abc import ABC +import json +from typing import Any, Dict, IO, List, Mapping, Optional, TYPE_CHECKING, Tuple, Union + +from ._configuration import MachineLearningServicesClientConfiguration +from ._model_base import Model, SdkJSONEncoder + +if TYPE_CHECKING: + from azure.core import PipelineClient + + from ._serialization import Deserializer, Serializer + + +class MachineLearningServicesClientMixinABC(ABC): + """DO NOT use this class. It is for internal typing use only.""" + + _client: "PipelineClient" + _config: MachineLearningServicesClientConfiguration + _serialize: "Serializer" + _deserialize: "Deserializer" + + +# file-like tuple could be `(filename, IO (or bytes))` or `(filename, IO (or bytes), content_type)` +FileContent = Union[str, bytes, IO[str], IO[bytes]] + +FileType = Union[ + # file (or bytes) + FileContent, + # (filename, file (or bytes)) + Tuple[Optional[str], FileContent], + # (filename, file (or bytes), content_type) + Tuple[Optional[str], FileContent, Optional[str]], +] + + +def serialize_multipart_data_entry(data_entry: Any) -> Any: + if isinstance(data_entry, (list, tuple, dict, Model)): + return json.dumps(data_entry, cls=SdkJSONEncoder, exclude_readonly=True) + return data_entry + + +def prepare_multipart_form_data( + body: Mapping[str, Any], multipart_fields: List[str], data_fields: List[str] +) -> Tuple[List[FileType], Dict[str, Any]]: + files: List[FileType] = [] + data: Dict[str, Any] = {} + for multipart_field in multipart_fields: + multipart_entry = body.get(multipart_field) + if isinstance(multipart_entry, list): + files.extend([(multipart_field, e) for e in multipart_entry]) + elif multipart_entry: + files.append((multipart_field, multipart_entry)) + + for data_field in data_fields: + data_entry = body.get(data_field) + if data_entry: + data[data_field] = serialize_multipart_data_entry(data_entry) + + return files, data diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_version.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_version.py new file mode 100644 index 000000000000..be71c81bd282 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/_version.py @@ -0,0 +1,9 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +VERSION = "1.0.0b1" diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/__init__.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/__init__.py new file mode 100644 index 000000000000..2ea7a4548ba9 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/__init__.py @@ -0,0 +1,29 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +# pylint: disable=wrong-import-position + +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from ._patch import * # pylint: disable=unused-wildcard-import + +from ._client import MachineLearningServicesClient # type: ignore + +try: + from ._patch import __all__ as _patch_all + from ._patch import * +except ImportError: + _patch_all = [] +from ._patch import patch_sdk as _patch_sdk + +__all__ = [ + "MachineLearningServicesClient", +] +__all__.extend([p for p in _patch_all if p not in __all__]) # pyright: ignore + +_patch_sdk() diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/_client.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/_client.py new file mode 100644 index 000000000000..c36d78cacf4e --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/_client.py @@ -0,0 +1,142 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from copy import deepcopy +from typing import Any, Awaitable, TYPE_CHECKING +from typing_extensions import Self + +from azure.core import AsyncPipelineClient +from azure.core.pipeline import policies +from azure.core.rest import AsyncHttpResponse, HttpRequest + +from .._serialization import Deserializer, Serializer +from ._configuration import MachineLearningServicesClientConfiguration +from .operations import ( + ConnectionsOperations, + DataOperations, + DataVersionsBaseOperations, + EvaluationsOperations, + IndexesOperations, + MachineLearningServicesClientOperationsMixin, + ModelContainersOperations, + ModelVersionsOperations, +) + +if TYPE_CHECKING: + from azure.core.credentials_async import AsyncTokenCredential + + +class MachineLearningServicesClient( + MachineLearningServicesClientOperationsMixin +): # pylint: disable=too-many-instance-attributes + """MachineLearningServicesClient. + + :ivar connections: ConnectionsOperations operations + :vartype connections: azure.ai.resources.autogen.aio.operations.ConnectionsOperations + :ivar data: DataOperations operations + :vartype data: azure.ai.resources.autogen.aio.operations.DataOperations + :ivar data_versions_base: DataVersionsBaseOperations operations + :vartype data_versions_base: + azure.ai.resources.autogen.aio.operations.DataVersionsBaseOperations + :ivar evaluations: EvaluationsOperations operations + :vartype evaluations: azure.ai.resources.autogen.aio.operations.EvaluationsOperations + :ivar indexes: IndexesOperations operations + :vartype indexes: azure.ai.resources.autogen.aio.operations.IndexesOperations + :ivar model_containers: ModelContainersOperations operations + :vartype model_containers: azure.ai.resources.autogen.aio.operations.ModelContainersOperations + :ivar model_versions: ModelVersionsOperations operations + :vartype model_versions: azure.ai.resources.autogen.aio.operations.ModelVersionsOperations + :param endpoint: Global endpoint in the form of: https://[hub-id]/api.ai.azure.com. Required. + :type endpoint: str + :param project_name: The name of the AI project. Required. + :type project_name: str + :param credential: Credential used to authenticate requests to the service. Required. + :type credential: ~azure.core.credentials_async.AsyncTokenCredential + :keyword api_version: The API version to use for this operation. Default value is + "2024-11-01-preview". Note that overriding this default value may result in unsupported + behavior. + :paramtype api_version: str + """ + + def __init__(self, endpoint: str, project_name: str, credential: "AsyncTokenCredential", **kwargs: Any) -> None: + _endpoint = "{endpoint}/projects/{projectName}" + self._config = MachineLearningServicesClientConfiguration( + endpoint=endpoint, project_name=project_name, credential=credential, **kwargs + ) + _policies = kwargs.pop("policies", None) + if _policies is None: + _policies = [ + policies.RequestIdPolicy(**kwargs), + self._config.headers_policy, + self._config.user_agent_policy, + self._config.proxy_policy, + policies.ContentDecodePolicy(**kwargs), + self._config.redirect_policy, + self._config.retry_policy, + self._config.authentication_policy, + self._config.custom_hook_policy, + self._config.logging_policy, + policies.DistributedTracingPolicy(**kwargs), + policies.SensitiveHeaderCleanupPolicy(**kwargs) if self._config.redirect_policy else None, + self._config.http_logging_policy, + ] + self._client: AsyncPipelineClient = AsyncPipelineClient(base_url=_endpoint, policies=_policies, **kwargs) + + self._serialize = Serializer() + self._deserialize = Deserializer() + self._serialize.client_side_validation = False + self.connections = ConnectionsOperations(self._client, self._config, self._serialize, self._deserialize) + self.data = DataOperations(self._client, self._config, self._serialize, self._deserialize) + self.data_versions_base = DataVersionsBaseOperations( + self._client, self._config, self._serialize, self._deserialize + ) + self.evaluations = EvaluationsOperations(self._client, self._config, self._serialize, self._deserialize) + self.indexes = IndexesOperations(self._client, self._config, self._serialize, self._deserialize) + self.model_containers = ModelContainersOperations( + self._client, self._config, self._serialize, self._deserialize + ) + self.model_versions = ModelVersionsOperations(self._client, self._config, self._serialize, self._deserialize) + + def send_request( + self, request: HttpRequest, *, stream: bool = False, **kwargs: Any + ) -> Awaitable[AsyncHttpResponse]: + """Runs the network request through the client's chained policies. + + >>> from azure.core.rest import HttpRequest + >>> request = HttpRequest("GET", "https://www.example.org/") + + >>> response = await client.send_request(request) + + + For more information on this code flow, see https://aka.ms/azsdk/dpcodegen/python/send_request + + :param request: The network request you want to make. Required. + :type request: ~azure.core.rest.HttpRequest + :keyword bool stream: Whether the response payload will be streamed. Defaults to False. + :return: The response of your network call. Does not do error handling on your response. + :rtype: ~azure.core.rest.AsyncHttpResponse + """ + + request_copy = deepcopy(request) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + + request_copy.url = self._client.format_url(request_copy.url, **path_format_arguments) + return self._client.send_request(request_copy, stream=stream, **kwargs) # type: ignore + + async def close(self) -> None: + await self._client.close() + + async def __aenter__(self) -> Self: + await self._client.__aenter__() + return self + + async def __aexit__(self, *exc_details: Any) -> None: + await self._client.__aexit__(*exc_details) diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/_configuration.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/_configuration.py new file mode 100644 index 000000000000..0830ad42fa89 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/_configuration.py @@ -0,0 +1,69 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from typing import Any, TYPE_CHECKING + +from azure.core.pipeline import policies + +from .._version import VERSION + +if TYPE_CHECKING: + from azure.core.credentials_async import AsyncTokenCredential + + +class MachineLearningServicesClientConfiguration: # pylint: disable=too-many-instance-attributes,name-too-long + """Configuration for MachineLearningServicesClient. + + Note that all parameters used to create this instance are saved as instance + attributes. + + :param endpoint: Global endpoint in the form of: https://[hub-id]/api.ai.azure.com. Required. + :type endpoint: str + :param project_name: The name of the AI project. Required. + :type project_name: str + :param credential: Credential used to authenticate requests to the service. Required. + :type credential: ~azure.core.credentials_async.AsyncTokenCredential + :keyword api_version: The API version to use for this operation. Default value is + "2024-11-01-preview". Note that overriding this default value may result in unsupported + behavior. + :paramtype api_version: str + """ + + def __init__(self, endpoint: str, project_name: str, credential: "AsyncTokenCredential", **kwargs: Any) -> None: + api_version: str = kwargs.pop("api_version", "2024-11-01-preview") + + if endpoint is None: + raise ValueError("Parameter 'endpoint' must not be None.") + if project_name is None: + raise ValueError("Parameter 'project_name' must not be None.") + if credential is None: + raise ValueError("Parameter 'credential' must not be None.") + + self.endpoint = endpoint + self.project_name = project_name + self.credential = credential + self.api_version = api_version + self.credential_scopes = kwargs.pop("credential_scopes", ["https://ai.azure.com/.default"]) + kwargs.setdefault("sdk_moniker", "ai-resources-autogen/{}".format(VERSION)) + self.polling_interval = kwargs.get("polling_interval", 30) + self._configure(**kwargs) + + def _configure(self, **kwargs: Any) -> None: + self.user_agent_policy = kwargs.get("user_agent_policy") or policies.UserAgentPolicy(**kwargs) + self.headers_policy = kwargs.get("headers_policy") or policies.HeadersPolicy(**kwargs) + self.proxy_policy = kwargs.get("proxy_policy") or policies.ProxyPolicy(**kwargs) + self.logging_policy = kwargs.get("logging_policy") or policies.NetworkTraceLoggingPolicy(**kwargs) + self.http_logging_policy = kwargs.get("http_logging_policy") or policies.HttpLoggingPolicy(**kwargs) + self.custom_hook_policy = kwargs.get("custom_hook_policy") or policies.CustomHookPolicy(**kwargs) + self.redirect_policy = kwargs.get("redirect_policy") or policies.AsyncRedirectPolicy(**kwargs) + self.retry_policy = kwargs.get("retry_policy") or policies.AsyncRetryPolicy(**kwargs) + self.authentication_policy = kwargs.get("authentication_policy") + if self.credential and not self.authentication_policy: + self.authentication_policy = policies.AsyncBearerTokenCredentialPolicy( + self.credential, *self.credential_scopes, **kwargs + ) diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/_patch.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/_patch.py new file mode 100644 index 000000000000..f7dd32510333 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/_patch.py @@ -0,0 +1,20 @@ +# ------------------------------------ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. +# ------------------------------------ +"""Customize generated code here. + +Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize +""" +from typing import List + +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level + + +def patch_sdk(): + """Do not remove from this file. + + `patch_sdk` is a last resort escape hatch that allows you to do customizations + you can't accomplish using the techniques described in + https://aka.ms/azsdk/python/dpcodegen/python/customize + """ diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/_vendor.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/_vendor.py new file mode 100644 index 000000000000..0517fd835d12 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/_vendor.py @@ -0,0 +1,25 @@ +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from abc import ABC +from typing import TYPE_CHECKING + +from ._configuration import MachineLearningServicesClientConfiguration + +if TYPE_CHECKING: + from azure.core import AsyncPipelineClient + + from .._serialization import Deserializer, Serializer + + +class MachineLearningServicesClientMixinABC(ABC): + """DO NOT use this class. It is for internal typing use only.""" + + _client: "AsyncPipelineClient" + _config: MachineLearningServicesClientConfiguration + _serialize: "Serializer" + _deserialize: "Deserializer" diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/operations/__init__.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/operations/__init__.py new file mode 100644 index 000000000000..6fda01b5f7fa --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/operations/__init__.py @@ -0,0 +1,39 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +# pylint: disable=wrong-import-position + +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from ._patch import * # pylint: disable=unused-wildcard-import + +from ._operations import ConnectionsOperations # type: ignore +from ._operations import DataOperations # type: ignore +from ._operations import DataVersionsBaseOperations # type: ignore +from ._operations import EvaluationsOperations # type: ignore +from ._operations import IndexesOperations # type: ignore +from ._operations import ModelContainersOperations # type: ignore +from ._operations import ModelVersionsOperations # type: ignore +from ._operations import MachineLearningServicesClientOperationsMixin # type: ignore + +from ._patch import __all__ as _patch_all +from ._patch import * +from ._patch import patch_sdk as _patch_sdk + +__all__ = [ + "ConnectionsOperations", + "DataOperations", + "DataVersionsBaseOperations", + "EvaluationsOperations", + "IndexesOperations", + "ModelContainersOperations", + "ModelVersionsOperations", + "MachineLearningServicesClientOperationsMixin", +] +__all__.extend([p for p in _patch_all if p not in __all__]) # pyright: ignore +_patch_sdk() diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/operations/_operations.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/operations/_operations.py new file mode 100644 index 000000000000..3441c0cbc1ca --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/operations/_operations.py @@ -0,0 +1,9034 @@ +# pylint: disable=too-many-lines +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +from io import IOBase +import json +import sys +from typing import Any, AsyncIterable, Callable, Dict, IO, List, Optional, TypeVar, Union, overload +import urllib.parse + +from azure.core.async_paging import AsyncItemPaged, AsyncList +from azure.core.exceptions import ( + ClientAuthenticationError, + HttpResponseError, + ResourceExistsError, + ResourceNotFoundError, + ResourceNotModifiedError, + StreamClosedError, + StreamConsumedError, + map_error, +) +from azure.core.pipeline import PipelineResponse +from azure.core.rest import AsyncHttpResponse, HttpRequest +from azure.core.tracing.decorator import distributed_trace +from azure.core.tracing.decorator_async import distributed_trace_async +from azure.core.utils import case_insensitive_dict + +from ... import _model_base, models as _models +from ..._model_base import SdkJSONEncoder, _deserialize +from ..._vendor import FileType, prepare_multipart_form_data +from ...operations._operations import ( + build_connections_get_request, + build_connections_list_request, + build_connections_list_with_credentials_request, + build_connections_post_request, + build_data_create_or_update_request, + build_data_delete_request, + build_data_get_request, + build_data_list_request, + build_data_versions_base_create_or_update_request, + build_data_versions_base_delete_request, + build_data_versions_base_get_request, + build_data_versions_base_list_request, + build_data_versions_base_publish_request, + build_evaluations_create_request, + build_evaluations_get_request, + build_evaluations_list_request, + build_evaluations_update_request, + build_indexes_create_or_update_request, + build_indexes_get_latest_request, + build_indexes_get_next_version_request, + build_indexes_get_request, + build_indexes_list_latest_request, + build_indexes_list_request, + build_machine_learning_services_cancel_run_request, + build_machine_learning_services_cancel_vector_store_file_batch_request, + build_machine_learning_services_create_assistant_request, + build_machine_learning_services_create_message_request, + build_machine_learning_services_create_or_update_batch_deployment_request, + build_machine_learning_services_create_or_update_batch_endpoint_request, + build_machine_learning_services_create_or_update_online_deployment_request, + build_machine_learning_services_create_or_update_online_endpoint_request, + build_machine_learning_services_create_run_request, + build_machine_learning_services_create_thread_and_run_request, + build_machine_learning_services_create_thread_request, + build_machine_learning_services_create_vector_store_file_batch_request, + build_machine_learning_services_create_vector_store_file_request, + build_machine_learning_services_create_vector_store_request, + build_machine_learning_services_delete_assistant_request, + build_machine_learning_services_delete_batch_deployment_request, + build_machine_learning_services_delete_batch_endpoint_request, + build_machine_learning_services_delete_file_request, + build_machine_learning_services_delete_online_deployment_request, + build_machine_learning_services_delete_online_endpoint_request, + build_machine_learning_services_delete_thread_request, + build_machine_learning_services_delete_vector_store_file_request, + build_machine_learning_services_delete_vector_store_request, + build_machine_learning_services_get_assistant_request, + build_machine_learning_services_get_batch_deployment_request, + build_machine_learning_services_get_batch_endpoint_request, + build_machine_learning_services_get_file_content_request, + build_machine_learning_services_get_file_request, + build_machine_learning_services_get_message_request, + build_machine_learning_services_get_online_deployment_request, + build_machine_learning_services_get_online_endpoint_request, + build_machine_learning_services_get_run_request, + build_machine_learning_services_get_run_step_request, + build_machine_learning_services_get_skus_online_deployment_request, + build_machine_learning_services_get_thread_request, + build_machine_learning_services_get_token_online_endpoint_request, + build_machine_learning_services_get_vector_store_file_batch_request, + build_machine_learning_services_get_vector_store_file_request, + build_machine_learning_services_get_vector_store_request, + build_machine_learning_services_list_assistants_request, + build_machine_learning_services_list_batch_deployments_request, + build_machine_learning_services_list_batch_endpoints_request, + build_machine_learning_services_list_files_request, + build_machine_learning_services_list_keys_online_endpoint_request, + build_machine_learning_services_list_messages_request, + build_machine_learning_services_list_online_deployments_request, + build_machine_learning_services_list_online_endpoints_request, + build_machine_learning_services_list_run_steps_request, + build_machine_learning_services_list_runs_request, + build_machine_learning_services_list_vector_store_file_batch_files_request, + build_machine_learning_services_list_vector_store_files_request, + build_machine_learning_services_list_vector_stores_request, + build_machine_learning_services_modify_vector_store_request, + build_machine_learning_services_poll_logs_online_deployment_request, + build_machine_learning_services_regenerate_keys_online_endpoint_request, + build_machine_learning_services_submit_tool_outputs_to_run_request, + build_machine_learning_services_update_assistant_request, + build_machine_learning_services_update_batch_deployment_request, + build_machine_learning_services_update_batch_endpoint_request, + build_machine_learning_services_update_message_request, + build_machine_learning_services_update_online_deployment_request, + build_machine_learning_services_update_online_endpoint_request, + build_machine_learning_services_update_run_request, + build_machine_learning_services_update_thread_request, + build_machine_learning_services_upload_file_request, + build_model_containers_create_or_update_request, + build_model_containers_get_request, + build_model_versions_list_request, +) +from .._vendor import MachineLearningServicesClientMixinABC + +if sys.version_info >= (3, 9): + from collections.abc import MutableMapping +else: + from typing import MutableMapping # type: ignore +T = TypeVar("T") +ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] +JSON = MutableMapping[str, Any] # pylint: disable=unsubscriptable-object +_Unset: Any = object() + + +class ConnectionsOperations: + """ + .. warning:: + **DO NOT** instantiate this class directly. + + Instead, you should access the following operations through + :class:`~azure.ai.resources.autogen.aio.MachineLearningServicesClient`'s + :attr:`connections` attribute. + """ + + def __init__(self, *args, **kwargs) -> None: + input_args = list(args) + self._client = input_args.pop(0) if input_args else kwargs.pop("client") + self._config = input_args.pop(0) if input_args else kwargs.pop("config") + self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer") + self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer") + + @distributed_trace_async + async def get(self, name: str, **kwargs: Any) -> _models.Connection: + """Get a connection by name. + + :param name: Name of the connection. Required. + :type name: str + :return: Connection. The Connection is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Connection + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.Connection] = kwargs.pop("cls", None) + + _request = build_connections_get_request( + name=name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Connection, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def post(self, name: str, **kwargs: Any) -> _models.Connection: + """Get a connection with credentials by name. + + :param name: Name of the connection. Required. + :type name: str + :return: Connection. The Connection is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Connection + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.Connection] = kwargs.pop("cls", None) + + _request = build_connections_post_request( + name=name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Connection, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list( + self, *, top: Optional[int] = None, skip: Optional[int] = None, **kwargs: Any + ) -> AsyncIterable["_models.Connection"]: + """List all connections in the project. + + :keyword top: The number of result items to return. Default value is None. + :paramtype top: int + :keyword skip: The number of result items to skip. Default value is None. + :paramtype skip: int + :return: An iterator like instance of Connection + :rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.ai.resources.autogen.models.Connection] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + maxpagesize = kwargs.pop("maxpagesize", None) + cls: ClsType[List[_models.Connection]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_connections_list_request( + top=top, + skip=skip, + maxpagesize=maxpagesize, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + async def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.Connection], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, AsyncList(list_of_elem) + + async def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return AsyncItemPaged(get_next, extract_data) + + @distributed_trace + def list_with_credentials( + self, *, top: Optional[int] = None, skip: Optional[int] = None, **kwargs: Any + ) -> AsyncIterable["_models.Connection"]: + """List all connections with credentials in the project. + + :keyword top: The number of result items to return. Default value is None. + :paramtype top: int + :keyword skip: The number of result items to skip. Default value is None. + :paramtype skip: int + :return: An iterator like instance of Connection + :rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.ai.resources.autogen.models.Connection] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + maxpagesize = kwargs.pop("maxpagesize", None) + cls: ClsType[List[_models.Connection]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_connections_list_with_credentials_request( + top=top, + skip=skip, + maxpagesize=maxpagesize, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + async def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.Connection], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, AsyncList(list_of_elem) + + async def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return AsyncItemPaged(get_next, extract_data) + + +class DataOperations: + """ + .. warning:: + **DO NOT** instantiate this class directly. + + Instead, you should access the following operations through + :class:`~azure.ai.resources.autogen.aio.MachineLearningServicesClient`'s + :attr:`data` attribute. + """ + + def __init__(self, *args, **kwargs) -> None: + input_args = list(args) + self._client = input_args.pop(0) if input_args else kwargs.pop("client") + self._config = input_args.pop(0) if input_args else kwargs.pop("config") + self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer") + self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer") + + @distributed_trace + def list( + self, + *, + _skip: Optional[str] = None, + list_view_type: Optional[Union[str, _models.ListViewType]] = None, + **kwargs: Any + ) -> AsyncIterable["_models.DataContainer"]: + """List data containers. + + :keyword _skip: Continuation token for pagination. Default value is None. + :paramtype _skip: str + :keyword list_view_type: View type for including/excluding (for example) archived entities. + Known values are: "ActiveOnly", "ArchivedOnly", and "All". Default value is None. + :paramtype list_view_type: str or ~azure.ai.resources.autogen.models.ListViewType + :return: An iterator like instance of DataContainer + :rtype: + ~azure.core.async_paging.AsyncItemPaged[~azure.ai.resources.autogen.models.DataContainer] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.DataContainer]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_data_list_request( + _skip=_skip, + list_view_type=list_view_type, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + async def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.DataContainer], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, AsyncList(list_of_elem) + + async def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return AsyncItemPaged(get_next, extract_data) + + @distributed_trace_async + async def delete(self, workspace_name: str, name: str, **kwargs: Any) -> None: + """Delete container. + + :param workspace_name: Name of Azure Machine Learning workspace. Required. + :type workspace_name: str + :param name: Container name. Required. + :type name: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_data_delete_request( + workspace_name=workspace_name, + name=name, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [204]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get(self, name: str, **kwargs: Any) -> _models.DataContainer: + """Get container. + + :param name: Container name. Required. + :type name: str + :return: DataContainer. The DataContainer is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DataContainer + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.DataContainer] = kwargs.pop("cls", None) + + _request = build_data_get_request( + name=name, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.DataContainer, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def create_or_update( + self, name: str, body: _models.DataContainer, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.DataContainer: + """Create or update container. + + :param name: Container name. Required. + :type name: str + :param body: Container entity to create or update. Required. + :type body: ~azure.ai.resources.autogen.models.DataContainer + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: DataContainer. The DataContainer is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DataContainer + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_or_update( + self, name: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.DataContainer: + """Create or update container. + + :param name: Container name. Required. + :type name: str + :param body: Container entity to create or update. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: DataContainer. The DataContainer is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DataContainer + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_or_update( + self, name: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.DataContainer: + """Create or update container. + + :param name: Container name. Required. + :type name: str + :param body: Container entity to create or update. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: DataContainer. The DataContainer is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DataContainer + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_or_update( + self, name: str, body: Union[_models.DataContainer, JSON, IO[bytes]], **kwargs: Any + ) -> _models.DataContainer: + """Create or update container. + + :param name: Container name. Required. + :type name: str + :param body: Container entity to create or update. Is one of the following types: + DataContainer, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.DataContainer or JSON or IO[bytes] + :return: DataContainer. The DataContainer is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DataContainer + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.DataContainer] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_data_create_or_update_request( + name=name, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.DataContainer, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + +class DataVersionsBaseOperations: + """ + .. warning:: + **DO NOT** instantiate this class directly. + + Instead, you should access the following operations through + :class:`~azure.ai.resources.autogen.aio.MachineLearningServicesClient`'s + :attr:`data_versions_base` attribute. + """ + + def __init__(self, *args, **kwargs) -> None: + input_args = list(args) + self._client = input_args.pop(0) if input_args else kwargs.pop("client") + self._config = input_args.pop(0) if input_args else kwargs.pop("config") + self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer") + self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer") + + @distributed_trace + def list( + self, + name: str, + *, + _order_by: Optional[str] = None, + _top: Optional[int] = None, + _skip: Optional[str] = None, + _tags: Optional[str] = None, + list_view_type: Optional[Union[str, _models.ListViewType]] = None, + **kwargs: Any + ) -> AsyncIterable["_models.DataVersionBase"]: + """List data versions in the data container. + + :param name: Container name. Required. + :type name: str + :keyword _order_by: Please choose OrderBy value from ['createdtime', 'modifiedtime']. Default + value is None. + :paramtype _order_by: str + :keyword _top: Top count of results, top count cannot be greater than the page size. If + topCount > page size, results with be default page size count will be returned. Default value + is None. + :paramtype _top: int + :keyword _skip: Continuation token for pagination. Default value is None. + :paramtype _skip: str + :keyword _tags: Comma-separated list of tag names (and optionally values). Example: + tag1,tag2=value2. Default value is None. + :paramtype _tags: str + :keyword list_view_type: [ListViewType.ActiveOnly, ListViewType.ArchivedOnly, ListViewType.All] + View type for including/excluding (for example) archived entities. Known values are: + "ActiveOnly", "ArchivedOnly", and "All". Default value is None. + :paramtype list_view_type: str or ~azure.ai.resources.autogen.models.ListViewType + :return: An iterator like instance of DataVersionBase + :rtype: + ~azure.core.async_paging.AsyncItemPaged[~azure.ai.resources.autogen.models.DataVersionBase] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.DataVersionBase]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_data_versions_base_list_request( + name=name, + _order_by=_order_by, + _top=_top, + _skip=_skip, + _tags=_tags, + list_view_type=list_view_type, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + async def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.DataVersionBase], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, AsyncList(list_of_elem) + + async def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return AsyncItemPaged(get_next, extract_data) + + @distributed_trace_async + async def delete(self, name: str, version: str, **kwargs: Any) -> None: + """Delete version. + + :param name: Container name. Required. + :type name: str + :param version: Version identifier. Required. + :type version: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_data_versions_base_delete_request( + name=name, + version=version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [204]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get(self, name: str, version: str, **kwargs: Any) -> _models.DataVersionBase: + """Get version. + + :param name: Container name. Required. + :type name: str + :param version: Version identifier. Required. + :type version: str + :return: DataVersionBase. The DataVersionBase is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DataVersionBase + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.DataVersionBase] = kwargs.pop("cls", None) + + _request = build_data_versions_base_get_request( + name=name, + version=version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.DataVersionBase, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def create_or_update( + self, + name: str, + version: str, + body: _models.DataVersionBase, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.DataVersionBase: + """Create or update version. + + :param name: Container name. Required. + :type name: str + :param version: Version identifier. Required. + :type version: str + :param body: Version entity to create or update. Required. + :type body: ~azure.ai.resources.autogen.models.DataVersionBase + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: DataVersionBase. The DataVersionBase is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DataVersionBase + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_or_update( + self, name: str, version: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.DataVersionBase: + """Create or update version. + + :param name: Container name. Required. + :type name: str + :param version: Version identifier. Required. + :type version: str + :param body: Version entity to create or update. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: DataVersionBase. The DataVersionBase is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DataVersionBase + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_or_update( + self, name: str, version: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.DataVersionBase: + """Create or update version. + + :param name: Container name. Required. + :type name: str + :param version: Version identifier. Required. + :type version: str + :param body: Version entity to create or update. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: DataVersionBase. The DataVersionBase is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DataVersionBase + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_or_update( + self, name: str, version: str, body: Union[_models.DataVersionBase, JSON, IO[bytes]], **kwargs: Any + ) -> _models.DataVersionBase: + """Create or update version. + + :param name: Container name. Required. + :type name: str + :param version: Version identifier. Required. + :type version: str + :param body: Version entity to create or update. Is one of the following types: + DataVersionBase, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.DataVersionBase or JSON or IO[bytes] + :return: DataVersionBase. The DataVersionBase is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DataVersionBase + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.DataVersionBase] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_data_versions_base_create_or_update_request( + name=name, + version=version, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.DataVersionBase, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def publish( + self, + name: str, + version: str, + body: _models.DestinationAsset, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> None: + """Publish version asset into registry. + + :param name: Container name. Required. + :type name: str + :param version: Version identifier. Required. + :type version: str + :param body: Destination registry info. Required. + :type body: ~azure.ai.resources.autogen.models.DestinationAsset + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def publish( + self, name: str, version: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Publish version asset into registry. + + :param name: Container name. Required. + :type name: str + :param version: Version identifier. Required. + :type version: str + :param body: Destination registry info. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def publish( + self, name: str, version: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Publish version asset into registry. + + :param name: Container name. Required. + :type name: str + :param version: Version identifier. Required. + :type version: str + :param body: Destination registry info. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def publish( + self, name: str, version: str, body: Union[_models.DestinationAsset, JSON, IO[bytes]], **kwargs: Any + ) -> None: + """Publish version asset into registry. + + :param name: Container name. Required. + :type name: str + :param version: Version identifier. Required. + :type version: str + :param body: Destination registry info. Is one of the following types: DestinationAsset, JSON, + IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.DestinationAsset or JSON or IO[bytes] + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_data_versions_base_publish_request( + name=name, + version=version, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [204]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + +class EvaluationsOperations: + """ + .. warning:: + **DO NOT** instantiate this class directly. + + Instead, you should access the following operations through + :class:`~azure.ai.resources.autogen.aio.MachineLearningServicesClient`'s + :attr:`evaluations` attribute. + """ + + def __init__(self, *args, **kwargs) -> None: + input_args = list(args) + self._client = input_args.pop(0) if input_args else kwargs.pop("client") + self._config = input_args.pop(0) if input_args else kwargs.pop("config") + self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer") + self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer") + + @distributed_trace_async + async def get(self, id: str, **kwargs: Any) -> _models.Evaluation: + """Get an evaluation. + + :param id: Identifier of the evaluation. Required. + :type id: str + :return: Evaluation. The Evaluation is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Evaluation + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.Evaluation] = kwargs.pop("cls", None) + + _request = build_evaluations_get_request( + id=id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Evaluation, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def create( + self, body: _models.Evaluation, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Evaluation: + """Creates an evaluation. + + :param body: Properties of Evaluation. Required. + :type body: ~azure.ai.resources.autogen.models.Evaluation + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Evaluation. The Evaluation is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Evaluation + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create(self, body: JSON, *, content_type: str = "application/json", **kwargs: Any) -> _models.Evaluation: + """Creates an evaluation. + + :param body: Properties of Evaluation. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Evaluation. The Evaluation is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Evaluation + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Evaluation: + """Creates an evaluation. + + :param body: Properties of Evaluation. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: Evaluation. The Evaluation is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Evaluation + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create(self, body: Union[_models.Evaluation, JSON, IO[bytes]], **kwargs: Any) -> _models.Evaluation: + """Creates an evaluation. + + :param body: Properties of Evaluation. Is one of the following types: Evaluation, JSON, + IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.Evaluation or JSON or IO[bytes] + :return: Evaluation. The Evaluation is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Evaluation + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.Evaluation] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_evaluations_create_request( + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [201]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Evaluation, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list( + self, *, top: Optional[int] = None, skip: Optional[int] = None, **kwargs: Any + ) -> AsyncIterable["_models.Evaluation"]: + """List evaluations. + + :keyword top: The number of result items to return. Default value is None. + :paramtype top: int + :keyword skip: The number of result items to skip. Default value is None. + :paramtype skip: int + :return: An iterator like instance of Evaluation + :rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.ai.resources.autogen.models.Evaluation] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + maxpagesize = kwargs.pop("maxpagesize", None) + cls: ClsType[List[_models.Evaluation]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_evaluations_list_request( + top=top, + skip=skip, + maxpagesize=maxpagesize, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + async def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.Evaluation], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, AsyncList(list_of_elem) + + async def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return AsyncItemPaged(get_next, extract_data) + + @overload + async def update( + self, id: str, body: _models.UpdateEvaluationRequest, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Evaluation: + """Update an evaluation. + + :param id: Identifier of the evaluation. Required. + :type id: str + :param body: Update evaluation request. Required. + :type body: ~azure.ai.resources.autogen.models.UpdateEvaluationRequest + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Evaluation. The Evaluation is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Evaluation + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update( + self, id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Evaluation: + """Update an evaluation. + + :param id: Identifier of the evaluation. Required. + :type id: str + :param body: Update evaluation request. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Evaluation. The Evaluation is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Evaluation + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update( + self, id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Evaluation: + """Update an evaluation. + + :param id: Identifier of the evaluation. Required. + :type id: str + :param body: Update evaluation request. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: Evaluation. The Evaluation is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Evaluation + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update( + self, id: str, body: Union[_models.UpdateEvaluationRequest, JSON, IO[bytes]], **kwargs: Any + ) -> _models.Evaluation: + """Update an evaluation. + + :param id: Identifier of the evaluation. Required. + :type id: str + :param body: Update evaluation request. Is one of the following types: UpdateEvaluationRequest, + JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.UpdateEvaluationRequest or JSON or IO[bytes] + :return: Evaluation. The Evaluation is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Evaluation + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.Evaluation] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_evaluations_update_request( + id=id, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Evaluation, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + +class IndexesOperations: + """ + .. warning:: + **DO NOT** instantiate this class directly. + + Instead, you should access the following operations through + :class:`~azure.ai.resources.autogen.aio.MachineLearningServicesClient`'s + :attr:`indexes` attribute. + """ + + def __init__(self, *args, **kwargs) -> None: + input_args = list(args) + self._client = input_args.pop(0) if input_args else kwargs.pop("client") + self._config = input_args.pop(0) if input_args else kwargs.pop("config") + self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer") + self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer") + + @distributed_trace_async + async def get(self, name: str, version: str, **kwargs: Any) -> _models.Index: + """Get a specific version of an Index. + + :param name: Name of the index. Required. + :type name: str + :param version: Version of the index. Required. + :type version: str + :return: Index. The Index is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Index + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.Index] = kwargs.pop("cls", None) + + _request = build_indexes_get_request( + name=name, + version=version, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Index, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def create_or_update( + self, name: str, version: str, body: _models.Index, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Index: + """Creates or updates a IndexVersion. + + :param name: Name of the index. Required. + :type name: str + :param version: Version of the index. Required. + :type version: str + :param body: Properties of an Index Version. Required. + :type body: ~azure.ai.resources.autogen.models.Index + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Index. The Index is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Index + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_or_update( + self, name: str, version: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Index: + """Creates or updates a IndexVersion. + + :param name: Name of the index. Required. + :type name: str + :param version: Version of the index. Required. + :type version: str + :param body: Properties of an Index Version. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Index. The Index is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Index + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_or_update( + self, name: str, version: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Index: + """Creates or updates a IndexVersion. + + :param name: Name of the index. Required. + :type name: str + :param version: Version of the index. Required. + :type version: str + :param body: Properties of an Index Version. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: Index. The Index is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Index + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_or_update( + self, name: str, version: str, body: Union[_models.Index, JSON, IO[bytes]], **kwargs: Any + ) -> _models.Index: + """Creates or updates a IndexVersion. + + :param name: Name of the index. Required. + :type name: str + :param version: Version of the index. Required. + :type version: str + :param body: Properties of an Index Version. Is one of the following types: Index, JSON, + IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.Index or JSON or IO[bytes] + :return: Index. The Index is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Index + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.Index] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_indexes_create_or_update_request( + name=name, + version=version, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200, 201]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Index, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list( + self, + name: str, + *, + list_view_type: str, + order_by: Optional[str] = None, + orderby: Optional[str] = None, + tags: Optional[str] = None, + top: Optional[int] = None, + skip: Optional[int] = None, + **kwargs: Any + ) -> AsyncIterable["_models.Index"]: + """List the versions of an Index given the name. + + :param name: Name of the index. Required. + :type name: str + :keyword list_view_type: View type for including/excluding (for example) archived entities. + Required. + :paramtype list_view_type: str + :keyword order_by: Ordering of list: Please choose orderby value from ['createdAt', + 'lastModifiedAt']. Default value is None. + :paramtype order_by: str + :keyword orderby: Ordering of list: Please choose orderby value from ['createdAt', + 'lastModifiedAt']. Default value is None. + :paramtype orderby: str + :keyword tags: Comma-separated list of tag names (and optionally values). Example: + tag1,tag2=value2. Default value is None. + :paramtype tags: str + :keyword top: The number of result items to return. Default value is None. + :paramtype top: int + :keyword skip: The number of result items to skip. Default value is None. + :paramtype skip: int + :return: An iterator like instance of Index + :rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.ai.resources.autogen.models.Index] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + maxpagesize = kwargs.pop("maxpagesize", None) + cls: ClsType[List[_models.Index]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_indexes_list_request( + name=name, + list_view_type=list_view_type, + order_by=order_by, + orderby=orderby, + tags=tags, + top=top, + skip=skip, + maxpagesize=maxpagesize, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + async def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.Index], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, AsyncList(list_of_elem) + + async def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return AsyncItemPaged(get_next, extract_data) + + @distributed_trace_async + async def get_latest(self, name: str, **kwargs: Any) -> _models.Index: + """Get latest version of the Index. Latest is defined by most recent created by date. + + :param name: Name of the index. Required. + :type name: str + :return: Index. The Index is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Index + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.Index] = kwargs.pop("cls", None) + + _request = build_indexes_get_latest_request( + name=name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Index, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_next_version(self, name: str, **kwargs: Any) -> _models.VersionInfo: + """Get next Index version as defined by the server. The server keeps track of all versions that + are string-representations of integers. If one exists, the nextVersion will be a string + representation of the highest integer value + 1. Otherwise, the nextVersion will default to + '1'. + + :param name: Name of the index. Required. + :type name: str + :return: VersionInfo. The VersionInfo is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VersionInfo + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VersionInfo] = kwargs.pop("cls", None) + + _request = build_indexes_get_next_version_request( + name=name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VersionInfo, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_latest( + self, *, top: Optional[int] = None, skip: Optional[int] = None, **kwargs: Any + ) -> AsyncIterable["_models.Index"]: + """List the latest version of each index. Latest is defined by most recent created by date. + + :keyword top: The number of result items to return. Default value is None. + :paramtype top: int + :keyword skip: The number of result items to skip. Default value is None. + :paramtype skip: int + :return: An iterator like instance of Index + :rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.ai.resources.autogen.models.Index] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + maxpagesize = kwargs.pop("maxpagesize", None) + cls: ClsType[List[_models.Index]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_indexes_list_latest_request( + top=top, + skip=skip, + maxpagesize=maxpagesize, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + async def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.Index], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, AsyncList(list_of_elem) + + async def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return AsyncItemPaged(get_next, extract_data) + + +class ModelContainersOperations: + """ + .. warning:: + **DO NOT** instantiate this class directly. + + Instead, you should access the following operations through + :class:`~azure.ai.resources.autogen.aio.MachineLearningServicesClient`'s + :attr:`model_containers` attribute. + """ + + def __init__(self, *args, **kwargs) -> None: + input_args = list(args) + self._client = input_args.pop(0) if input_args else kwargs.pop("client") + self._config = input_args.pop(0) if input_args else kwargs.pop("config") + self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer") + self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer") + + @distributed_trace_async + async def get(self, name: str, **kwargs: Any) -> _models.ModelContainer: + """Get a model container. + + :param name: Name of the model container. Required. + :type name: str + :return: ModelContainer. The ModelContainer is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ModelContainer + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ModelContainer] = kwargs.pop("cls", None) + + _request = build_model_containers_get_request( + name=name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ModelContainer, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def create_or_update(self, name: str, **kwargs: Any) -> _models.ModelContainer: + """Creates or updates a model container. + + :param name: Name of the model container. Required. + :type name: str + :return: ModelContainer. The ModelContainer is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ModelContainer + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ModelContainer] = kwargs.pop("cls", None) + + _request = build_model_containers_create_or_update_request( + name=name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200, 201]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ModelContainer, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + +class ModelVersionsOperations: + """ + .. warning:: + **DO NOT** instantiate this class directly. + + Instead, you should access the following operations through + :class:`~azure.ai.resources.autogen.aio.MachineLearningServicesClient`'s + :attr:`model_versions` attribute. + """ + + def __init__(self, *args, **kwargs) -> None: + input_args = list(args) + self._client = input_args.pop(0) if input_args else kwargs.pop("client") + self._config = input_args.pop(0) if input_args else kwargs.pop("config") + self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer") + self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer") + + @distributed_trace + def list( + self, + name: str, + *, + _skip: Optional[str] = None, + _order_by: Optional[str] = None, + _top: Optional[int] = None, + version: Optional[str] = None, + description: Optional[str] = None, + offset: Optional[int] = None, + tags: Optional[str] = None, + properties: Optional[str] = None, + feed: Optional[str] = None, + list_view_type: Optional[Union[str, _models.ListViewType]] = None, + **kwargs: Any + ) -> AsyncIterable["_models.ModelVersion"]: + """List model versions. + + :param name: Name of the model container. Required. + :type name: str + :keyword _skip: $skip. Default value is None. + :paramtype _skip: str + :keyword _order_by: $orderBy. Default value is None. + :paramtype _order_by: str + :keyword _top: $top. Default value is None. + :paramtype _top: int + :keyword version: Model version. Default value is None. + :paramtype version: str + :keyword description: Model description. Default value is None. + :paramtype description: str + :keyword offset: Number of initial results to skip. Default value is None. + :paramtype offset: int + :keyword tags: Comma-separated list of tag names (and optionally values). Example: + tag1,tag2=value2. Default value is None. + :paramtype tags: str + :keyword properties: Comma-separated list of property names (and optionally values). Example: + prop1,prop2=value2. Default value is None. + :paramtype properties: str + :keyword feed: Name of the feed. Default value is None. + :paramtype feed: str + :keyword list_view_type: View type for including/excluding (for example) archived entities. + Known values are: "ActiveOnly", "ArchivedOnly", and "All". Default value is None. + :paramtype list_view_type: str or ~azure.ai.resources.autogen.models.ListViewType + :return: An iterator like instance of ModelVersion + :rtype: + ~azure.core.async_paging.AsyncItemPaged[~azure.ai.resources.autogen.models.ModelVersion] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.ModelVersion]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_model_versions_list_request( + name=name, + _skip=_skip, + _order_by=_order_by, + _top=_top, + version=version, + description=description, + offset=offset, + tags=tags, + properties=properties, + feed=feed, + list_view_type=list_view_type, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + async def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.ModelVersion], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, AsyncList(list_of_elem) + + async def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return AsyncItemPaged(get_next, extract_data) + + +class MachineLearningServicesClientOperationsMixin( # pylint: disable=too-many-public-methods,name-too-long + MachineLearningServicesClientMixinABC +): + + @overload + async def create_assistant( + self, body: _models.AssistantCreationOptions, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Assistant: + """Creates a new assistant. + + :param body: The request details to use when creating a new assistant. Required. + :type body: ~azure.ai.resources.autogen.models.AssistantCreationOptions + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_assistant( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Assistant: + """Creates a new assistant. + + :param body: The request details to use when creating a new assistant. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_assistant( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Assistant: + """Creates a new assistant. + + :param body: The request details to use when creating a new assistant. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_assistant( + self, body: Union[_models.AssistantCreationOptions, JSON, IO[bytes]], **kwargs: Any + ) -> _models.Assistant: + """Creates a new assistant. + + :param body: The request details to use when creating a new assistant. Is one of the following + types: AssistantCreationOptions, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.AssistantCreationOptions or JSON or IO[bytes] + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.Assistant] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_assistant_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Assistant, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def list_assistants( + self, + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIPageableListOfAssistant: + """Gets a list of assistants that were previously created. + + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.resources.autogen.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfAssistant. The OpenAIPageableListOfAssistant is compatible with + MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OpenAIPageableListOfAssistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfAssistant] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_list_assistants_request( + limit=limit, + order=order, + after=after, + before=before, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfAssistant, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_assistant(self, assistant_id: str, **kwargs: Any) -> _models.Assistant: + """Retrieves an existing assistant. + + :param assistant_id: The ID of the assistant to retrieve. Required. + :type assistant_id: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.Assistant] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_assistant_request( + assistant_id=assistant_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Assistant, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def update_assistant( + self, + assistant_id: str, + body: _models.UpdateAssistantOptions, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.Assistant: + """Modifies an existing assistant. + + :param assistant_id: The ID of the assistant to modify. Required. + :type assistant_id: str + :param body: The request details to use when modifying an existing assistant. Required. + :type body: ~azure.ai.resources.autogen.models.UpdateAssistantOptions + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_assistant( + self, assistant_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Assistant: + """Modifies an existing assistant. + + :param assistant_id: The ID of the assistant to modify. Required. + :type assistant_id: str + :param body: The request details to use when modifying an existing assistant. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_assistant( + self, assistant_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Assistant: + """Modifies an existing assistant. + + :param assistant_id: The ID of the assistant to modify. Required. + :type assistant_id: str + :param body: The request details to use when modifying an existing assistant. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_assistant( + self, assistant_id: str, body: Union[_models.UpdateAssistantOptions, JSON, IO[bytes]], **kwargs: Any + ) -> _models.Assistant: + """Modifies an existing assistant. + + :param assistant_id: The ID of the assistant to modify. Required. + :type assistant_id: str + :param body: The request details to use when modifying an existing assistant. Is one of the + following types: UpdateAssistantOptions, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.UpdateAssistantOptions or JSON or IO[bytes] + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.Assistant] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_update_assistant_request( + assistant_id=assistant_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Assistant, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def delete_assistant(self, assistant_id: str, **kwargs: Any) -> _models.AssistantDeletionStatus: + """Deletes an assistant. + + :param assistant_id: The ID of the assistant to delete. Required. + :type assistant_id: str + :return: AssistantDeletionStatus. The AssistantDeletionStatus is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.AssistantDeletionStatus + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.AssistantDeletionStatus] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_delete_assistant_request( + assistant_id=assistant_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AssistantDeletionStatus, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def list_files( + self, *, purpose: Optional[Union[str, _models.FilePurpose]] = None, **kwargs: Any + ) -> _models.FileListResponse: + """Gets a list of previously uploaded files. + + :keyword purpose: A value that, when provided, limits list results to files matching the + corresponding purpose. Known values are: "fine-tune", "fine-tune-results", "assistants", + "assistants_output", "batch", "batch_output", and "vision". Default value is None. + :paramtype purpose: str or ~azure.ai.resources.autogen.models.FilePurpose + :return: FileListResponse. The FileListResponse is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.FileListResponse + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.FileListResponse] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_list_files_request( + purpose=purpose, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FileListResponse, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def upload_file(self, body: JSON, **kwargs: Any) -> _models.OpenAIFile: + """Uploads a file for use by other operations. + + :param body: Required. + :type body: JSON + :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OpenAIFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def upload_file( + self, *, file: FileType, purpose: Union[str, _models.FilePurpose], filename: Optional[str] = None, **kwargs: Any + ) -> _models.OpenAIFile: + """Uploads a file for use by other operations. + + :keyword file: The file data (not filename) to upload. Required. + :paramtype file: ~azure.ai.resources.autogen._vendor.FileType + :keyword purpose: The intended purpose of the file. Known values are: "fine-tune", + "fine-tune-results", "assistants", "assistants_output", "batch", "batch_output", and "vision". + Required. + :paramtype purpose: str or ~azure.ai.resources.autogen.models.FilePurpose + :keyword filename: A filename to associate with the uploaded data. Default value is None. + :paramtype filename: str + :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OpenAIFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def upload_file( + self, + body: JSON = _Unset, + *, + file: FileType = _Unset, + purpose: Union[str, _models.FilePurpose] = _Unset, + filename: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIFile: + """Uploads a file for use by other operations. + + :param body: Is one of the following types: JSON Required. + :type body: JSON + :keyword file: The file data (not filename) to upload. Required. + :paramtype file: ~azure.ai.resources.autogen._vendor.FileType + :keyword purpose: The intended purpose of the file. Known values are: "fine-tune", + "fine-tune-results", "assistants", "assistants_output", "batch", "batch_output", and "vision". + Required. + :paramtype purpose: str or ~azure.ai.resources.autogen.models.FilePurpose + :keyword filename: A filename to associate with the uploaded data. Default value is None. + :paramtype filename: str + :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OpenAIFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIFile] = kwargs.pop("cls", None) + + if body is _Unset: + if file is _Unset: + raise TypeError("missing required argument: file") + if purpose is _Unset: + raise TypeError("missing required argument: purpose") + body = {"file": file, "filename": filename, "purpose": purpose} + body = {k: v for k, v in body.items() if v is not None} + _body = body.as_dict() if isinstance(body, _model_base.Model) else body + _file_fields: List[str] = ["file"] + _data_fields: List[str] = ["purpose", "filename"] + _files, _data = prepare_multipart_form_data(_body, _file_fields, _data_fields) + + _request = build_machine_learning_services_upload_file_request( + files=_files, + data=_data, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def delete_file(self, file_id: str, **kwargs: Any) -> _models.FileDeletionStatus: + """Delete a previously uploaded file. + + :param file_id: The ID of the file to delete. Required. + :type file_id: str + :return: FileDeletionStatus. The FileDeletionStatus is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.FileDeletionStatus + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.FileDeletionStatus] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_delete_file_request( + file_id=file_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FileDeletionStatus, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_file(self, file_id: str, **kwargs: Any) -> _models.OpenAIFile: + """Returns information about a specific file. Does not retrieve file content. + + :param file_id: The ID of the file to retrieve. Required. + :type file_id: str + :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OpenAIFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIFile] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_file_request( + file_id=file_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_file_content(self, file_id: str, **kwargs: Any) -> bytes: + """Returns information about a specific file. Does not retrieve file content. + + :param file_id: The ID of the file to retrieve. Required. + :type file_id: str + :return: bytes + :rtype: bytes + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[bytes] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_file_content_request( + file_id=file_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(bytes, response.json(), format="base64") + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_online_endpoint(self, name: str, **kwargs: Any) -> _models.OnlineEndpoint: + """Get an Online Endpoint. + + :param name: Name of the endpoint. Required. + :type name: str + :return: OnlineEndpoint. The OnlineEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OnlineEndpoint] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_online_endpoint_request( + name=name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OnlineEndpoint, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def delete_online_endpoint(self, name: str, **kwargs: Any) -> None: + """Delete an Online Endpoint. + + :param name: Name of the endpoint. Required. + :type name: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_delete_online_endpoint_request( + name=name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [204]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @overload + async def update_online_endpoint( + self, + name: str, + body: _models.PartialMinimalTrackedResourceWithIdentity, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.OnlineEndpoint: + """Update an Online Endpoint. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint entity to apply during operation. Required. + :type body: ~azure.ai.resources.autogen.models.PartialMinimalTrackedResourceWithIdentity + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineEndpoint. The OnlineEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_online_endpoint( + self, name: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.OnlineEndpoint: + """Update an Online Endpoint. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint entity to apply during operation. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineEndpoint. The OnlineEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_online_endpoint( + self, name: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.OnlineEndpoint: + """Update an Online Endpoint. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint entity to apply during operation. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineEndpoint. The OnlineEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_online_endpoint( + self, name: str, body: Union[_models.PartialMinimalTrackedResourceWithIdentity, JSON, IO[bytes]], **kwargs: Any + ) -> _models.OnlineEndpoint: + """Update an Online Endpoint. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint entity to apply during operation. Is one of the following types: + PartialMinimalTrackedResourceWithIdentity, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.PartialMinimalTrackedResourceWithIdentity or + JSON or IO[bytes] + :return: OnlineEndpoint. The OnlineEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.OnlineEndpoint] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_update_online_endpoint_request( + name=name, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OnlineEndpoint, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def create_or_update_online_endpoint( + self, name: str, body: _models.OnlineEndpoint, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.OnlineEndpoint: + """Create or Update an Online Endpoint. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint. Required. + :type body: ~azure.ai.resources.autogen.models.OnlineEndpoint + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineEndpoint. The OnlineEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_or_update_online_endpoint( + self, name: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.OnlineEndpoint: + """Create or Update an Online Endpoint. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineEndpoint. The OnlineEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_or_update_online_endpoint( + self, name: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.OnlineEndpoint: + """Create or Update an Online Endpoint. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineEndpoint. The OnlineEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_or_update_online_endpoint( + self, name: str, body: Union[_models.OnlineEndpoint, JSON, IO[bytes]], **kwargs: Any + ) -> _models.OnlineEndpoint: + """Create or Update an Online Endpoint. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint. Is one of the following types: OnlineEndpoint, JSON, IO[bytes] + Required. + :type body: ~azure.ai.resources.autogen.models.OnlineEndpoint or JSON or IO[bytes] + :return: OnlineEndpoint. The OnlineEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.OnlineEndpoint] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_or_update_online_endpoint_request( + name=name, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OnlineEndpoint, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_online_endpoints( + self, + *, + name: Optional[str] = None, + count: Optional[int] = None, + compute_type: Optional[Union[str, _models.EndpointComputeType]] = None, + tags: Optional[str] = None, + properties: Optional[str] = None, + order_by: Optional[Union[str, _models.OrderString]] = None, + **kwargs: Any + ) -> AsyncIterable["_models.OnlineEndpoint"]: + """List Online Endpoints. + + :keyword name: Name of the endpoint. Default value is None. + :paramtype name: str + :keyword count: Number of endpoints to be retrieved in a page of results. Default value is + None. + :paramtype count: int + :keyword compute_type: EndpointComputeType to be filtered by. Known values are: "Managed", + "Kubernetes", and "AzureMLCompute". Default value is None. + :paramtype compute_type: str or ~azure.ai.resources.autogen.models.EndpointComputeType + :keyword tags: A set of tags with which to filter the returned models. It is a comma separated + string of tags key or tags key=value. Example: tagKey1,tagKey2,tagKey3=value3 . Default value + is None. + :paramtype tags: str + :keyword properties: A set of properties with which to filter the returned models. It is a + comma separated string of properties key and/or properties key=value Example: + propKey1,propKey2,propKey3=value3 . Default value is None. + :paramtype properties: str + :keyword order_by: The option to order the response. Known values are: "CreatedAtDesc", + "CreatedAtAsc", "UpdatedAtDesc", and "UpdatedAtAsc". Default value is None. + :paramtype order_by: str or ~azure.ai.resources.autogen.models.OrderString + :return: An iterator like instance of OnlineEndpoint + :rtype: + ~azure.core.async_paging.AsyncItemPaged[~azure.ai.resources.autogen.models.OnlineEndpoint] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.OnlineEndpoint]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_machine_learning_services_list_online_endpoints_request( + name=name, + count=count, + compute_type=compute_type, + tags=tags, + properties=properties, + order_by=order_by, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + async def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.OnlineEndpoint], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, AsyncList(list_of_elem) + + async def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return AsyncItemPaged(get_next, extract_data) + + @overload + async def list_keys_online_endpoint( + self, + name: str, + body: _models.RegenerateEndpointKeysRequest, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.EndpointAuthKeys: + """List EndpointAuthKeys for an Endpoint using Key-based authentication. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint. Required. + :type body: ~azure.ai.resources.autogen.models.RegenerateEndpointKeysRequest + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: EndpointAuthKeys. The EndpointAuthKeys is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.EndpointAuthKeys + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def list_keys_online_endpoint( + self, name: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.EndpointAuthKeys: + """List EndpointAuthKeys for an Endpoint using Key-based authentication. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: EndpointAuthKeys. The EndpointAuthKeys is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.EndpointAuthKeys + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def list_keys_online_endpoint( + self, name: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.EndpointAuthKeys: + """List EndpointAuthKeys for an Endpoint using Key-based authentication. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: EndpointAuthKeys. The EndpointAuthKeys is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.EndpointAuthKeys + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def list_keys_online_endpoint( + self, name: str, body: Union[_models.RegenerateEndpointKeysRequest, JSON, IO[bytes]], **kwargs: Any + ) -> _models.EndpointAuthKeys: + """List EndpointAuthKeys for an Endpoint using Key-based authentication. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint. Is one of the following types: RegenerateEndpointKeysRequest, + JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.RegenerateEndpointKeysRequest or JSON or + IO[bytes] + :return: EndpointAuthKeys. The EndpointAuthKeys is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.EndpointAuthKeys + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.EndpointAuthKeys] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_list_keys_online_endpoint_request( + name=name, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.EndpointAuthKeys, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def regenerate_keys_online_endpoint(self, name: str, **kwargs: Any) -> _models.EndpointAuthKeys: + """Regenerate EndpointAuthKeys for an Endpoint using Key-based authentication (asynchronous). + + :param name: Name of the endpoint. Required. + :type name: str + :return: EndpointAuthKeys. The EndpointAuthKeys is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.EndpointAuthKeys + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.EndpointAuthKeys] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_regenerate_keys_online_endpoint_request( + name=name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.EndpointAuthKeys, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_token_online_endpoint(self, name: str, **kwargs: Any) -> _models.EndpointAuthToken: + """Retrieve a valid AML token for an Endpoint using AMLToken-based authentication. + + :param name: Name of the endpoint. Required. + :type name: str + :return: EndpointAuthToken. The EndpointAuthToken is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.EndpointAuthToken + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.EndpointAuthToken] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_token_online_endpoint_request( + name=name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.EndpointAuthToken, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_online_deployments( + self, endpoint_name: str, *, _order_by: Optional[str] = None, _top: Optional[int] = None, **kwargs: Any + ) -> AsyncIterable["_models.OnlineDeployment"]: + """List Online Deployments. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :keyword _order_by: Ordering of list. Default value is None. + :paramtype _order_by: str + :keyword _top: Top of list. Default value is None. + :paramtype _top: int + :return: An iterator like instance of OnlineDeployment + :rtype: + ~azure.core.async_paging.AsyncItemPaged[~azure.ai.resources.autogen.models.OnlineDeployment] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.OnlineDeployment]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_machine_learning_services_list_online_deployments_request( + endpoint_name=endpoint_name, + _order_by=_order_by, + _top=_top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + async def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.OnlineDeployment], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, AsyncList(list_of_elem) + + async def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return AsyncItemPaged(get_next, extract_data) + + @distributed_trace_async + async def delete_online_deployment(self, endpoint_name: str, deployment_name: str, **kwargs: Any) -> None: + """Delete an Online Deployment. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_delete_online_deployment_request( + endpoint_name=endpoint_name, + deployment_name=deployment_name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [204]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get_online_deployment( + self, endpoint_name: str, deployment_name: str, **kwargs: Any + ) -> _models.OnlineDeployment: + """Gets an online inference deployment by id. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :return: OnlineDeployment. The OnlineDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OnlineDeployment] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_online_deployment_request( + endpoint_name=endpoint_name, + deployment_name=deployment_name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OnlineDeployment, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def update_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: _models.PartialMinimalTrackedResourceWithSku, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.OnlineDeployment: + """Update a Online Deployment. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: Online Endpoint entity to apply during operation. Required. + :type body: ~azure.ai.resources.autogen.models.PartialMinimalTrackedResourceWithSku + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineDeployment. The OnlineDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: JSON, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.OnlineDeployment: + """Update a Online Deployment. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: Online Endpoint entity to apply during operation. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineDeployment. The OnlineDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: IO[bytes], + *, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.OnlineDeployment: + """Update a Online Deployment. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: Online Endpoint entity to apply during operation. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineDeployment. The OnlineDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: Union[_models.PartialMinimalTrackedResourceWithSku, JSON, IO[bytes]], + **kwargs: Any + ) -> _models.OnlineDeployment: + """Update a Online Deployment. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: Online Endpoint entity to apply during operation. Is one of the following types: + PartialMinimalTrackedResourceWithSku, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.PartialMinimalTrackedResourceWithSku or JSON or + IO[bytes] + :return: OnlineDeployment. The OnlineDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.OnlineDeployment] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_update_online_deployment_request( + endpoint_name=endpoint_name, + deployment_name=deployment_name, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OnlineDeployment, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def create_or_update_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: _models.OnlineDeployment, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.OnlineDeployment: + """Create or Update an Online Deployment. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: Inference Endpoint entity to apply during operation. Required. + :type body: ~azure.ai.resources.autogen.models.OnlineDeployment + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineDeployment. The OnlineDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_or_update_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: JSON, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.OnlineDeployment: + """Create or Update an Online Deployment. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: Inference Endpoint entity to apply during operation. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineDeployment. The OnlineDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_or_update_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: IO[bytes], + *, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.OnlineDeployment: + """Create or Update an Online Deployment. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: Inference Endpoint entity to apply during operation. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineDeployment. The OnlineDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_or_update_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: Union[_models.OnlineDeployment, JSON, IO[bytes]], + **kwargs: Any + ) -> _models.OnlineDeployment: + """Create or Update an Online Deployment. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: Inference Endpoint entity to apply during operation. Is one of the following + types: OnlineDeployment, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.OnlineDeployment or JSON or IO[bytes] + :return: OnlineDeployment. The OnlineDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.OnlineDeployment] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_or_update_online_deployment_request( + endpoint_name=endpoint_name, + deployment_name=deployment_name, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OnlineDeployment, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def poll_logs_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: _models.DeploymentLogsRequest, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.DeploymentLogs: + """Polls an Endpoint operation. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: The request containing parameters for retrieving logs. Required. + :type body: ~azure.ai.resources.autogen.models.DeploymentLogsRequest + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: DeploymentLogs. The DeploymentLogs is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DeploymentLogs + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def poll_logs_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: JSON, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.DeploymentLogs: + """Polls an Endpoint operation. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: The request containing parameters for retrieving logs. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: DeploymentLogs. The DeploymentLogs is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DeploymentLogs + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def poll_logs_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: IO[bytes], + *, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.DeploymentLogs: + """Polls an Endpoint operation. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: The request containing parameters for retrieving logs. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: DeploymentLogs. The DeploymentLogs is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DeploymentLogs + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def poll_logs_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: Union[_models.DeploymentLogsRequest, JSON, IO[bytes]], + **kwargs: Any + ) -> _models.DeploymentLogs: + """Polls an Endpoint operation. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: The request containing parameters for retrieving logs. Is one of the following + types: DeploymentLogsRequest, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.DeploymentLogsRequest or JSON or IO[bytes] + :return: DeploymentLogs. The DeploymentLogs is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DeploymentLogs + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.DeploymentLogs] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_poll_logs_online_deployment_request( + endpoint_name=endpoint_name, + deployment_name=deployment_name, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.DeploymentLogs, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_skus_online_deployment( + self, endpoint_name: str, deployment_name: str, *, count: int, **kwargs: Any + ) -> AsyncIterable["_models.SkuResource"]: + """List Inference Endpoint Deployment Skus. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :keyword count: Number of Skus to be retrieved in a page of results. Required. + :paramtype count: int + :return: An iterator like instance of SkuResource + :rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.ai.resources.autogen.models.SkuResource] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.SkuResource]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_machine_learning_services_get_skus_online_deployment_request( + endpoint_name=endpoint_name, + deployment_name=deployment_name, + count=count, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + async def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.SkuResource], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, AsyncList(list_of_elem) + + async def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return AsyncItemPaged(get_next, extract_data) + + @distributed_trace_async + async def get_batch_endpoint(self, endpoint_name: str, **kwargs: Any) -> _models.BatchEndpoint: + """Get a Batch Endpoint. + + :param endpoint_name: Name for the Batch Endpoint. Required. + :type endpoint_name: str + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.BatchEndpoint] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_batch_endpoint_request( + endpoint_name=endpoint_name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.BatchEndpoint, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def delete_batch_endpoint(self, endpoint_name: str, **kwargs: Any) -> None: + """Delete an Batch Endpoint. + + :param endpoint_name: Inference Endpoint name. Required. + :type endpoint_name: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_delete_batch_endpoint_request( + endpoint_name=endpoint_name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [204]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @overload + async def update_batch_endpoint( + self, + endpoint_name: str, + body: _models.PartialMinimalTrackedResourceWithIdentity, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.BatchEndpoint: + """Update a Batch Endpoint. + + :param endpoint_name: Name for the Batch inference endpoint. Required. + :type endpoint_name: str + :param body: Mutable batch inference endpoint definition object. Required. + :type body: ~azure.ai.resources.autogen.models.PartialMinimalTrackedResourceWithIdentity + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_batch_endpoint( + self, endpoint_name: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.BatchEndpoint: + """Update a Batch Endpoint. + + :param endpoint_name: Name for the Batch inference endpoint. Required. + :type endpoint_name: str + :param body: Mutable batch inference endpoint definition object. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_batch_endpoint( + self, endpoint_name: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.BatchEndpoint: + """Update a Batch Endpoint. + + :param endpoint_name: Name for the Batch inference endpoint. Required. + :type endpoint_name: str + :param body: Mutable batch inference endpoint definition object. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_batch_endpoint( + self, + endpoint_name: str, + body: Union[_models.PartialMinimalTrackedResourceWithIdentity, JSON, IO[bytes]], + **kwargs: Any + ) -> _models.BatchEndpoint: + """Update a Batch Endpoint. + + :param endpoint_name: Name for the Batch inference endpoint. Required. + :type endpoint_name: str + :param body: Mutable batch inference endpoint definition object. Is one of the following types: + PartialMinimalTrackedResourceWithIdentity, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.PartialMinimalTrackedResourceWithIdentity or + JSON or IO[bytes] + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.BatchEndpoint] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_update_batch_endpoint_request( + endpoint_name=endpoint_name, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.BatchEndpoint, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def create_or_update_batch_endpoint( + self, endpoint_name: str, body: _models.BatchEndpoint, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.BatchEndpoint: + """Create or Update a Batch Endpoint. + + :param endpoint_name: Name for the Batch inference endpoint. Required. + :type endpoint_name: str + :param body: Batch inference endpoint definition object. Required. + :type body: ~azure.ai.resources.autogen.models.BatchEndpoint + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_or_update_batch_endpoint( + self, endpoint_name: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.BatchEndpoint: + """Create or Update a Batch Endpoint. + + :param endpoint_name: Name for the Batch inference endpoint. Required. + :type endpoint_name: str + :param body: Batch inference endpoint definition object. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_or_update_batch_endpoint( + self, endpoint_name: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.BatchEndpoint: + """Create or Update a Batch Endpoint. + + :param endpoint_name: Name for the Batch inference endpoint. Required. + :type endpoint_name: str + :param body: Batch inference endpoint definition object. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_or_update_batch_endpoint( + self, endpoint_name: str, body: Union[_models.BatchEndpoint, JSON, IO[bytes]], **kwargs: Any + ) -> _models.BatchEndpoint: + """Create or Update a Batch Endpoint. + + :param endpoint_name: Name for the Batch inference endpoint. Required. + :type endpoint_name: str + :param body: Batch inference endpoint definition object. Is one of the following types: + BatchEndpoint, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.BatchEndpoint or JSON or IO[bytes] + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.BatchEndpoint] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_or_update_batch_endpoint_request( + endpoint_name=endpoint_name, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.BatchEndpoint, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_batch_endpoints( + self, *, count: Optional[int] = None, **kwargs: Any + ) -> AsyncIterable["_models.BatchEndpoint"]: + """List Batch Endpoints. + + :keyword count: Number of endpoints to be retrieved in a page of results. Default value is + None. + :paramtype count: int + :return: An iterator like instance of BatchEndpoint + :rtype: + ~azure.core.async_paging.AsyncItemPaged[~azure.ai.resources.autogen.models.BatchEndpoint] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.BatchEndpoint]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_machine_learning_services_list_batch_endpoints_request( + count=count, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + async def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.BatchEndpoint], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, AsyncList(list_of_elem) + + async def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return AsyncItemPaged(get_next, extract_data) + + @distributed_trace + def list_batch_deployments( + self, + endpoint_name: str, + *, + _order_by: Optional[str] = None, + _top: Optional[int] = None, + _skip: Optional[str] = None, + **kwargs: Any + ) -> AsyncIterable["_models.BatchDeployment"]: + """List Batch Deployments. + + :param endpoint_name: Endpoint name. Required. + :type endpoint_name: str + :keyword _order_by: Ordering of list. Default value is None. + :paramtype _order_by: str + :keyword _top: Top of list. Default value is None. + :paramtype _top: int + :keyword _skip: Continuation token for pagination. Default value is None. + :paramtype _skip: str + :return: An iterator like instance of BatchDeployment + :rtype: + ~azure.core.async_paging.AsyncItemPaged[~azure.ai.resources.autogen.models.BatchDeployment] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.BatchDeployment]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_machine_learning_services_list_batch_deployments_request( + endpoint_name=endpoint_name, + _order_by=_order_by, + _top=_top, + _skip=_skip, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + async def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.BatchDeployment], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, AsyncList(list_of_elem) + + async def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return AsyncItemPaged(get_next, extract_data) + + @distributed_trace_async + async def delete_batch_deployment(self, endpoint_name: str, deployment_name: str, **kwargs: Any) -> None: + """Delete an Batch Deployment. + + :param endpoint_name: Endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference deployment identifier. Required. + :type deployment_name: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_delete_batch_deployment_request( + endpoint_name=endpoint_name, + deployment_name=deployment_name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [204]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get_batch_deployment( + self, endpoint_name: str, deployment_name: str, **kwargs: Any + ) -> _models.BatchDeployment: + """Gets a batch inference deployment by id. + + :param endpoint_name: Endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference deployment identifier. Required. + :type deployment_name: str + :return: BatchDeployment. The BatchDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.BatchDeployment] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_batch_deployment_request( + endpoint_name=endpoint_name, + deployment_name=deployment_name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.BatchDeployment, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def update_batch_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: _models.PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.BatchDeployment: + """Update a Batch Deployment. + + :param endpoint_name: Endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference deployment identifier. Required. + :type deployment_name: str + :param body: Mutable batch inference endpoint definition object. Required. + :type body: + ~azure.ai.resources.autogen.models.PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchDeployment. The BatchDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_batch_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: JSON, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.BatchDeployment: + """Update a Batch Deployment. + + :param endpoint_name: Endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference deployment identifier. Required. + :type deployment_name: str + :param body: Mutable batch inference endpoint definition object. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchDeployment. The BatchDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_batch_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: IO[bytes], + *, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.BatchDeployment: + """Update a Batch Deployment. + + :param endpoint_name: Endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference deployment identifier. Required. + :type deployment_name: str + :param body: Mutable batch inference endpoint definition object. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchDeployment. The BatchDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_batch_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: Union[_models.PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties, JSON, IO[bytes]], + **kwargs: Any + ) -> _models.BatchDeployment: + """Update a Batch Deployment. + + :param endpoint_name: Endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference deployment identifier. Required. + :type deployment_name: str + :param body: Mutable batch inference endpoint definition object. Is one of the following types: + PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties, JSON, IO[bytes] Required. + :type body: + ~azure.ai.resources.autogen.models.PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties + or JSON or IO[bytes] + :return: BatchDeployment. The BatchDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.BatchDeployment] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_update_batch_deployment_request( + endpoint_name=endpoint_name, + deployment_name=deployment_name, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.BatchDeployment, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def create_or_update_batch_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: _models.BatchDeployment, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.BatchEndpoint: + """Create or Update a Batch Deployment. + + :param endpoint_name: Endpoint name. Required. + :type endpoint_name: str + :param deployment_name: The identifier for the Batch inference deployment. Required. + :type deployment_name: str + :param body: Batch inference deployment definition object. Required. + :type body: ~azure.ai.resources.autogen.models.BatchDeployment + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_or_update_batch_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: JSON, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.BatchEndpoint: + """Create or Update a Batch Deployment. + + :param endpoint_name: Endpoint name. Required. + :type endpoint_name: str + :param deployment_name: The identifier for the Batch inference deployment. Required. + :type deployment_name: str + :param body: Batch inference deployment definition object. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_or_update_batch_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: IO[bytes], + *, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.BatchEndpoint: + """Create or Update a Batch Deployment. + + :param endpoint_name: Endpoint name. Required. + :type endpoint_name: str + :param deployment_name: The identifier for the Batch inference deployment. Required. + :type deployment_name: str + :param body: Batch inference deployment definition object. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_or_update_batch_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: Union[_models.BatchDeployment, JSON, IO[bytes]], + **kwargs: Any + ) -> _models.BatchEndpoint: + """Create or Update a Batch Deployment. + + :param endpoint_name: Endpoint name. Required. + :type endpoint_name: str + :param deployment_name: The identifier for the Batch inference deployment. Required. + :type deployment_name: str + :param body: Batch inference deployment definition object. Is one of the following types: + BatchDeployment, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.BatchDeployment or JSON or IO[bytes] + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.BatchEndpoint] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_or_update_batch_deployment_request( + endpoint_name=endpoint_name, + deployment_name=deployment_name, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.BatchEndpoint, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def create_message( + self, + thread_id: str, + body: _models.ThreadMessageOptions, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.ThreadMessage: + """Creates a new message on a specified thread. + + :param thread_id: The ID of the thread to create the new message on. Required. + :type thread_id: str + :param body: A single message within an assistant thread, as provided during that thread's + creation for its initial state. Required. + :type body: ~azure.ai.resources.autogen.models.ThreadMessageOptions + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_message( + self, thread_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadMessage: + """Creates a new message on a specified thread. + + :param thread_id: The ID of the thread to create the new message on. Required. + :type thread_id: str + :param body: A single message within an assistant thread, as provided during that thread's + creation for its initial state. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_message( + self, thread_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadMessage: + """Creates a new message on a specified thread. + + :param thread_id: The ID of the thread to create the new message on. Required. + :type thread_id: str + :param body: A single message within an assistant thread, as provided during that thread's + creation for its initial state. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_message( + self, thread_id: str, body: Union[_models.ThreadMessageOptions, JSON, IO[bytes]], **kwargs: Any + ) -> _models.ThreadMessage: + """Creates a new message on a specified thread. + + :param thread_id: The ID of the thread to create the new message on. Required. + :type thread_id: str + :param body: A single message within an assistant thread, as provided during that thread's + creation for its initial state. Is one of the following types: ThreadMessageOptions, JSON, + IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.ThreadMessageOptions or JSON or IO[bytes] + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadMessage] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_message_request( + thread_id=thread_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadMessage, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def list_messages( + self, + thread_id: str, + *, + run_id: Optional[str] = None, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIPageableListOfThreadMessage: + """Gets a list of messages that exist on a thread. + + :param thread_id: The ID of the thread to list messages from. Required. + :type thread_id: str + :keyword run_id: Filter messages by the run ID that generated them. Default value is None. + :paramtype run_id: str + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.resources.autogen.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfThreadMessage. The OpenAIPageableListOfThreadMessage is compatible + with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OpenAIPageableListOfThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfThreadMessage] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_list_messages_request( + thread_id=thread_id, + run_id=run_id, + limit=limit, + order=order, + after=after, + before=before, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfThreadMessage, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_message(self, thread_id: str, message_id: str, **kwargs: Any) -> _models.ThreadMessage: + """Gets an existing message from an existing thread. + + :param thread_id: The ID of the thread to retrieve the specified message from. Required. + :type thread_id: str + :param message_id: The ID of the message to retrieve from the specified thread. Required. + :type message_id: str + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ThreadMessage] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_message_request( + thread_id=thread_id, + message_id=message_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadMessage, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def update_message( + self, thread_id: str, message_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadMessage: + """Modifies an existing message on an existing thread. + + :param thread_id: The ID of the thread containing the specified message to modify. Required. + :type thread_id: str + :param message_id: The ID of the message to modify on the specified thread. Required. + :type message_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_message( + self, + thread_id: str, + message_id: str, + *, + content_type: str = "application/json", + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadMessage: + """Modifies an existing message on an existing thread. + + :param thread_id: The ID of the thread containing the specified message to modify. Required. + :type thread_id: str + :param message_id: The ID of the message to modify on the specified thread. Required. + :type message_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_message( + self, thread_id: str, message_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadMessage: + """Modifies an existing message on an existing thread. + + :param thread_id: The ID of the thread containing the specified message to modify. Required. + :type thread_id: str + :param message_id: The ID of the message to modify on the specified thread. Required. + :type message_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_message( + self, + thread_id: str, + message_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadMessage: + """Modifies an existing message on an existing thread. + + :param thread_id: The ID of the thread containing the specified message to modify. Required. + :type thread_id: str + :param message_id: The ID of the message to modify on the specified thread. Required. + :type message_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadMessage] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"metadata": metadata} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_update_message_request( + thread_id=thread_id, + message_id=message_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadMessage, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_run_step(self, thread_id: str, run_id: str, step_id: str, **kwargs: Any) -> _models.RunStep: + """Gets a single run step from a thread run. + + :param thread_id: The ID of the thread that was run. Required. + :type thread_id: str + :param run_id: The ID of the specific run to retrieve the step from. Required. + :type run_id: str + :param step_id: The ID of the step to retrieve information about. Required. + :type step_id: str + :return: RunStep. The RunStep is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.RunStep + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.RunStep] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_run_step_request( + thread_id=thread_id, + run_id=run_id, + step_id=step_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.RunStep, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def list_run_steps( + self, + thread_id: str, + run_id: str, + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIPageableListOfRunStep: + """Gets a list of run steps from a thread run. + + :param thread_id: The ID of the thread that was run. Required. + :type thread_id: str + :param run_id: The ID of the run to list steps from. Required. + :type run_id: str + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.resources.autogen.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfRunStep. The OpenAIPageableListOfRunStep is compatible with + MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OpenAIPageableListOfRunStep + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfRunStep] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_list_run_steps_request( + thread_id=thread_id, + run_id=run_id, + limit=limit, + order=order, + after=after, + before=before, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfRunStep, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def create_run( + self, thread_id: str, body: _models.CreateRunOptions, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new run for an assistant thread. + + :param thread_id: The ID of the thread to run. Required. + :type thread_id: str + :param body: The details used when creating a new run of an assistant thread. Required. + :type body: ~azure.ai.resources.autogen.models.CreateRunOptions + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_run( + self, thread_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new run for an assistant thread. + + :param thread_id: The ID of the thread to run. Required. + :type thread_id: str + :param body: The details used when creating a new run of an assistant thread. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_run( + self, thread_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new run for an assistant thread. + + :param thread_id: The ID of the thread to run. Required. + :type thread_id: str + :param body: The details used when creating a new run of an assistant thread. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_run( + self, thread_id: str, body: Union[_models.CreateRunOptions, JSON, IO[bytes]], **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new run for an assistant thread. + + :param thread_id: The ID of the thread to run. Required. + :type thread_id: str + :param body: The details used when creating a new run of an assistant thread. Is one of the + following types: CreateRunOptions, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.CreateRunOptions or JSON or IO[bytes] + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_run_request( + thread_id=thread_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def list_runs( + self, + thread_id: str, + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIPageableListOfThreadRun: + """Gets a list of runs for a specified thread. + + :param thread_id: The ID of the thread to list runs from. Required. + :type thread_id: str + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.resources.autogen.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfThreadRun. The OpenAIPageableListOfThreadRun is compatible with + MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OpenAIPageableListOfThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfThreadRun] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_list_runs_request( + thread_id=thread_id, + limit=limit, + order=order, + after=after, + before=before, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_run(self, thread_id: str, run_id: str, **kwargs: Any) -> _models.ThreadRun: + """Gets an existing run from an existing thread. + + :param thread_id: The ID of the thread to retrieve run information from. Required. + :type thread_id: str + :param run_id: The ID of the thread to retrieve information about. Required. + :type run_id: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_run_request( + thread_id=thread_id, + run_id=run_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def update_run( + self, thread_id: str, run_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Modifies an existing thread run. + + :param thread_id: The ID of the thread associated with the specified run. Required. + :type thread_id: str + :param run_id: The ID of the run to modify. Required. + :type run_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_run( + self, + thread_id: str, + run_id: str, + *, + content_type: str = "application/json", + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadRun: + """Modifies an existing thread run. + + :param thread_id: The ID of the thread associated with the specified run. Required. + :type thread_id: str + :param run_id: The ID of the run to modify. Required. + :type run_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_run( + self, thread_id: str, run_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Modifies an existing thread run. + + :param thread_id: The ID of the thread associated with the specified run. Required. + :type thread_id: str + :param run_id: The ID of the run to modify. Required. + :type run_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_run( + self, + thread_id: str, + run_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any + ) -> _models.ThreadRun: + """Modifies an existing thread run. + + :param thread_id: The ID of the thread associated with the specified run. Required. + :type thread_id: str + :param run_id: The ID of the run to modify. Required. + :type run_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"metadata": metadata} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_update_run_request( + thread_id=thread_id, + run_id=run_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def submit_tool_outputs_to_run( + self, thread_id: str, run_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Submits outputs from tools as requested by tool calls in a run. Runs that need submitted tool + outputs will have a status of 'requires_action' with a required_action.type of + 'submit_tool_outputs'. + + :param thread_id: The ID of the thread that was run. Required. + :type thread_id: str + :param run_id: The ID of the run that requires tool outputs. Required. + :type run_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def submit_tool_outputs_to_run( + self, + thread_id: str, + run_id: str, + *, + tool_outputs: List[_models.ToolOutput], + content_type: str = "application/json", + stream_parameter: Optional[bool] = None, + **kwargs: Any + ) -> _models.ThreadRun: + """Submits outputs from tools as requested by tool calls in a run. Runs that need submitted tool + outputs will have a status of 'requires_action' with a required_action.type of + 'submit_tool_outputs'. + + :param thread_id: The ID of the thread that was run. Required. + :type thread_id: str + :param run_id: The ID of the run that requires tool outputs. Required. + :type run_id: str + :keyword tool_outputs: A list of tools for which the outputs are being submitted. Required. + :paramtype tool_outputs: list[~azure.ai.resources.autogen.models.ToolOutput] + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword stream_parameter: If ``true``\\ , returns a stream of events that happen during the + Run as server-sent events, terminating when the Run enters a terminal state with a ``data: + [DONE]`` message. Default value is None. + :paramtype stream_parameter: bool + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def submit_tool_outputs_to_run( + self, thread_id: str, run_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Submits outputs from tools as requested by tool calls in a run. Runs that need submitted tool + outputs will have a status of 'requires_action' with a required_action.type of + 'submit_tool_outputs'. + + :param thread_id: The ID of the thread that was run. Required. + :type thread_id: str + :param run_id: The ID of the run that requires tool outputs. Required. + :type run_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def submit_tool_outputs_to_run( + self, + thread_id: str, + run_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + tool_outputs: List[_models.ToolOutput] = _Unset, + stream_parameter: Optional[bool] = None, + **kwargs: Any + ) -> _models.ThreadRun: + """Submits outputs from tools as requested by tool calls in a run. Runs that need submitted tool + outputs will have a status of 'requires_action' with a required_action.type of + 'submit_tool_outputs'. + + :param thread_id: The ID of the thread that was run. Required. + :type thread_id: str + :param run_id: The ID of the run that requires tool outputs. Required. + :type run_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword tool_outputs: A list of tools for which the outputs are being submitted. Required. + :paramtype tool_outputs: list[~azure.ai.resources.autogen.models.ToolOutput] + :keyword stream_parameter: If ``true``\\ , returns a stream of events that happen during the + Run as server-sent events, terminating when the Run enters a terminal state with a ``data: + [DONE]`` message. Default value is None. + :paramtype stream_parameter: bool + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + if body is _Unset: + if tool_outputs is _Unset: + raise TypeError("missing required argument: tool_outputs") + body = {"stream": stream_parameter, "tool_outputs": tool_outputs} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_submit_tool_outputs_to_run_request( + thread_id=thread_id, + run_id=run_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def cancel_run(self, thread_id: str, run_id: str, **kwargs: Any) -> _models.ThreadRun: + """Cancels a run of an in progress thread. + + :param thread_id: The ID of the thread being run. Required. + :type thread_id: str + :param run_id: The ID of the run to cancel. Required. + :type run_id: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_cancel_run_request( + thread_id=thread_id, + run_id=run_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def create_thread_and_run( + self, body: _models.CreateAndRunThreadOptions, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new assistant thread and immediately starts a run using that new thread. + + :param body: The details used when creating and immediately running a new assistant thread. + Required. + :type body: ~azure.ai.resources.autogen.models.CreateAndRunThreadOptions + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_thread_and_run( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new assistant thread and immediately starts a run using that new thread. + + :param body: The details used when creating and immediately running a new assistant thread. + Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_thread_and_run( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new assistant thread and immediately starts a run using that new thread. + + :param body: The details used when creating and immediately running a new assistant thread. + Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_thread_and_run( + self, body: Union[_models.CreateAndRunThreadOptions, JSON, IO[bytes]], **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new assistant thread and immediately starts a run using that new thread. + + :param body: The details used when creating and immediately running a new assistant thread. Is + one of the following types: CreateAndRunThreadOptions, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.CreateAndRunThreadOptions or JSON or IO[bytes] + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_thread_and_run_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def create_thread( + self, body: _models.AssistantThreadCreationOptions, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.AssistantThread: + """Creates a new thread. Threads contain messages and can be run by assistants. + + :param body: The details used to create a new assistant thread. Required. + :type body: ~azure.ai.resources.autogen.models.AssistantThreadCreationOptions + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_thread( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.AssistantThread: + """Creates a new thread. Threads contain messages and can be run by assistants. + + :param body: The details used to create a new assistant thread. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_thread( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.AssistantThread: + """Creates a new thread. Threads contain messages and can be run by assistants. + + :param body: The details used to create a new assistant thread. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_thread( + self, body: Union[_models.AssistantThreadCreationOptions, JSON, IO[bytes]], **kwargs: Any + ) -> _models.AssistantThread: + """Creates a new thread. Threads contain messages and can be run by assistants. + + :param body: The details used to create a new assistant thread. Is one of the following types: + AssistantThreadCreationOptions, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.AssistantThreadCreationOptions or JSON or + IO[bytes] + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.AssistantThread] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_thread_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AssistantThread, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_thread(self, thread_id: str, **kwargs: Any) -> _models.AssistantThread: + """Gets information about an existing thread. + + :param thread_id: The ID of the thread to retrieve information about. Required. + :type thread_id: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.AssistantThread] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_thread_request( + thread_id=thread_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AssistantThread, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def update_thread( + self, + thread_id: str, + body: _models.UpdateAssistantThreadOptions, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.AssistantThread: + """Modifies an existing thread. + + :param thread_id: The ID of the thread to modify. Required. + :type thread_id: str + :param body: The details used to update an existing assistant thread. Required. + :type body: ~azure.ai.resources.autogen.models.UpdateAssistantThreadOptions + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_thread( + self, thread_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.AssistantThread: + """Modifies an existing thread. + + :param thread_id: The ID of the thread to modify. Required. + :type thread_id: str + :param body: The details used to update an existing assistant thread. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_thread( + self, thread_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.AssistantThread: + """Modifies an existing thread. + + :param thread_id: The ID of the thread to modify. Required. + :type thread_id: str + :param body: The details used to update an existing assistant thread. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_thread( + self, thread_id: str, body: Union[_models.UpdateAssistantThreadOptions, JSON, IO[bytes]], **kwargs: Any + ) -> _models.AssistantThread: + """Modifies an existing thread. + + :param thread_id: The ID of the thread to modify. Required. + :type thread_id: str + :param body: The details used to update an existing assistant thread. Is one of the following + types: UpdateAssistantThreadOptions, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.UpdateAssistantThreadOptions or JSON or + IO[bytes] + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.AssistantThread] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_update_thread_request( + thread_id=thread_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AssistantThread, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def delete_thread(self, thread_id: str, **kwargs: Any) -> _models.ThreadDeletionStatus: + """Deletes an existing thread. + + :param thread_id: The ID of the thread to delete. Required. + :type thread_id: str + :return: ThreadDeletionStatus. The ThreadDeletionStatus is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadDeletionStatus + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ThreadDeletionStatus] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_delete_thread_request( + thread_id=thread_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadDeletionStatus, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def list_vector_stores( + self, + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIPageableListOfVectorStore: + """Returns a list of vector stores. + + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.resources.autogen.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfVectorStore. The OpenAIPageableListOfVectorStore is compatible + with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OpenAIPageableListOfVectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfVectorStore] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_list_vector_stores_request( + limit=limit, + order=order, + after=after, + before=before, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfVectorStore, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def create_vector_store( + self, body: _models.VectorStoreOptions, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStore: + """Creates a vector store. + + :param body: Request object for creating a vector store. Required. + :type body: ~azure.ai.resources.autogen.models.VectorStoreOptions + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_vector_store( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStore: + """Creates a vector store. + + :param body: Request object for creating a vector store. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_vector_store( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStore: + """Creates a vector store. + + :param body: Request object for creating a vector store. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_vector_store( + self, body: Union[_models.VectorStoreOptions, JSON, IO[bytes]], **kwargs: Any + ) -> _models.VectorStore: + """Creates a vector store. + + :param body: Request object for creating a vector store. Is one of the following types: + VectorStoreOptions, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.VectorStoreOptions or JSON or IO[bytes] + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.VectorStore] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_vector_store_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStore, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_vector_store(self, vector_store_id: str, **kwargs: Any) -> _models.VectorStore: + """Returns the vector store object matching the specified ID. + + :param vector_store_id: The ID of the vector store to retrieve. Required. + :type vector_store_id: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStore] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_vector_store_request( + vector_store_id=vector_store_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStore, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def modify_vector_store( + self, + vector_store_id: str, + body: _models.VectorStoreUpdateOptions, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.VectorStore: + """The ID of the vector store to modify. + + :param vector_store_id: The ID of the vector store to modify. Required. + :type vector_store_id: str + :param body: Request object for updating a vector store. Required. + :type body: ~azure.ai.resources.autogen.models.VectorStoreUpdateOptions + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def modify_vector_store( + self, vector_store_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStore: + """The ID of the vector store to modify. + + :param vector_store_id: The ID of the vector store to modify. Required. + :type vector_store_id: str + :param body: Request object for updating a vector store. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def modify_vector_store( + self, vector_store_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStore: + """The ID of the vector store to modify. + + :param vector_store_id: The ID of the vector store to modify. Required. + :type vector_store_id: str + :param body: Request object for updating a vector store. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def modify_vector_store( + self, vector_store_id: str, body: Union[_models.VectorStoreUpdateOptions, JSON, IO[bytes]], **kwargs: Any + ) -> _models.VectorStore: + """The ID of the vector store to modify. + + :param vector_store_id: The ID of the vector store to modify. Required. + :type vector_store_id: str + :param body: Request object for updating a vector store. Is one of the following types: + VectorStoreUpdateOptions, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.VectorStoreUpdateOptions or JSON or IO[bytes] + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.VectorStore] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_modify_vector_store_request( + vector_store_id=vector_store_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStore, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def delete_vector_store(self, vector_store_id: str, **kwargs: Any) -> _models.VectorStoreDeletionStatus: + """Deletes the vector store object matching the specified ID. + + :param vector_store_id: The ID of the vector store to delete. Required. + :type vector_store_id: str + :return: VectorStoreDeletionStatus. The VectorStoreDeletionStatus is compatible with + MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreDeletionStatus + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStoreDeletionStatus] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_delete_vector_store_request( + vector_store_id=vector_store_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreDeletionStatus, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def list_vector_store_files( + self, + vector_store_id: str, + *, + filter: Optional[Union[str, _models.VectorStoreFileStatusFilter]] = None, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIPageableListOfVectorStoreFile: + """Returns a list of vector store files. + + :param vector_store_id: The ID of the vector store that the files belong to. Required. + :type vector_store_id: str + :keyword filter: Filter by file status. Known values are: "in_progress", "completed", "failed", + and "cancelled". Default value is None. + :paramtype filter: str or ~azure.ai.resources.autogen.models.VectorStoreFileStatusFilter + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.resources.autogen.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfVectorStoreFile. The OpenAIPageableListOfVectorStoreFile is + compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OpenAIPageableListOfVectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfVectorStoreFile] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_list_vector_store_files_request( + vector_store_id=vector_store_id, + filter=filter, + limit=limit, + order=order, + after=after, + before=before, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfVectorStoreFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def create_vector_store_file( + self, vector_store_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStoreFile: + """Create a vector store file by attaching a file to a vector store. + + :param vector_store_id: The ID of the vector store for which to create a File. Required. + :type vector_store_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_vector_store_file( + self, + vector_store_id: str, + *, + file_id: str, + content_type: str = "application/json", + chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, + **kwargs: Any + ) -> _models.VectorStoreFile: + """Create a vector store file by attaching a file to a vector store. + + :param vector_store_id: The ID of the vector store for which to create a File. Required. + :type vector_store_id: str + :keyword file_id: A File ID that the vector store should use. Useful for tools like + ``file_search`` that can access files. Required. + :paramtype file_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will + use the auto strategy. Default value is None. + :paramtype chunking_strategy: + ~azure.ai.resources.autogen.models.VectorStoreChunkingStrategyRequest + :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_vector_store_file( + self, vector_store_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStoreFile: + """Create a vector store file by attaching a file to a vector store. + + :param vector_store_id: The ID of the vector store for which to create a File. Required. + :type vector_store_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_vector_store_file( + self, + vector_store_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + file_id: str = _Unset, + chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, + **kwargs: Any + ) -> _models.VectorStoreFile: + """Create a vector store file by attaching a file to a vector store. + + :param vector_store_id: The ID of the vector store for which to create a File. Required. + :type vector_store_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword file_id: A File ID that the vector store should use. Useful for tools like + ``file_search`` that can access files. Required. + :paramtype file_id: str + :keyword chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will + use the auto strategy. Default value is None. + :paramtype chunking_strategy: + ~azure.ai.resources.autogen.models.VectorStoreChunkingStrategyRequest + :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.VectorStoreFile] = kwargs.pop("cls", None) + + if body is _Unset: + if file_id is _Unset: + raise TypeError("missing required argument: file_id") + body = {"chunking_strategy": chunking_strategy, "file_id": file_id} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_vector_store_file_request( + vector_store_id=vector_store_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_vector_store_file(self, vector_store_id: str, file_id: str, **kwargs: Any) -> _models.VectorStoreFile: + """Retrieves a vector store file. + + :param vector_store_id: The ID of the vector store that the file belongs to. Required. + :type vector_store_id: str + :param file_id: The ID of the file being retrieved. Required. + :type file_id: str + :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStoreFile] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_vector_store_file_request( + vector_store_id=vector_store_id, + file_id=file_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def delete_vector_store_file( + self, vector_store_id: str, file_id: str, **kwargs: Any + ) -> _models.VectorStoreFileDeletionStatus: + """Delete a vector store file. This will remove the file from the vector store but the file itself + will not be deleted. To delete the file, use the delete file endpoint. + + :param vector_store_id: The ID of the vector store that the file belongs to. Required. + :type vector_store_id: str + :param file_id: The ID of the file to delete its relationship to the vector store. Required. + :type file_id: str + :return: VectorStoreFileDeletionStatus. The VectorStoreFileDeletionStatus is compatible with + MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFileDeletionStatus + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStoreFileDeletionStatus] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_delete_vector_store_file_request( + vector_store_id=vector_store_id, + file_id=file_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFileDeletionStatus, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def create_vector_store_file_batch( + self, vector_store_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStoreFileBatch: + """Create a vector store file batch. + + :param vector_store_id: The ID of the vector store for which to create a File Batch. Required. + :type vector_store_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_vector_store_file_batch( + self, + vector_store_id: str, + *, + file_ids: List[str], + content_type: str = "application/json", + chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, + **kwargs: Any + ) -> _models.VectorStoreFileBatch: + """Create a vector store file batch. + + :param vector_store_id: The ID of the vector store for which to create a File Batch. Required. + :type vector_store_id: str + :keyword file_ids: A list of File IDs that the vector store should use. Useful for tools like + ``file_search`` that can access files. Required. + :paramtype file_ids: list[str] + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will + use the auto strategy. Default value is None. + :paramtype chunking_strategy: + ~azure.ai.resources.autogen.models.VectorStoreChunkingStrategyRequest + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_vector_store_file_batch( + self, vector_store_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStoreFileBatch: + """Create a vector store file batch. + + :param vector_store_id: The ID of the vector store for which to create a File Batch. Required. + :type vector_store_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_vector_store_file_batch( + self, + vector_store_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + file_ids: List[str] = _Unset, + chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, + **kwargs: Any + ) -> _models.VectorStoreFileBatch: + """Create a vector store file batch. + + :param vector_store_id: The ID of the vector store for which to create a File Batch. Required. + :type vector_store_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword file_ids: A list of File IDs that the vector store should use. Useful for tools like + ``file_search`` that can access files. Required. + :paramtype file_ids: list[str] + :keyword chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will + use the auto strategy. Default value is None. + :paramtype chunking_strategy: + ~azure.ai.resources.autogen.models.VectorStoreChunkingStrategyRequest + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.VectorStoreFileBatch] = kwargs.pop("cls", None) + + if body is _Unset: + if file_ids is _Unset: + raise TypeError("missing required argument: file_ids") + body = {"chunking_strategy": chunking_strategy, "file_ids": file_ids} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_vector_store_file_batch_request( + vector_store_id=vector_store_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFileBatch, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_vector_store_file_batch( + self, vector_store_id: str, batch_id: str, **kwargs: Any + ) -> _models.VectorStoreFileBatch: + """Retrieve a vector store file batch. + + :param vector_store_id: The ID of the vector store that the file batch belongs to. Required. + :type vector_store_id: str + :param batch_id: The ID of the file batch being retrieved. Required. + :type batch_id: str + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStoreFileBatch] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_vector_store_file_batch_request( + vector_store_id=vector_store_id, + batch_id=batch_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFileBatch, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def cancel_vector_store_file_batch( + self, vector_store_id: str, batch_id: str, **kwargs: Any + ) -> _models.VectorStoreFileBatch: + """Cancel a vector store file batch. This attempts to cancel the processing of files in this batch + as soon as possible. + + :param vector_store_id: The ID of the vector store that the file batch belongs to. Required. + :type vector_store_id: str + :param batch_id: The ID of the file batch to cancel. Required. + :type batch_id: str + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStoreFileBatch] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_cancel_vector_store_file_batch_request( + vector_store_id=vector_store_id, + batch_id=batch_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFileBatch, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def list_vector_store_file_batch_files( + self, + vector_store_id: str, + batch_id: str, + *, + filter: Optional[Union[str, _models.VectorStoreFileStatusFilter]] = None, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any + ) -> _models.OpenAIPageableListOfVectorStoreFile: + """Returns a list of vector store files in a batch. + + :param vector_store_id: The ID of the vector store that the file batch belongs to. Required. + :type vector_store_id: str + :param batch_id: The ID of the file batch that the files belong to. Required. + :type batch_id: str + :keyword filter: Filter by file status. Known values are: "in_progress", "completed", "failed", + and "cancelled". Default value is None. + :paramtype filter: str or ~azure.ai.resources.autogen.models.VectorStoreFileStatusFilter + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.resources.autogen.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfVectorStoreFile. The OpenAIPageableListOfVectorStoreFile is + compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OpenAIPageableListOfVectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfVectorStoreFile] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_list_vector_store_file_batch_files_request( + vector_store_id=vector_store_id, + batch_id=batch_id, + filter=filter, + limit=limit, + order=order, + after=after, + before=before, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfVectorStoreFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/operations/_patch.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/operations/_patch.py new file mode 100644 index 000000000000..f7dd32510333 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/aio/operations/_patch.py @@ -0,0 +1,20 @@ +# ------------------------------------ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. +# ------------------------------------ +"""Customize generated code here. + +Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize +""" +from typing import List + +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level + + +def patch_sdk(): + """Do not remove from this file. + + `patch_sdk` is a last resort escape hatch that allows you to do customizations + you can't accomplish using the techniques described in + https://aka.ms/azsdk/python/dpcodegen/python/customize + """ diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/models/__init__.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/models/__init__.py new file mode 100644 index 000000000000..84588eec4dfe --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/models/__init__.py @@ -0,0 +1,442 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +# pylint: disable=wrong-import-position + +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from ._patch import * # pylint: disable=unused-wildcard-import + + +from ._models import ( # type: ignore + AadCredential, + ApiKeyCredential, + AppInsightsConfiguration, + AssetBase, + AssetContainer, + AssetReferenceBase, + Assistant, + AssistantCreationOptions, + AssistantDeletionStatus, + AssistantThread, + AssistantThreadCreationOptions, + AssistantsApiResponseFormat, + AssistantsNamedToolChoice, + BaseCredential, + BaseModel, + BatchDeployment, + BatchDeploymentConfiguration, + BatchEndpoint, + BatchEndpointDefaults, + BatchRetrySettings, + CodeConfiguration, + CodeInterpreterToolDefinition, + CodeInterpreterToolResource, + Collection, + Connection, + CreateAndRunThreadOptions, + CreateCodeInterpreterToolResourceOptions, + CreateFileSearchToolResourceVectorStoreOptions, + CreateRunOptions, + CreateToolResourcesOptions, + DataCollector, + DataContainer, + DataPathAssetReference, + DataVersionBase, + Dataset, + DeploymentLogs, + DeploymentLogsRequest, + DeploymentResourceConfiguration, + DestinationAsset, + EndpointAuthKeys, + EndpointAuthToken, + EndpointBase, + EndpointDeploymentBase, + Evaluation, + EvaluationTarget, + EvaluatorConfiguration, + FileDeletionStatus, + FileListResponse, + FileSearchToolDefinition, + FileSearchToolDefinitionDetails, + FileSearchToolResource, + FlavorData, + FunctionDefinition, + FunctionName, + FunctionToolDefinition, + IdAssetReference, + Index, + InputData, + MessageAttachment, + MessageContent, + MessageImageFileContent, + MessageImageFileDetails, + MessageIncompleteDetails, + MessageTextAnnotation, + MessageTextContent, + MessageTextDetails, + MessageTextFileCitationAnnotation, + MessageTextFileCitationDetails, + MessageTextFilePathAnnotation, + MessageTextFilePathDetails, + ModelContainer, + ModelVersion, + OnlineDeployment, + OnlineEndpoint, + OnlineRequestSettings, + OnlineScaleSettings, + OpenAIFile, + OpenAIPageableListOfAssistant, + OpenAIPageableListOfRunStep, + OpenAIPageableListOfThreadMessage, + OpenAIPageableListOfThreadRun, + OpenAIPageableListOfVectorStore, + OpenAIPageableListOfVectorStoreFile, + OutputPathAssetReference, + PartialBatchDeployment, + PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties, + PartialManagedServiceIdentity, + PartialMinimalTrackedResource, + PartialMinimalTrackedResourceWithIdentity, + PartialMinimalTrackedResourceWithSku, + PartialSku, + ProbeSettings, + RegenerateEndpointKeysRequest, + RequestLogging, + RequiredAction, + RequiredFunctionToolCall, + RequiredFunctionToolCallDetails, + RequiredToolCall, + ResourceBase, + ResourceConfiguration, + RunCompletionUsage, + RunError, + RunStep, + RunStepCodeInterpreterImageOutput, + RunStepCodeInterpreterImageReference, + RunStepCodeInterpreterLogOutput, + RunStepCodeInterpreterToolCall, + RunStepCodeInterpreterToolCallDetails, + RunStepCodeInterpreterToolCallOutput, + RunStepCompletionUsage, + RunStepDetails, + RunStepError, + RunStepFileSearchToolCall, + RunStepFunctionToolCall, + RunStepFunctionToolCallDetails, + RunStepMessageCreationDetails, + RunStepMessageCreationReference, + RunStepToolCall, + RunStepToolCallDetails, + SasCredential, + SkuCapacity, + SkuResource, + SkuSetting, + SubmitToolOutputsAction, + SubmitToolOutputsDetails, + SystemData, + ThreadDeletionStatus, + ThreadMessage, + ThreadMessageOptions, + ThreadRun, + ToolDefinition, + ToolOutput, + ToolResources, + TruncationObject, + UpdateAssistantOptions, + UpdateAssistantThreadOptions, + UpdateCodeInterpreterToolResourceOptions, + UpdateEvaluationRequest, + UpdateFileSearchToolResourceOptions, + UpdateToolResourcesOptions, + UriFileDataVersion, + UriFolderDataVersion, + VectorStore, + VectorStoreAutoChunkingStrategyRequest, + VectorStoreAutoChunkingStrategyResponse, + VectorStoreChunkingStrategyRequest, + VectorStoreChunkingStrategyResponse, + VectorStoreDeletionStatus, + VectorStoreExpirationPolicy, + VectorStoreFile, + VectorStoreFileBatch, + VectorStoreFileCount, + VectorStoreFileDeletionStatus, + VectorStoreFileError, + VectorStoreOptions, + VectorStoreStaticChunkingStrategyOptions, + VectorStoreStaticChunkingStrategyRequest, + VectorStoreStaticChunkingStrategyResponse, + VectorStoreUpdateOptions, + VersionInfo, +) + +from ._enums import ( # type: ignore + ApiResponseFormat, + AssetProvisioningState, + AssistantsApiResponseFormatMode, + AssistantsApiToolChoiceOptionMode, + AssistantsNamedToolChoiceType, + BatchDeploymentConfigurationType, + BatchLoggingLevel, + BatchOutputAction, + ContainerType, + CredentialType, + DataCollectionMode, + DataType, + DeploymentProvisioningState, + EgressPublicNetworkAccessType, + EndpointAuthMode, + EndpointComputeType, + EndpointProvisioningState, + FilePurpose, + FileState, + IncompleteRunDetails, + KeyType, + ListSortOrder, + ListViewType, + ManagedServiceIdentityType, + MessageIncompleteDetailsReason, + MessageRole, + MessageStatus, + OrderString, + PublicNetworkAccessType, + ReferenceType, + RollingRateType, + RunStatus, + RunStepErrorCode, + RunStepStatus, + RunStepType, + ScaleType, + SkuScaleType, + SkuTier, + TruncationStrategy, + VectorStoreChunkingStrategyRequestType, + VectorStoreChunkingStrategyResponseType, + VectorStoreExpirationPolicyAnchor, + VectorStoreFileBatchStatus, + VectorStoreFileErrorCode, + VectorStoreFileStatus, + VectorStoreFileStatusFilter, + VectorStoreStatus, +) +from ._patch import __all__ as _patch_all +from ._patch import * +from ._patch import patch_sdk as _patch_sdk + +__all__ = [ + "AadCredential", + "ApiKeyCredential", + "AppInsightsConfiguration", + "AssetBase", + "AssetContainer", + "AssetReferenceBase", + "Assistant", + "AssistantCreationOptions", + "AssistantDeletionStatus", + "AssistantThread", + "AssistantThreadCreationOptions", + "AssistantsApiResponseFormat", + "AssistantsNamedToolChoice", + "BaseCredential", + "BaseModel", + "BatchDeployment", + "BatchDeploymentConfiguration", + "BatchEndpoint", + "BatchEndpointDefaults", + "BatchRetrySettings", + "CodeConfiguration", + "CodeInterpreterToolDefinition", + "CodeInterpreterToolResource", + "Collection", + "Connection", + "CreateAndRunThreadOptions", + "CreateCodeInterpreterToolResourceOptions", + "CreateFileSearchToolResourceVectorStoreOptions", + "CreateRunOptions", + "CreateToolResourcesOptions", + "DataCollector", + "DataContainer", + "DataPathAssetReference", + "DataVersionBase", + "Dataset", + "DeploymentLogs", + "DeploymentLogsRequest", + "DeploymentResourceConfiguration", + "DestinationAsset", + "EndpointAuthKeys", + "EndpointAuthToken", + "EndpointBase", + "EndpointDeploymentBase", + "Evaluation", + "EvaluationTarget", + "EvaluatorConfiguration", + "FileDeletionStatus", + "FileListResponse", + "FileSearchToolDefinition", + "FileSearchToolDefinitionDetails", + "FileSearchToolResource", + "FlavorData", + "FunctionDefinition", + "FunctionName", + "FunctionToolDefinition", + "IdAssetReference", + "Index", + "InputData", + "MessageAttachment", + "MessageContent", + "MessageImageFileContent", + "MessageImageFileDetails", + "MessageIncompleteDetails", + "MessageTextAnnotation", + "MessageTextContent", + "MessageTextDetails", + "MessageTextFileCitationAnnotation", + "MessageTextFileCitationDetails", + "MessageTextFilePathAnnotation", + "MessageTextFilePathDetails", + "ModelContainer", + "ModelVersion", + "OnlineDeployment", + "OnlineEndpoint", + "OnlineRequestSettings", + "OnlineScaleSettings", + "OpenAIFile", + "OpenAIPageableListOfAssistant", + "OpenAIPageableListOfRunStep", + "OpenAIPageableListOfThreadMessage", + "OpenAIPageableListOfThreadRun", + "OpenAIPageableListOfVectorStore", + "OpenAIPageableListOfVectorStoreFile", + "OutputPathAssetReference", + "PartialBatchDeployment", + "PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties", + "PartialManagedServiceIdentity", + "PartialMinimalTrackedResource", + "PartialMinimalTrackedResourceWithIdentity", + "PartialMinimalTrackedResourceWithSku", + "PartialSku", + "ProbeSettings", + "RegenerateEndpointKeysRequest", + "RequestLogging", + "RequiredAction", + "RequiredFunctionToolCall", + "RequiredFunctionToolCallDetails", + "RequiredToolCall", + "ResourceBase", + "ResourceConfiguration", + "RunCompletionUsage", + "RunError", + "RunStep", + "RunStepCodeInterpreterImageOutput", + "RunStepCodeInterpreterImageReference", + "RunStepCodeInterpreterLogOutput", + "RunStepCodeInterpreterToolCall", + "RunStepCodeInterpreterToolCallDetails", + "RunStepCodeInterpreterToolCallOutput", + "RunStepCompletionUsage", + "RunStepDetails", + "RunStepError", + "RunStepFileSearchToolCall", + "RunStepFunctionToolCall", + "RunStepFunctionToolCallDetails", + "RunStepMessageCreationDetails", + "RunStepMessageCreationReference", + "RunStepToolCall", + "RunStepToolCallDetails", + "SasCredential", + "SkuCapacity", + "SkuResource", + "SkuSetting", + "SubmitToolOutputsAction", + "SubmitToolOutputsDetails", + "SystemData", + "ThreadDeletionStatus", + "ThreadMessage", + "ThreadMessageOptions", + "ThreadRun", + "ToolDefinition", + "ToolOutput", + "ToolResources", + "TruncationObject", + "UpdateAssistantOptions", + "UpdateAssistantThreadOptions", + "UpdateCodeInterpreterToolResourceOptions", + "UpdateEvaluationRequest", + "UpdateFileSearchToolResourceOptions", + "UpdateToolResourcesOptions", + "UriFileDataVersion", + "UriFolderDataVersion", + "VectorStore", + "VectorStoreAutoChunkingStrategyRequest", + "VectorStoreAutoChunkingStrategyResponse", + "VectorStoreChunkingStrategyRequest", + "VectorStoreChunkingStrategyResponse", + "VectorStoreDeletionStatus", + "VectorStoreExpirationPolicy", + "VectorStoreFile", + "VectorStoreFileBatch", + "VectorStoreFileCount", + "VectorStoreFileDeletionStatus", + "VectorStoreFileError", + "VectorStoreOptions", + "VectorStoreStaticChunkingStrategyOptions", + "VectorStoreStaticChunkingStrategyRequest", + "VectorStoreStaticChunkingStrategyResponse", + "VectorStoreUpdateOptions", + "VersionInfo", + "ApiResponseFormat", + "AssetProvisioningState", + "AssistantsApiResponseFormatMode", + "AssistantsApiToolChoiceOptionMode", + "AssistantsNamedToolChoiceType", + "BatchDeploymentConfigurationType", + "BatchLoggingLevel", + "BatchOutputAction", + "ContainerType", + "CredentialType", + "DataCollectionMode", + "DataType", + "DeploymentProvisioningState", + "EgressPublicNetworkAccessType", + "EndpointAuthMode", + "EndpointComputeType", + "EndpointProvisioningState", + "FilePurpose", + "FileState", + "IncompleteRunDetails", + "KeyType", + "ListSortOrder", + "ListViewType", + "ManagedServiceIdentityType", + "MessageIncompleteDetailsReason", + "MessageRole", + "MessageStatus", + "OrderString", + "PublicNetworkAccessType", + "ReferenceType", + "RollingRateType", + "RunStatus", + "RunStepErrorCode", + "RunStepStatus", + "RunStepType", + "ScaleType", + "SkuScaleType", + "SkuTier", + "TruncationStrategy", + "VectorStoreChunkingStrategyRequestType", + "VectorStoreChunkingStrategyResponseType", + "VectorStoreExpirationPolicyAnchor", + "VectorStoreFileBatchStatus", + "VectorStoreFileErrorCode", + "VectorStoreFileStatus", + "VectorStoreFileStatusFilter", + "VectorStoreStatus", +] +__all__.extend([p for p in _patch_all if p not in __all__]) # pyright: ignore +_patch_sdk() diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/models/_enums.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/models/_enums.py new file mode 100644 index 000000000000..efd8fb640137 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/models/_enums.py @@ -0,0 +1,565 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from enum import Enum +from azure.core import CaseInsensitiveEnumMeta + + +class ApiResponseFormat(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Possible API response formats.""" + + TEXT = "text" + """``text`` format should be used for requests involving any sort of ToolCall.""" + JSON_OBJECT = "json_object" + """Using ``json_object`` format will limit the usage of ToolCall to only functions.""" + + +class AssetProvisioningState(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Asset Provisioning State Definition.""" + + SUCCEEDED = "Succeeded" + """The operation succeeded.""" + FAILED = "Failed" + """The operation failed.""" + CANCELED = "Canceled" + """The operation was canceled.""" + CREATING = "Creating" + """The operation is creating.""" + UPDATING = "Updating" + """The operation is updating.""" + DELETING = "Deleting" + """The operation is deleting.""" + + +class AssistantsApiResponseFormatMode(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Represents the mode in which the model will handle the return format of a tool call.""" + + AUTO = "auto" + """Default value. Let the model handle the return format.""" + NONE = "none" + """Setting the value to ``none``\\ , will result in a 400 Bad request.""" + + +class AssistantsApiToolChoiceOptionMode(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Specifies how the tool choice will be used.""" + + NONE = "none" + """The model will not call a function and instead generates a message.""" + AUTO = "auto" + """The model can pick between generating a message or calling a function.""" + + +class AssistantsNamedToolChoiceType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Available tool types for assistants named tools.""" + + FUNCTION = "function" + """Tool type ``function``""" + CODE_INTERPRETER = "code_interpreter" + """Tool type ``code_interpreter``""" + FILE_SEARCH = "file_search" + """Tool type ``file_search``""" + + +class BatchDeploymentConfigurationType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The enumerated property types for batch deployments.""" + + MODEL = "Model" + """Model""" + PIPELINE_COMPONENT = "PipelineComponent" + """PipelineComponent""" + + +class BatchLoggingLevel(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Log verbosity for batch inferencing. Increasing verbosity order for logging is : Warning, Info + and Debug. The default value is Info. + """ + + INFO = "Info" + """Info level logging.""" + WARNING = "Warning" + """Warning level logging.""" + DEBUG = "Debug" + """Debug level logging.""" + + +class BatchOutputAction(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Enum to determine how batch inferencing will handle output.""" + + SUMMARY_ONLY = "SummaryOnly" + """SummaryOnly""" + APPEND_ROW = "AppendRow" + """AppendRow""" + + +class ContainerType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Container types.""" + + STORAGE_INITIALIZER = "StorageInitializer" + """StorageInitializer""" + INFERENCE_SERVER = "InferenceServer" + """InferenceServer""" + + +class CredentialType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The different Credential types.""" + + API_KEY = "ApiKey" + AAD = "AAD" + SAS = "SAS" + + +class DataCollectionMode(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Data collection mode.""" + + ENABLED = "Enabled" + """Enabled""" + DISABLED = "Disabled" + """Disabled""" + + +class DataType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Enum to determine the type of data.""" + + URI_FILE = "uri_file" + """URI file.""" + URI_FOLDER = "uri_folder" + """URI folder.""" + + +class DeploymentProvisioningState(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Possible values for DeploymentProvisioningState.""" + + CREATING = "Creating" + """The endpoint is being created.""" + DELETING = "Deleting" + """The endpoint is being deleted.""" + SCALING = "Scaling" + """The endpoint is being scaled.""" + UPDATING = "Updating" + """The endpoint is being updated.""" + SUCCEEDED = "Succeeded" + """The endpoint provisioning succeeded.""" + FAILED = "Failed" + """The endpoint provisioning failed.""" + CANCELED = "Canceled" + """The endpoint provisioning was canceled.""" + + +class EgressPublicNetworkAccessType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Enum to determine whether PublicNetworkAccess is Enabled or Disabled for egress of a + deployment. + """ + + ENABLED = "Enabled" + """PublicNetworkAccess is enabled for egress.""" + DISABLED = "Disabled" + """PublicNetworkAccess is disabled for egress.""" + + +class EndpointAuthMode(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Enum to determine endpoint authentication mode.""" + + AML_TOKEN = "AMLToken" + """AMLToken""" + KEY = "Key" + """Key""" + AAD_TOKEN = "AADToken" + """AADToken""" + + +class EndpointComputeType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Enum to determine endpoint compute type.""" + + MANAGED = "Managed" + """Managed""" + KUBERNETES = "Kubernetes" + """Kubernetes""" + AZURE_ML_COMPUTE = "AzureMLCompute" + """AzureMLCompute""" + + +class EndpointProvisioningState(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """State of endpoint provisioning.""" + + CREATING = "Creating" + """The endpoint is being created.""" + DELETING = "Deleting" + """The endpoint is being deleted.""" + SUCCEEDED = "Succeeded" + """The endpoint provisioning succeeded.""" + FAILED = "Failed" + """The endpoint provisioning failed.""" + UPDATING = "Updating" + """The endpoint is being updated.""" + CANCELED = "Canceled" + """The endpoint provisioning was canceled.""" + + +class FilePurpose(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The possible values denoting the intended usage of a file.""" + + FINE_TUNE = "fine-tune" + """Indicates a file is used for fine tuning input.""" + FINE_TUNE_RESULTS = "fine-tune-results" + """Indicates a file is used for fine tuning results.""" + ASSISTANTS = "assistants" + """Indicates a file is used as input to assistants.""" + ASSISTANTS_OUTPUT = "assistants_output" + """Indicates a file is used as output by assistants.""" + BATCH = "batch" + """Indicates a file is used as input to batch operations.""" + BATCH_OUTPUT = "batch_output" + """Indicates a file is used as output by a vector store batch operation.""" + VISION = "vision" + """Indicates a file is used as input to a vision operation.""" + + +class FileState(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The state of the file.""" + + UPLOADED = "uploaded" + """The file has been uploaded but it's not yet processed. This state is not returned by Azure + OpenAI and exposed only for compatibility. It can be categorized as an inactive state.""" + PENDING = "pending" + """The operation was created and is not queued to be processed in the future. It can be + categorized as an inactive state.""" + RUNNING = "running" + """The operation has started to be processed. It can be categorized as an active state.""" + PROCESSED = "processed" + """The operation has successfully processed and is ready for consumption. It can be categorized as + a terminal state.""" + ERROR = "error" + """The operation has completed processing with a failure and cannot be further consumed. It can be + categorized as a terminal state.""" + DELETING = "deleting" + """The entity is in the process to be deleted. This state is not returned by Azure OpenAI and + exposed only for compatibility. It can be categorized as an active state.""" + DELETED = "deleted" + """The entity has been deleted but may still be referenced by other entities predating the + deletion. It can be categorized as a terminal state.""" + + +class IncompleteRunDetails(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The reason why the run is incomplete. This will point to which specific token limit was reached + over the course of the run. + """ + + MAX_COMPLETION_TOKENS = "max_completion_tokens" + """Maximum completion tokens exceeded""" + MAX_PROMPT_TOKENS = "max_prompt_tokens" + """Maximum prompt tokens exceeded""" + + +class KeyType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Type of key (Primary or Secondary).""" + + PRIMARY = "Primary" + """Primary key.""" + SECONDARY = "Secondary" + """Secondary key.""" + + +class ListSortOrder(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The available sorting options when requesting a list of response objects.""" + + ASCENDING = "asc" + """Specifies an ascending sort order.""" + DESCENDING = "desc" + """Specifies a descending sort order.""" + + +class ListViewType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """List View Type Definition.""" + + ACTIVE_ONLY = "ActiveOnly" + """List only active items.""" + ARCHIVED_ONLY = "ArchivedOnly" + """List only archived items.""" + ALL = "All" + """List all items.""" + + +class ManagedServiceIdentityType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Type of managed service identity (where both SystemAssigned and UserAssigned types are + allowed). + """ + + NONE = "None" + """No managed service identity.""" + SYSTEM_ASSIGNED = "SystemAssigned" + """SystemAssigned identity""" + USER_ASSIGNED = "UserAssigned" + """UserAssigned identity.""" + SYSTEM_ASSIGNED_USER_ASSIGNED = "SystemAssigned,UserAssigned" + """SystemAssigned and UserAssigned identity.""" + + +class MessageIncompleteDetailsReason(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """A set of reasons describing why a message is marked as incomplete.""" + + CONTENT_FILTER = "content_filter" + """The run generating the message was terminated due to content filter flagging.""" + MAX_TOKENS = "max_tokens" + """The run generating the message exhausted available tokens before completion.""" + RUN_CANCELLED = "run_cancelled" + """The run generating the message was cancelled before completion.""" + RUN_FAILED = "run_failed" + """The run generating the message failed.""" + RUN_EXPIRED = "run_expired" + """The run generating the message expired.""" + + +class MessageRole(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The possible values for roles attributed to messages in a thread.""" + + USER = "user" + """The role representing the end-user.""" + ASSISTANT = "assistant" + """The role representing the assistant.""" + + +class MessageStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The possible execution status values for a thread message.""" + + IN_PROGRESS = "in_progress" + """A run is currently creating this message.""" + INCOMPLETE = "incomplete" + """This message is incomplete. See incomplete_details for more information.""" + COMPLETED = "completed" + """This message was successfully completed by a run.""" + + +class OrderString(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The type of ordering to use.""" + + CREATED_AT_DESC = "CreatedAtDesc" + """Sort creation date/time in descending order.""" + CREATED_AT_ASC = "CreatedAtAsc" + """Sort creation date/time in ascending order.""" + UPDATED_AT_DESC = "UpdatedAtDesc" + """Sort updated at date/time in descending order.""" + UPDATED_AT_ASC = "UpdatedAtAsc" + """Sort updated at date/time in ascending order.""" + + +class PublicNetworkAccessType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Enum to determine whether PublicNetworkAccess is Enabled or Disabled.""" + + ENABLED = "Enabled" + """PublicNetworkAccess is enabled.""" + DISABLED = "Disabled" + """PublicNetworkAccess is disabled.""" + + +class ReferenceType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Enum to determine which reference method to use for an asset.""" + + ID = "Id" + """Id""" + DATA_PATH = "DataPath" + """DataPath""" + OUTPUT_PATH = "OutputPath" + """OutputPath""" + + +class RollingRateType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Rolling rate type.""" + + YEAR = "Year" + """Year""" + MONTH = "Month" + """Month""" + DAY = "Day" + """Day""" + HOUR = "Hour" + """Hour""" + MINUTE = "Minute" + """Minute""" + + +class RunStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Possible values for the status of an assistant thread run.""" + + QUEUED = "queued" + """Represents a run that is queued to start.""" + IN_PROGRESS = "in_progress" + """Represents a run that is in progress.""" + REQUIRES_ACTION = "requires_action" + """Represents a run that needs another operation, such as tool output submission, to continue.""" + CANCELLING = "cancelling" + """Represents a run that is in the process of cancellation.""" + CANCELLED = "cancelled" + """Represents a run that has been cancelled.""" + FAILED = "failed" + """Represents a run that failed.""" + COMPLETED = "completed" + """Represents a run that successfully completed.""" + EXPIRED = "expired" + """Represents a run that expired before it could otherwise finish.""" + + +class RunStepErrorCode(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Possible error code values attributable to a failed run step.""" + + SERVER_ERROR = "server_error" + """Represents a server error.""" + RATE_LIMIT_EXCEEDED = "rate_limit_exceeded" + """Represents an error indicating configured rate limits were exceeded.""" + + +class RunStepStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Possible values for the status of a run step.""" + + IN_PROGRESS = "in_progress" + """Represents a run step still in progress.""" + CANCELLED = "cancelled" + """Represents a run step that was cancelled.""" + FAILED = "failed" + """Represents a run step that failed.""" + COMPLETED = "completed" + """Represents a run step that successfully completed.""" + EXPIRED = "expired" + """Represents a run step that expired before otherwise finishing.""" + + +class RunStepType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The possible types of run steps.""" + + MESSAGE_CREATION = "message_creation" + """Represents a run step to create a message.""" + TOOL_CALLS = "tool_calls" + """Represents a run step that calls tools.""" + + +class ScaleType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Scale type for online endpoints.""" + + DEFAULT = "Default" + """Default.""" + TARGET_UTILIZATION = "TargetUtilization" + """TargetUtilization.""" + + +class SkuScaleType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Node scaling setting for the compute sku.""" + + AUTOMATIC = "Automatic" + """Automatically scales node count.""" + MANUAL = "Manual" + """Node count scaled upon user request.""" + NONE = "None" + """Fixed set of nodes.""" + + +class SkuTier(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """This field is required to be implemented by the Resource Provider if the service has more than + one tier, but is not required on a PUT. + """ + + FREE = "Free" + """The Free service tier.""" + BASIC = "Basic" + """The Basic service tier.""" + STANDARD = "Standard" + """The Standard service tier.""" + PREMIUM = "Premium" + """The Premium service tier.""" + + +class TruncationStrategy(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Possible truncation strategies for the thread.""" + + AUTO = "auto" + """Default value. Messages in the middle of the thread will be dropped to fit the context length + of the model.""" + LAST_MESSAGES = "last_messages" + """The thread will truncate to the ``lastMessages`` count of recent messages.""" + + +class VectorStoreChunkingStrategyRequestType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Type of chunking strategy.""" + + AUTO = "auto" + STATIC = "static" + + +class VectorStoreChunkingStrategyResponseType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Type of chunking strategy.""" + + OTHER = "other" + STATIC = "static" + + +class VectorStoreExpirationPolicyAnchor(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Describes the relationship between the days and the expiration of this vector store.""" + + LAST_ACTIVE_AT = "last_active_at" + """The expiration policy is based on the last time the vector store was active.""" + + +class VectorStoreFileBatchStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The status of the vector store file batch.""" + + IN_PROGRESS = "in_progress" + """The vector store is still processing this file batch.""" + COMPLETED = "completed" + """The vector store file batch is ready for use.""" + CANCELLED = "cancelled" + """The vector store file batch was cancelled.""" + FAILED = "failed" + """The vector store file batch failed to process.""" + + +class VectorStoreFileErrorCode(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Error code variants for vector store file processing.""" + + INTERNAL_ERROR = "internal_error" + """An internal error occurred.""" + FILE_NOT_FOUND = "file_not_found" + """The file was not found.""" + PARSING_ERROR = "parsing_error" + """The file could not be parsed.""" + UNHANDLED_MIME_TYPE = "unhandled_mime_type" + """The file has an unhandled mime type.""" + + +class VectorStoreFileStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Vector store file status.""" + + IN_PROGRESS = "in_progress" + """The file is currently being processed.""" + COMPLETED = "completed" + """The file has been successfully processed.""" + FAILED = "failed" + """The file has failed to process.""" + CANCELLED = "cancelled" + """The file was cancelled.""" + + +class VectorStoreFileStatusFilter(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Query parameter filter for vector store file retrieval endpoint.""" + + IN_PROGRESS = "in_progress" + """Retrieve only files that are currently being processed""" + COMPLETED = "completed" + """Retrieve only files that have been successfully processed""" + FAILED = "failed" + """Retrieve only files that have failed to process""" + CANCELLED = "cancelled" + """Retrieve only files that were cancelled""" + + +class VectorStoreStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Vector store possible status.""" + + EXPIRED = "expired" + """expired status indicates that this vector store has expired and is no longer available for use.""" + IN_PROGRESS = "in_progress" + """in_progress status indicates that this vector store is still processing files.""" + COMPLETED = "completed" + """completed status indicates that this vector store is ready for use.""" diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/models/_models.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/models/_models.py new file mode 100644 index 000000000000..61cb14db0f84 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/models/_models.py @@ -0,0 +1,7445 @@ +# pylint: disable=too-many-lines +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +# pylint: disable=useless-super-delegation + +import datetime +from typing import Any, Dict, List, Literal, Mapping, Optional, TYPE_CHECKING, Union, overload + +from .. import _model_base +from .._model_base import rest_discriminator, rest_field +from ._enums import ( + CredentialType, + DataType, + ReferenceType, + RunStepType, + VectorStoreChunkingStrategyRequestType, + VectorStoreChunkingStrategyResponseType, +) + +if TYPE_CHECKING: + from .. import _types, models as _models + + +class BaseCredential(_model_base.Model): + """Base Credential definition. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + AadCredential, ApiKeyCredential, SasCredential + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar name: Credential name. Required. + :vartype name: str + :ivar type: Required. Known values are: "ApiKey", "AAD", and "SAS". + :vartype type: str or ~azure.ai.resources.autogen.models.CredentialType + """ + + __mapping__: Dict[str, _model_base.Model] = {} + name: str = rest_field(visibility=["read"]) + """Credential name. Required.""" + type: str = rest_discriminator(name="type") + """Required. Known values are: \"ApiKey\", \"AAD\", and \"SAS\".""" + + @overload + def __init__( + self, + *, + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AadCredential(BaseCredential, discriminator="AAD"): + """AAD Credential definition. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar name: Credential name. Required. + :vartype name: str + :ivar type: Required. + :vartype type: str or ~azure.ai.resources.autogen.models.AAD + """ + + type: Literal[CredentialType.AAD] = rest_discriminator(name="type") # type: ignore + """Required.""" + + @overload + def __init__( + self, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type=CredentialType.AAD, **kwargs) + + +class ApiKeyCredential(BaseCredential, discriminator="ApiKey"): + """ApiKey Credential definition. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar name: Credential name. Required. + :vartype name: str + :ivar api_key: API Key. Required. + :vartype api_key: str + :ivar type: Required. + :vartype type: str or ~azure.ai.resources.autogen.models.API_KEY + """ + + api_key: str = rest_field(name="apiKey", visibility=["read"]) + """API Key. Required.""" + type: Literal[CredentialType.API_KEY] = rest_discriminator(name="type") # type: ignore + """Required.""" + + @overload + def __init__( + self, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type=CredentialType.API_KEY, **kwargs) + + +class InputData(_model_base.Model): + """Abstract data class. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + AppInsightsConfiguration, Dataset + + + :ivar type: Discriminator property for InputData. Required. Default value is None. + :vartype type: str + :ivar id: Evaluation input data. Required. + :vartype id: str + """ + + __mapping__: Dict[str, _model_base.Model] = {} + type: str = rest_discriminator(name="type") + """Discriminator property for InputData. Required. Default value is None.""" + id: str = rest_field() + """Evaluation input data. Required.""" + + @overload + def __init__( + self, + *, + type: str, + id: str, # pylint: disable=redefined-builtin + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AppInsightsConfiguration(InputData, discriminator="app_insights"): + """Data Source for Application Insight. + + + :ivar id: Evaluation input data. Required. + :vartype id: str + :ivar type: Required. Default value is "app_insights". + :vartype type: str + :ivar connection_string: Application Insight connection string. Required. + :vartype connection_string: str + :ivar query: Query to fetch data. Required. + :vartype query: str + """ + + type: Literal["app_insights"] = rest_discriminator(name="type") # type: ignore + """Required. Default value is \"app_insights\".""" + connection_string: str = rest_field(name="connectionString") + """Application Insight connection string. Required.""" + query: str = rest_field() + """Query to fetch data. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + connection_string: str, + query: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="app_insights", **kwargs) + + +class ResourceBase(_model_base.Model): + """ResourceBase definition. + + :ivar description: The asset description text. + :vartype description: str + :ivar properties: The asset property dictionary. + :vartype properties: dict[str, str] + :ivar tags: Tag dictionary. Tags can be added, removed, and updated. + :vartype tags: dict[str, str] + """ + + description: Optional[str] = rest_field() + """The asset description text.""" + properties: Optional[Dict[str, str]] = rest_field() + """The asset property dictionary.""" + tags: Optional[Dict[str, str]] = rest_field() + """Tag dictionary. Tags can be added, removed, and updated.""" + + @overload + def __init__( + self, + *, + description: Optional[str] = None, + properties: Optional[Dict[str, str]] = None, + tags: Optional[Dict[str, str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AssetBase(ResourceBase): + """Base definition for an asset. + + :ivar description: The asset description text. + :vartype description: str + :ivar properties: The asset property dictionary. + :vartype properties: dict[str, str] + :ivar tags: Tag dictionary. Tags can be added, removed, and updated. + :vartype tags: dict[str, str] + :ivar is_anonymous: If the name version are system generated (anonymous registration). + :vartype is_anonymous: bool + :ivar is_archived: Is the asset archived?. + :vartype is_archived: bool + """ + + is_anonymous: Optional[bool] = rest_field(name="isAnonymous", visibility=["read", "create"]) + """If the name version are system generated (anonymous registration).""" + is_archived: Optional[bool] = rest_field(name="isArchived", visibility=["read", "create", "update"]) + """Is the asset archived?.""" + + @overload + def __init__( + self, + *, + description: Optional[str] = None, + properties: Optional[Dict[str, str]] = None, + tags: Optional[Dict[str, str]] = None, + is_anonymous: Optional[bool] = None, + is_archived: Optional[bool] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AssetContainer(ResourceBase): + """AssetContainer definition. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + :ivar description: The asset description text. + :vartype description: str + :ivar properties: The asset property dictionary. + :vartype properties: dict[str, str] + :ivar tags: Tag dictionary. Tags can be added, removed, and updated. + :vartype tags: dict[str, str] + :ivar is_archived: Is the asset archived?. + :vartype is_archived: bool + :ivar latest_version: The latest version inside this container. + :vartype latest_version: str + :ivar next_version: The next auto incremental version. + :vartype next_version: str + """ + + is_archived: Optional[bool] = rest_field(name="isArchived", visibility=["read", "create", "update"]) + """Is the asset archived?.""" + latest_version: Optional[str] = rest_field(name="latestVersion", visibility=["read"]) + """The latest version inside this container.""" + next_version: Optional[str] = rest_field(name="nextVersion", visibility=["read"]) + """The next auto incremental version.""" + + @overload + def __init__( + self, + *, + description: Optional[str] = None, + properties: Optional[Dict[str, str]] = None, + tags: Optional[Dict[str, str]] = None, + is_archived: Optional[bool] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AssetReferenceBase(_model_base.Model): + """Base definition for asset references. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + DataPathAssetReference, IdAssetReference, OutputPathAssetReference + + + :ivar reference_type: Asset reference type. Required. Known values are: "Id", "DataPath", and + "OutputPath". + :vartype reference_type: str or ~azure.ai.resources.autogen.models.ReferenceType + """ + + __mapping__: Dict[str, _model_base.Model] = {} + reference_type: str = rest_discriminator(name="referenceType") + """Asset reference type. Required. Known values are: \"Id\", \"DataPath\", and \"OutputPath\".""" + + @overload + def __init__( + self, + *, + reference_type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class Assistant(_model_base.Model): + """Represents an assistant that can call the model and use tools. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar id: The identifier, which can be referenced in API endpoints. Required. + :vartype id: str + :ivar object: The object type, which is always assistant. Required. Default value is + "assistant". + :vartype object: str + :ivar created_at: The Unix timestamp, in seconds, representing when this object was created. + Required. + :vartype created_at: ~datetime.datetime + :ivar name: The name of the assistant. Required. + :vartype name: str + :ivar description: The description of the assistant. Required. + :vartype description: str + :ivar model: The ID of the model to use. Required. + :vartype model: str + :ivar instructions: The system instructions for the assistant to use. Required. + :vartype instructions: str + :ivar tools: The collection of tools enabled for the assistant. Required. + :vartype tools: list[~azure.ai.resources.autogen.models.ToolDefinition] + :ivar tool_resources: A set of resources that are used by the assistant's tools. The resources + are specific to the type of tool. For example, the ``code_interpreter`` tool requires a list of + file IDs, while the ``file_search`` tool requires a list of vector store IDs. Required. + :vartype tool_resources: ~azure.ai.resources.autogen.models.ToolResources + :ivar temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 + will make the output more random, while lower values like 0.2 will make it more focused and + deterministic. Required. + :vartype temperature: float + :ivar top_p: An alternative to sampling with temperature, called nucleus sampling, where the + model considers the results of the tokens with top_p probability mass. So 0.1 means only the + tokens comprising the top 10% probability mass are considered. We generally recommend altering + this or temperature but not both. Required. + :vartype top_p: float + :ivar response_format: The response format of the tool calls used by this assistant. Is one of + the following types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], + AssistantsApiResponseFormat + :vartype response_format: str or str or + ~azure.ai.resources.autogen.models.AssistantsApiResponseFormatMode or + ~azure.ai.resources.autogen.models.AssistantsApiResponseFormat + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required. + :vartype metadata: dict[str, str] + """ + + id: str = rest_field() + """The identifier, which can be referenced in API endpoints. Required.""" + object: Literal["assistant"] = rest_field() + """The object type, which is always assistant. Required. Default value is \"assistant\".""" + created_at: datetime.datetime = rest_field(format="unix-timestamp") + """The Unix timestamp, in seconds, representing when this object was created. Required.""" + name: str = rest_field() + """The name of the assistant. Required.""" + description: str = rest_field() + """The description of the assistant. Required.""" + model: str = rest_field() + """The ID of the model to use. Required.""" + instructions: str = rest_field() + """The system instructions for the assistant to use. Required.""" + tools: List["_models.ToolDefinition"] = rest_field() + """The collection of tools enabled for the assistant. Required.""" + tool_resources: "_models.ToolResources" = rest_field() + """A set of resources that are used by the assistant's tools. The resources are specific to the + type of tool. For example, the ``code_interpreter`` tool requires a list of file IDs, while the + ``file_search`` tool requires a list of vector store IDs. Required.""" + temperature: float = rest_field() + """What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output + more random, while lower values like 0.2 will make it more focused and deterministic. Required.""" + top_p: float = rest_field() + """An alternative to sampling with temperature, called nucleus sampling, where the model considers + the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising + the top 10% probability mass are considered. We generally recommend altering this or + temperature but not both. Required.""" + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = rest_field() + """The response format of the tool calls used by this assistant. Is one of the following types: + str, Union[str, \"_models.AssistantsApiResponseFormatMode\"], AssistantsApiResponseFormat""" + metadata: Dict[str, str] = rest_field() + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + created_at: datetime.datetime, + name: str, + description: str, + model: str, + instructions: str, + tools: List["_models.ToolDefinition"], + tool_resources: "_models.ToolResources", + temperature: float, + top_p: float, + metadata: Dict[str, str], + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["assistant"] = "assistant" + + +class AssistantCreationOptions(_model_base.Model): + """The request details to use when creating a new assistant. + + All required parameters must be populated in order to send to server. + + :ivar model: The ID of the model to use. Required. + :vartype model: str + :ivar name: The name of the new assistant. + :vartype name: str + :ivar description: The description of the new assistant. + :vartype description: str + :ivar instructions: The system instructions for the new assistant to use. + :vartype instructions: str + :ivar tools: The collection of tools to enable for the new assistant. + :vartype tools: list[~azure.ai.resources.autogen.models.ToolDefinition] + :ivar tool_resources: A set of resources that are used by the assistant's tools. The resources + are specific to the type of tool. For example, the ``code_interpreter`` tool requires a list of + file IDs, while the ``file_search`` tool requires a list of vector store IDs. + :vartype tool_resources: ~azure.ai.resources.autogen.models.CreateToolResourcesOptions + :ivar temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 + will make the output more random, while lower values like 0.2 will make it more focused and + deterministic. + :vartype temperature: float + :ivar top_p: An alternative to sampling with temperature, called nucleus sampling, where the + model considers the results of the tokens with top_p probability mass. So 0.1 means only the + tokens comprising the top 10% probability mass are considered. We generally recommend altering + this or temperature but not both. + :vartype top_p: float + :ivar response_format: The response format of the tool calls used by this assistant. Is one of + the following types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], + AssistantsApiResponseFormat + :vartype response_format: str or str or + ~azure.ai.resources.autogen.models.AssistantsApiResponseFormatMode or + ~azure.ai.resources.autogen.models.AssistantsApiResponseFormat + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. + :vartype metadata: dict[str, str] + """ + + model: str = rest_field() + """The ID of the model to use. Required.""" + name: Optional[str] = rest_field() + """The name of the new assistant.""" + description: Optional[str] = rest_field() + """The description of the new assistant.""" + instructions: Optional[str] = rest_field() + """The system instructions for the new assistant to use.""" + tools: Optional[List["_models.ToolDefinition"]] = rest_field() + """The collection of tools to enable for the new assistant.""" + tool_resources: Optional["_models.CreateToolResourcesOptions"] = rest_field() + """A set of resources that are used by the assistant's tools. The resources are specific to the + type of tool. For example, the ``code_interpreter`` tool requires a list of file IDs, while the + ``file_search`` tool requires a list of vector store IDs.""" + temperature: Optional[float] = rest_field() + """What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output + more random, while lower values like 0.2 will make it more focused and deterministic.""" + top_p: Optional[float] = rest_field() + """An alternative to sampling with temperature, called nucleus sampling, where the model considers + the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising + the top 10% probability mass are considered. We generally recommend altering this or + temperature but not both.""" + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = rest_field() + """The response format of the tool calls used by this assistant. Is one of the following types: + str, Union[str, \"_models.AssistantsApiResponseFormatMode\"], AssistantsApiResponseFormat""" + metadata: Optional[Dict[str, str]] = rest_field() + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length.""" + + @overload + def __init__( + self, + *, + model: str, + name: Optional[str] = None, + description: Optional[str] = None, + instructions: Optional[str] = None, + tools: Optional[List["_models.ToolDefinition"]] = None, + tool_resources: Optional["_models.CreateToolResourcesOptions"] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = None, + metadata: Optional[Dict[str, str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AssistantDeletionStatus(_model_base.Model): + """The status of an assistant deletion operation. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar id: The ID of the resource specified for deletion. Required. + :vartype id: str + :ivar deleted: A value indicating whether deletion was successful. Required. + :vartype deleted: bool + :ivar object: The object type, which is always 'assistant.deleted'. Required. Default value is + "assistant.deleted". + :vartype object: str + """ + + id: str = rest_field() + """The ID of the resource specified for deletion. Required.""" + deleted: bool = rest_field() + """A value indicating whether deletion was successful. Required.""" + object: Literal["assistant.deleted"] = rest_field() + """The object type, which is always 'assistant.deleted'. Required. Default value is + \"assistant.deleted\".""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + deleted: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["assistant.deleted"] = "assistant.deleted" + + +class AssistantsApiResponseFormat(_model_base.Model): + """An object describing the expected output of the model. If ``json_object`` only ``function`` + type ``tools`` are allowed to be passed to the Run. If ``text`` the model can return text or + any value needed. + + :ivar type: Must be one of ``text`` or ``json_object``. Known values are: "text" and + "json_object". + :vartype type: str or ~azure.ai.resources.autogen.models.ApiResponseFormat + """ + + type: Optional[Union[str, "_models.ApiResponseFormat"]] = rest_field() + """Must be one of ``text`` or ``json_object``. Known values are: \"text\" and \"json_object\".""" + + @overload + def __init__( + self, + *, + type: Optional[Union[str, "_models.ApiResponseFormat"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AssistantsNamedToolChoice(_model_base.Model): + """Specifies a tool the model should use. Use to force the model to call a specific tool. + + + :ivar type: the type of tool. If type is ``function``\\ , the function name must be set. + Required. Known values are: "function", "code_interpreter", and "file_search". + :vartype type: str or ~azure.ai.resources.autogen.models.AssistantsNamedToolChoiceType + :ivar function: The name of the function to call. + :vartype function: ~azure.ai.resources.autogen.models.FunctionName + """ + + type: Union[str, "_models.AssistantsNamedToolChoiceType"] = rest_field() + """the type of tool. If type is ``function``\ , the function name must be set. Required. Known + values are: \"function\", \"code_interpreter\", and \"file_search\".""" + function: Optional["_models.FunctionName"] = rest_field() + """The name of the function to call.""" + + @overload + def __init__( + self, + *, + type: Union[str, "_models.AssistantsNamedToolChoiceType"], + function: Optional["_models.FunctionName"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AssistantThread(_model_base.Model): + """Information about a single thread associated with an assistant. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar id: The identifier, which can be referenced in API endpoints. Required. + :vartype id: str + :ivar object: The object type, which is always 'thread'. Required. Default value is "thread". + :vartype object: str + :ivar created_at: The Unix timestamp, in seconds, representing when this object was created. + Required. + :vartype created_at: ~datetime.datetime + :ivar tool_resources: A set of resources that are made available to the assistant's tools in + this thread. The resources are specific to the type of tool. For example, the + ``code_interpreter`` tool requires a list of file IDs, while the ``file_search`` tool requires + a list of vector store IDs. Required. + :vartype tool_resources: ~azure.ai.resources.autogen.models.ToolResources + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required. + :vartype metadata: dict[str, str] + """ + + id: str = rest_field() + """The identifier, which can be referenced in API endpoints. Required.""" + object: Literal["thread"] = rest_field() + """The object type, which is always 'thread'. Required. Default value is \"thread\".""" + created_at: datetime.datetime = rest_field(format="unix-timestamp") + """The Unix timestamp, in seconds, representing when this object was created. Required.""" + tool_resources: "_models.ToolResources" = rest_field() + """A set of resources that are made available to the assistant's tools in this thread. The + resources are specific to the type of tool. For example, the ``code_interpreter`` tool requires + a list of file IDs, while the ``file_search`` tool requires a list of vector store IDs. + Required.""" + metadata: Dict[str, str] = rest_field() + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + created_at: datetime.datetime, + tool_resources: "_models.ToolResources", + metadata: Dict[str, str], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["thread"] = "thread" + + +class AssistantThreadCreationOptions(_model_base.Model): + """The details used to create a new assistant thread. + + :ivar messages: The initial messages to associate with the new thread. + :vartype messages: list[~azure.ai.resources.autogen.models.ThreadMessageOptions] + :ivar tool_resources: A set of resources that are made available to the assistant's tools in + this thread. The resources are specific to the type of tool. For example, the + ``code_interpreter`` tool requires a list of file IDs, while the ``file_search`` tool requires + a list of vector store IDs. + :vartype tool_resources: ~azure.ai.resources.autogen.models.CreateToolResourcesOptions + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. + :vartype metadata: dict[str, str] + """ + + messages: Optional[List["_models.ThreadMessageOptions"]] = rest_field() + """The initial messages to associate with the new thread.""" + tool_resources: Optional["_models.CreateToolResourcesOptions"] = rest_field() + """A set of resources that are made available to the assistant's tools in this thread. The + resources are specific to the type of tool. For example, the ``code_interpreter`` tool requires + a list of file IDs, while the ``file_search`` tool requires a list of vector store IDs.""" + metadata: Optional[Dict[str, str]] = rest_field() + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length.""" + + @overload + def __init__( + self, + *, + messages: Optional[List["_models.ThreadMessageOptions"]] = None, + tool_resources: Optional["_models.CreateToolResourcesOptions"] = None, + metadata: Optional[Dict[str, str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class EvaluationTarget(_model_base.Model): + """Evaluation Target. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + BaseModel + + + :ivar type: Discriminator property for EvaluationTarget. Required. Default value is None. + :vartype type: str + :ivar name: Name of the evaluation target. + :vartype name: str + """ + + __mapping__: Dict[str, _model_base.Model] = {} + type: str = rest_discriminator(name="type") + """Discriminator property for EvaluationTarget. Required. Default value is None.""" + name: Optional[str] = rest_field() + """Name of the evaluation target.""" + + @overload + def __init__( + self, + *, + type: str, + name: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class BaseModel(EvaluationTarget, discriminator="base_model"): + """Base Model and prompt for evaluation. + + + :ivar name: Name of the evaluation target. + :vartype name: str + :ivar model_config: Base model for evaluation. Required. + :vartype model_config: dict[str, dict[str, any]] + :ivar prompt: System prompt to be used with base model. This property will hold prompt asset + id. Required. + :vartype prompt: str + :ivar parameters: parameters for evaluation. Required. + :vartype parameters: dict[str, str] + :ivar type: Required. Default value is "base_model". + :vartype type: str + """ + + model_config: Dict[str, Dict[str, Any]] = rest_field() + """Base model for evaluation. Required.""" + prompt: str = rest_field() + """System prompt to be used with base model. This property will hold prompt asset id. Required.""" + parameters: Dict[str, str] = rest_field() + """parameters for evaluation. Required.""" + type: Literal["base_model"] = rest_discriminator(name="type") # type: ignore + """Required. Default value is \"base_model\".""" + + @overload + def __init__( + self, + *, + model_config: Dict[str, Dict[str, Any]], + prompt: str, + parameters: Dict[str, str], + name: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="base_model", **kwargs) + + +class EndpointDeploymentBase(_model_base.Model): + """Base definition for endpoint deployment. + + :ivar code_configuration: Code configuration for the endpoint deployment. + :vartype code_configuration: ~azure.ai.resources.autogen.models.CodeConfiguration + :ivar description: Description of the endpoint deployment. + :vartype description: str + :ivar environment_id: ARM resource ID or AssetId of the environment specification for the + endpoint deployment. + :vartype environment_id: str + :ivar environment_variables: Environment variables configuration for the deployment. + :vartype environment_variables: dict[str, str] + :ivar properties: Property dictionary. Properties can be added, but not removed or altered. + :vartype properties: dict[str, str] + """ + + code_configuration: Optional["_models.CodeConfiguration"] = rest_field(name="codeConfiguration") + """Code configuration for the endpoint deployment.""" + description: Optional[str] = rest_field() + """Description of the endpoint deployment.""" + environment_id: Optional[str] = rest_field(name="environmentId") + """ARM resource ID or AssetId of the environment specification for the endpoint deployment.""" + environment_variables: Optional[Dict[str, str]] = rest_field(name="environmentVariables") + """Environment variables configuration for the deployment.""" + properties: Optional[Dict[str, str]] = rest_field() + """Property dictionary. Properties can be added, but not removed or altered.""" + + @overload + def __init__( + self, + *, + code_configuration: Optional["_models.CodeConfiguration"] = None, + description: Optional[str] = None, + environment_id: Optional[str] = None, + environment_variables: Optional[Dict[str, str]] = None, + properties: Optional[Dict[str, str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class BatchDeployment(EndpointDeploymentBase): + """Batch inference settings per deployment. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + :ivar code_configuration: Code configuration for the endpoint deployment. + :vartype code_configuration: ~azure.ai.resources.autogen.models.CodeConfiguration + :ivar description: Description of the endpoint deployment. + :vartype description: str + :ivar environment_id: ARM resource ID or AssetId of the environment specification for the + endpoint deployment. + :vartype environment_id: str + :ivar environment_variables: Environment variables configuration for the deployment. + :vartype environment_variables: dict[str, str] + :ivar properties: Property dictionary. Properties can be added, but not removed or altered. + :vartype properties: dict[str, str] + :ivar compute: Compute target for batch inference operation. + :vartype compute: str + :ivar deployment_configuration: Properties relevant to different deployment types. + :vartype deployment_configuration: + ~azure.ai.resources.autogen.models.BatchDeploymentConfiguration + :ivar error_threshold: Error threshold, if the error count for the entire input goes above this + value, the batch inference will be aborted. Range is [-1, int.MaxValue]. For FileDataset, this + value is the count of file failures. For TabularDataset, this value is the count of record + failures. If set to -1 (the lower bound), all failures during batch inference will be ignored. + :vartype error_threshold: int + :ivar logging_level: Logging level for batch inference operation. Known values are: "Info", + "Warning", and "Debug". + :vartype logging_level: str or ~azure.ai.resources.autogen.models.BatchLoggingLevel + :ivar max_concurrency_per_instance: Indicates maximum number of parallelism per instance. + :vartype max_concurrency_per_instance: int + :ivar mini_batch_size: Size of the mini-batch passed to each batch invocation. For FileDataset, + this is the number of files per mini-batch. For TabularDataset, this is the size of the records + in bytes, per mini-batch. + :vartype mini_batch_size: int + :ivar model: Reference to the model asset for the endpoint deployment. + :vartype model: ~azure.ai.resources.autogen.models.AssetReferenceBase + :ivar output_action: Indicates how the output will be organized. Known values are: + "SummaryOnly" and "AppendRow". + :vartype output_action: str or ~azure.ai.resources.autogen.models.BatchOutputAction + :ivar output_file_name: Customized output file name for append_row output action. + :vartype output_file_name: str + :ivar provisioning_state: Provisioning state for the endpoint deployment. Known values are: + "Creating", "Deleting", "Scaling", "Updating", "Succeeded", "Failed", and "Canceled". + :vartype provisioning_state: str or + ~azure.ai.resources.autogen.models.DeploymentProvisioningState + :ivar resources: Indicates compute configuration for the job. If not provided, will default to + the defaults defined in ResourceConfiguration. + :vartype resources: ~azure.ai.resources.autogen.models.DeploymentResourceConfiguration + :ivar retry_settings: Retry Settings for the batch inference operation. If not provided, will + default to the defaults defined in BatchRetrySettings. + :vartype retry_settings: ~azure.ai.resources.autogen.models.BatchRetrySettings + """ + + compute: Optional[str] = rest_field() + """Compute target for batch inference operation.""" + deployment_configuration: Optional["_models.BatchDeploymentConfiguration"] = rest_field( + name="deploymentConfiguration" + ) + """Properties relevant to different deployment types.""" + error_threshold: Optional[int] = rest_field(name="errorThreshold") + """Error threshold, if the error count for the entire input goes above this value, the batch + inference will be aborted. Range is [-1, int.MaxValue]. For FileDataset, this value is the + count of file failures. For TabularDataset, this value is the count of record failures. If set + to -1 (the lower bound), all failures during batch inference will be ignored.""" + logging_level: Optional[Union[str, "_models.BatchLoggingLevel"]] = rest_field(name="loggingLevel") + """Logging level for batch inference operation. Known values are: \"Info\", \"Warning\", and + \"Debug\".""" + max_concurrency_per_instance: Optional[int] = rest_field(name="maxConcurrencyPerInstance") + """Indicates maximum number of parallelism per instance.""" + mini_batch_size: Optional[int] = rest_field(name="miniBatchSize") + """Size of the mini-batch passed to each batch invocation. For FileDataset, this is the number of + files per mini-batch. For TabularDataset, this is the size of the records in bytes, per + mini-batch.""" + model: Optional["_models.AssetReferenceBase"] = rest_field() + """Reference to the model asset for the endpoint deployment.""" + output_action: Optional[Union[str, "_models.BatchOutputAction"]] = rest_field(name="outputAction") + """Indicates how the output will be organized. Known values are: \"SummaryOnly\" and + \"AppendRow\".""" + output_file_name: Optional[str] = rest_field(name="outputFileName") + """Customized output file name for append_row output action.""" + provisioning_state: Optional[Union[str, "_models.DeploymentProvisioningState"]] = rest_field( + name="provisioningState", visibility=["read"] + ) + """Provisioning state for the endpoint deployment. Known values are: \"Creating\", \"Deleting\", + \"Scaling\", \"Updating\", \"Succeeded\", \"Failed\", and \"Canceled\".""" + resources: Optional["_models.DeploymentResourceConfiguration"] = rest_field() + """Indicates compute configuration for the job. If not provided, will default to the defaults + defined in ResourceConfiguration.""" + retry_settings: Optional["_models.BatchRetrySettings"] = rest_field(name="retrySettings") + """Retry Settings for the batch inference operation. If not provided, will default to the defaults + defined in BatchRetrySettings.""" + + @overload + def __init__( + self, + *, + code_configuration: Optional["_models.CodeConfiguration"] = None, + description: Optional[str] = None, + environment_id: Optional[str] = None, + environment_variables: Optional[Dict[str, str]] = None, + properties: Optional[Dict[str, str]] = None, + compute: Optional[str] = None, + deployment_configuration: Optional["_models.BatchDeploymentConfiguration"] = None, + error_threshold: Optional[int] = None, + logging_level: Optional[Union[str, "_models.BatchLoggingLevel"]] = None, + max_concurrency_per_instance: Optional[int] = None, + mini_batch_size: Optional[int] = None, + model: Optional["_models.AssetReferenceBase"] = None, + output_action: Optional[Union[str, "_models.BatchOutputAction"]] = None, + output_file_name: Optional[str] = None, + resources: Optional["_models.DeploymentResourceConfiguration"] = None, + retry_settings: Optional["_models.BatchRetrySettings"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class BatchDeploymentConfiguration(_model_base.Model): + """Properties relevant to different deployment types. + + + :ivar deployment_configuration_type: The batch deployment configuration type. Required. Known + values are: "Model" and "PipelineComponent". + :vartype deployment_configuration_type: str or + ~azure.ai.resources.autogen.models.BatchDeploymentConfigurationType + """ + + deployment_configuration_type: Union[str, "_models.BatchDeploymentConfigurationType"] = rest_discriminator( + name="deploymentConfigurationType" + ) + """The batch deployment configuration type. Required. Known values are: \"Model\" and + \"PipelineComponent\".""" + + @overload + def __init__( + self, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class EndpointBase(_model_base.Model): + """Inference Endpoint base definition. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar auth_mode: [Required] Use 'Key' for key based authentication and 'AMLToken' for Azure + Machine Learning token-based authentication. 'Key' doesn't expire but 'AMLToken' does. + Required. Known values are: "AMLToken", "Key", and "AADToken". + :vartype auth_mode: str or ~azure.ai.resources.autogen.models.EndpointAuthMode + :ivar description: Description of the inference endpoint. + :vartype description: str + :ivar keys_property: EndpointAuthKeys to set initially on an Endpoint. This property will + always be returned as null. AuthKey values must be retrieved using the ListKeys API. + :vartype keys_property: ~azure.ai.resources.autogen.models.EndpointAuthKeys + :ivar properties: Property dictionary. Properties can be added, but not removed or altered. + :vartype properties: dict[str, str] + :ivar scoring_uri: Endpoint URI. + :vartype scoring_uri: str + :ivar swagger_uri: Endpoint Swagger URI. + :vartype swagger_uri: str + """ + + auth_mode: Union[str, "_models.EndpointAuthMode"] = rest_field(name="authMode") + """[Required] Use 'Key' for key based authentication and 'AMLToken' for Azure Machine Learning + token-based authentication. 'Key' doesn't expire but 'AMLToken' does. Required. Known values + are: \"AMLToken\", \"Key\", and \"AADToken\".""" + description: Optional[str] = rest_field() + """Description of the inference endpoint.""" + keys_property: Optional["_models.EndpointAuthKeys"] = rest_field(name="keys", visibility=["create"]) + """EndpointAuthKeys to set initially on an Endpoint. This property will always be returned as + null. AuthKey values must be retrieved using the ListKeys API.""" + properties: Optional[Dict[str, str]] = rest_field() + """Property dictionary. Properties can be added, but not removed or altered.""" + scoring_uri: Optional[str] = rest_field(name="scoringUri", visibility=["read"]) + """Endpoint URI.""" + swagger_uri: Optional[str] = rest_field(name="swaggerUri", visibility=["read"]) + """Endpoint Swagger URI.""" + + @overload + def __init__( + self, + *, + auth_mode: Union[str, "_models.EndpointAuthMode"], + description: Optional[str] = None, + keys_property: Optional["_models.EndpointAuthKeys"] = None, + properties: Optional[Dict[str, str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class BatchEndpoint(EndpointBase): + """Batch endpoint configuration. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar auth_mode: [Required] Use 'Key' for key based authentication and 'AMLToken' for Azure + Machine Learning token-based authentication. 'Key' doesn't expire but 'AMLToken' does. + Required. Known values are: "AMLToken", "Key", and "AADToken". + :vartype auth_mode: str or ~azure.ai.resources.autogen.models.EndpointAuthMode + :ivar description: Description of the inference endpoint. + :vartype description: str + :ivar keys_property: EndpointAuthKeys to set initially on an Endpoint. This property will + always be returned as null. AuthKey values must be retrieved using the ListKeys API. + :vartype keys_property: ~azure.ai.resources.autogen.models.EndpointAuthKeys + :ivar properties: Property dictionary. Properties can be added, but not removed or altered. + :vartype properties: dict[str, str] + :ivar scoring_uri: Endpoint URI. + :vartype scoring_uri: str + :ivar swagger_uri: Endpoint Swagger URI. + :vartype swagger_uri: str + :ivar defaults: Default values for Batch Endpoint. + :vartype defaults: ~azure.ai.resources.autogen.models.BatchEndpointDefaults + :ivar provisioning_state: Provisioning state for the endpoint. Known values are: "Creating", + "Deleting", "Succeeded", "Failed", "Updating", and "Canceled". + :vartype provisioning_state: str or + ~azure.ai.resources.autogen.models.EndpointProvisioningState + """ + + defaults: Optional["_models.BatchEndpointDefaults"] = rest_field() + """Default values for Batch Endpoint.""" + provisioning_state: Optional[Union[str, "_models.EndpointProvisioningState"]] = rest_field( + name="provisioningState", visibility=["read"] + ) + """Provisioning state for the endpoint. Known values are: \"Creating\", \"Deleting\", + \"Succeeded\", \"Failed\", \"Updating\", and \"Canceled\".""" + + @overload + def __init__( + self, + *, + auth_mode: Union[str, "_models.EndpointAuthMode"], + description: Optional[str] = None, + keys_property: Optional["_models.EndpointAuthKeys"] = None, + properties: Optional[Dict[str, str]] = None, + defaults: Optional["_models.BatchEndpointDefaults"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class BatchEndpointDefaults(_model_base.Model): + """Batch endpoint default values. + + :ivar deployment_name: Name of the deployment that will be default for the endpoint. This + deployment will end up getting 100% traffic when the endpoint scoring URL is invoked. + :vartype deployment_name: str + """ + + deployment_name: Optional[str] = rest_field(name="deploymentName") + """Name of the deployment that will be default for the endpoint. This deployment will end up + getting 100% traffic when the endpoint scoring URL is invoked.""" + + @overload + def __init__( + self, + *, + deployment_name: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class BatchRetrySettings(_model_base.Model): + """Retry settings for a batch inference operation. + + :ivar max_retries: Maximum retry count for a mini-batch. + :vartype max_retries: int + :ivar timeout: Invocation timeout for a mini-batch, in ISO 8601 format. + :vartype timeout: ~datetime.timedelta + """ + + max_retries: Optional[int] = rest_field(name="maxRetries") + """Maximum retry count for a mini-batch.""" + timeout: Optional[datetime.timedelta] = rest_field() + """Invocation timeout for a mini-batch, in ISO 8601 format.""" + + @overload + def __init__( + self, + *, + max_retries: Optional[int] = None, + timeout: Optional[datetime.timedelta] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class CodeConfiguration(_model_base.Model): + """Configuration for a scoring code asset. + + + :ivar code_id: ARM resource ID of the code asset. + :vartype code_id: str + :ivar scoring_script: [Required] The script to execute on startup. eg. 'score.py'. Required. + :vartype scoring_script: str + """ + + code_id: Optional[str] = rest_field(name="codeId", visibility=["read", "create"]) + """ARM resource ID of the code asset.""" + scoring_script: str = rest_field(name="scoringScript", visibility=["read", "create"]) + """[Required] The script to execute on startup. eg. 'score.py'. Required.""" + + @overload + def __init__( + self, + *, + scoring_script: str, + code_id: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ToolDefinition(_model_base.Model): + """An abstract representation of an input tool definition that an assistant can use. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + CodeInterpreterToolDefinition, FileSearchToolDefinition, FunctionToolDefinition + + + :ivar type: The object type. Required. Default value is None. + :vartype type: str + """ + + __mapping__: Dict[str, _model_base.Model] = {} + type: str = rest_discriminator(name="type") + """The object type. Required. Default value is None.""" + + @overload + def __init__( + self, + *, + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class CodeInterpreterToolDefinition(ToolDefinition, discriminator="code_interpreter"): + """The input definition information for a code interpreter tool as used to configure an assistant. + + + :ivar type: The object type, which is always 'code_interpreter'. Required. Default value is + "code_interpreter". + :vartype type: str + """ + + type: Literal["code_interpreter"] = rest_discriminator(name="type") # type: ignore + """The object type, which is always 'code_interpreter'. Required. Default value is + \"code_interpreter\".""" + + @overload + def __init__( + self, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="code_interpreter", **kwargs) + + +class CodeInterpreterToolResource(_model_base.Model): + """A set of resources that are used by the ``code_interpreter`` tool. + + + :ivar file_ids: A list of file IDs made available to the ``code_interpreter`` tool. There can + be a maximum of 20 files associated with the tool. Required. + :vartype file_ids: list[str] + """ + + file_ids: List[str] = rest_field() + """A list of file IDs made available to the ``code_interpreter`` tool. There can be a maximum of + 20 files associated with the tool. Required.""" + + @overload + def __init__( + self, + *, + file_ids: List[str], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class Collection(_model_base.Model): + """Collection definition. + + :ivar client_id: The msi client id used to collect logging to blob storage. If it's + null,backend will pick a registered endpoint identity to auth. + :vartype client_id: str + :ivar data_collection_mode: Enable or disable data collection. Known values are: "Enabled" and + "Disabled". + :vartype data_collection_mode: str or ~azure.ai.resources.autogen.models.DataCollectionMode + :ivar data_id: The data asset arm resource id. Client side will ensure data asset is pointing + to the blob storage, and backend will collect data to the blob storage. + :vartype data_id: str + :ivar sampling_rate: The sampling rate for collection. Sampling rate 1.0 means we collect 100% + of data by default. + :vartype sampling_rate: float + """ + + client_id: Optional[str] = rest_field(name="clientId") + """The msi client id used to collect logging to blob storage. If it's null,backend will pick a + registered endpoint identity to auth.""" + data_collection_mode: Optional[Union[str, "_models.DataCollectionMode"]] = rest_field(name="dataCollectionMode") + """Enable or disable data collection. Known values are: \"Enabled\" and \"Disabled\".""" + data_id: Optional[str] = rest_field(name="dataId") + """The data asset arm resource id. Client side will ensure data asset is pointing to the blob + storage, and backend will collect data to the blob storage.""" + sampling_rate: Optional[float] = rest_field(name="samplingRate") + """The sampling rate for collection. Sampling rate 1.0 means we collect 100% of data by default.""" + + @overload + def __init__( + self, + *, + client_id: Optional[str] = None, + data_collection_mode: Optional[Union[str, "_models.DataCollectionMode"]] = None, + data_id: Optional[str] = None, + sampling_rate: Optional[float] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class Connection(_model_base.Model): + """Connection Definition. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar name: Name of the connection. Required. + :vartype name: str + :ivar type: The type of connection, such as AzureOpenAI, AIServices, AISearch, etc. Required. + :vartype type: str + :ivar target: The URL endpoint of the external resource being connected to. Required. + :vartype target: str + :ivar credentials: Credential used to connect to the external resource. Required. + :vartype credentials: ~azure.ai.resources.autogen.models.BaseCredential + :ivar system_data: Metadata containing createdBy and modifiedBy information. + :vartype system_data: ~azure.ai.resources.autogen.models.SystemData + """ + + name: str = rest_field(visibility=["read"]) + """Name of the connection. Required.""" + type: str = rest_field(visibility=["read"]) + """The type of connection, such as AzureOpenAI, AIServices, AISearch, etc. Required.""" + target: str = rest_field() + """The URL endpoint of the external resource being connected to. Required.""" + credentials: "_models.BaseCredential" = rest_field() + """Credential used to connect to the external resource. Required.""" + system_data: Optional["_models.SystemData"] = rest_field(name="systemData", visibility=["read"]) + """Metadata containing createdBy and modifiedBy information.""" + + @overload + def __init__( + self, + *, + target: str, + credentials: "_models.BaseCredential", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class CreateAndRunThreadOptions(_model_base.Model): + """The details used when creating and immediately running a new assistant thread. + + All required parameters must be populated in order to send to server. + + :ivar assistant_id: The ID of the assistant for which the thread should be created. Required. + :vartype assistant_id: str + :ivar thread: The details used to create the new thread. If no thread is provided, an empty one + will be created. + :vartype thread: ~azure.ai.resources.autogen.models.AssistantThreadCreationOptions + :ivar model: The overridden model that the assistant should use to run the thread. + :vartype model: str + :ivar instructions: The overridden system instructions the assistant should use to run the + thread. + :vartype instructions: str + :ivar tools: The overridden list of enabled tools the assistant should use to run the thread. + :vartype tools: list[~azure.ai.resources.autogen.models.ToolDefinition] + :ivar tool_resources: Override the tools the assistant can use for this run. This is useful for + modifying the behavior on a per-run basis. + :vartype tool_resources: ~azure.ai.resources.autogen.models.UpdateToolResourcesOptions + :ivar stream: If ``true``\\ , returns a stream of events that happen during the Run as + server-sent events, + terminating when the Run enters a terminal state with a ``data: [DONE]`` message. + :vartype stream: bool + :ivar temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 + will make the output + more random, while lower values like 0.2 will make it more focused and deterministic. + :vartype temperature: float + :ivar top_p: An alternative to sampling with temperature, called nucleus sampling, where the + model + considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens + comprising the top 10% probability mass are considered. + + We generally recommend altering this or temperature but not both. + :vartype top_p: float + :ivar max_prompt_tokens: The maximum number of prompt tokens that may be used over the course + of the run. The run will make a best effort to use only + the number of prompt tokens specified, across multiple turns of the run. If the run exceeds + the number of prompt tokens specified, + the run will end with status ``incomplete``. See ``incomplete_details`` for more info. + :vartype max_prompt_tokens: int + :ivar max_completion_tokens: The maximum number of completion tokens that may be used over the + course of the run. The run will make a best effort to use only + the number of completion tokens specified, across multiple turns of the run. If the run + exceeds the number of completion tokens + specified, the run will end with status ``incomplete``. See ``incomplete_details`` for more + info. + :vartype max_completion_tokens: int + :ivar truncation_strategy: The strategy to use for dropping messages as the context windows + moves forward. + :vartype truncation_strategy: ~azure.ai.resources.autogen.models.TruncationObject + :ivar tool_choice: Controls whether or not and which tool is called by the model. Is one of the + following types: str, Union[str, "_models.AssistantsApiToolChoiceOptionMode"], + AssistantsNamedToolChoice + :vartype tool_choice: str or str or + ~azure.ai.resources.autogen.models.AssistantsApiToolChoiceOptionMode or + ~azure.ai.resources.autogen.models.AssistantsNamedToolChoice + :ivar response_format: Specifies the format that the model must output. Is one of the following + types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], AssistantsApiResponseFormat + :vartype response_format: str or str or + ~azure.ai.resources.autogen.models.AssistantsApiResponseFormatMode or + ~azure.ai.resources.autogen.models.AssistantsApiResponseFormat + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. + :vartype metadata: dict[str, str] + """ + + assistant_id: str = rest_field() + """The ID of the assistant for which the thread should be created. Required.""" + thread: Optional["_models.AssistantThreadCreationOptions"] = rest_field() + """The details used to create the new thread. If no thread is provided, an empty one will be + created.""" + model: Optional[str] = rest_field() + """The overridden model that the assistant should use to run the thread.""" + instructions: Optional[str] = rest_field() + """The overridden system instructions the assistant should use to run the thread.""" + tools: Optional[List["_models.ToolDefinition"]] = rest_field() + """The overridden list of enabled tools the assistant should use to run the thread.""" + tool_resources: Optional["_models.UpdateToolResourcesOptions"] = rest_field() + """Override the tools the assistant can use for this run. This is useful for modifying the + behavior on a per-run basis.""" + stream: Optional[bool] = rest_field() + """If ``true``\ , returns a stream of events that happen during the Run as server-sent events, + terminating when the Run enters a terminal state with a ``data: [DONE]`` message.""" + temperature: Optional[float] = rest_field() + """What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output + more random, while lower values like 0.2 will make it more focused and deterministic.""" + top_p: Optional[float] = rest_field() + """An alternative to sampling with temperature, called nucleus sampling, where the model + considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens + comprising the top 10% probability mass are considered. + + We generally recommend altering this or temperature but not both.""" + max_prompt_tokens: Optional[int] = rest_field() + """The maximum number of prompt tokens that may be used over the course of the run. The run will + make a best effort to use only + the number of prompt tokens specified, across multiple turns of the run. If the run exceeds the + number of prompt tokens specified, + the run will end with status ``incomplete``. See ``incomplete_details`` for more info.""" + max_completion_tokens: Optional[int] = rest_field() + """The maximum number of completion tokens that may be used over the course of the run. The run + will make a best effort to use only + the number of completion tokens specified, across multiple turns of the run. If the run exceeds + the number of completion tokens + specified, the run will end with status ``incomplete``. See ``incomplete_details`` for more + info.""" + truncation_strategy: Optional["_models.TruncationObject"] = rest_field() + """The strategy to use for dropping messages as the context windows moves forward.""" + tool_choice: Optional["_types.AssistantsApiToolChoiceOption"] = rest_field() + """Controls whether or not and which tool is called by the model. Is one of the following types: + str, Union[str, \"_models.AssistantsApiToolChoiceOptionMode\"], AssistantsNamedToolChoice""" + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = rest_field() + """Specifies the format that the model must output. Is one of the following types: str, Union[str, + \"_models.AssistantsApiResponseFormatMode\"], AssistantsApiResponseFormat""" + metadata: Optional[Dict[str, str]] = rest_field() + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length.""" + + @overload + def __init__( + self, + *, + assistant_id: str, + thread: Optional["_models.AssistantThreadCreationOptions"] = None, + model: Optional[str] = None, + instructions: Optional[str] = None, + tools: Optional[List["_models.ToolDefinition"]] = None, + tool_resources: Optional["_models.UpdateToolResourcesOptions"] = None, + stream: Optional[bool] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + max_prompt_tokens: Optional[int] = None, + max_completion_tokens: Optional[int] = None, + truncation_strategy: Optional["_models.TruncationObject"] = None, + tool_choice: Optional["_types.AssistantsApiToolChoiceOption"] = None, + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = None, + metadata: Optional[Dict[str, str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class CreateCodeInterpreterToolResourceOptions(_model_base.Model): + """A set of resources that will be used by the ``code_interpreter`` tool. Request object. + + :ivar file_ids: A list of file IDs made available to the ``code_interpreter`` tool. + :vartype file_ids: list[str] + """ + + file_ids: Optional[List[str]] = rest_field() + """A list of file IDs made available to the ``code_interpreter`` tool.""" + + @overload + def __init__( + self, + *, + file_ids: Optional[List[str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class CreateFileSearchToolResourceVectorStoreOptions(_model_base.Model): # pylint: disable=name-too-long + """File IDs associated to the vector store to be passed to the helper. + + All required parameters must be populated in order to send to server. + + :ivar file_ids: A list of file IDs to add to the vector store. There can be a maximum of 10000 + files in a vector store. Required. + :vartype file_ids: list[str] + :ivar chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will use + the ``auto`` strategy. Required. + :vartype chunking_strategy: + ~azure.ai.resources.autogen.models.VectorStoreChunkingStrategyRequest + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. + :vartype metadata: dict[str, str] + """ + + file_ids: List[str] = rest_field() + """A list of file IDs to add to the vector store. There can be a maximum of 10000 files in a + vector store. Required.""" + chunking_strategy: "_models.VectorStoreChunkingStrategyRequest" = rest_field() + """The chunking strategy used to chunk the file(s). If not set, will use the ``auto`` strategy. + Required.""" + metadata: Optional[Dict[str, str]] = rest_field() + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length.""" + + @overload + def __init__( + self, + *, + file_ids: List[str], + chunking_strategy: "_models.VectorStoreChunkingStrategyRequest", + metadata: Optional[Dict[str, str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class CreateRunOptions(_model_base.Model): + """The details used when creating a new run of an assistant thread. + + All required parameters must be populated in order to send to server. + + :ivar assistant_id: The ID of the assistant that should run the thread. Required. + :vartype assistant_id: str + :ivar model: The overridden model name that the assistant should use to run the thread. + :vartype model: str + :ivar instructions: The overridden system instructions that the assistant should use to run the + thread. + :vartype instructions: str + :ivar additional_instructions: Additional instructions to append at the end of the instructions + for the run. This is useful for modifying the behavior + on a per-run basis without overriding other instructions. + :vartype additional_instructions: str + :ivar additional_messages: Adds additional messages to the thread before creating the run. + :vartype additional_messages: list[~azure.ai.resources.autogen.models.ThreadMessage] + :ivar tools: The overridden list of enabled tools that the assistant should use to run the + thread. + :vartype tools: list[~azure.ai.resources.autogen.models.ToolDefinition] + :ivar stream: If ``true``\\ , returns a stream of events that happen during the Run as + server-sent events, + terminating when the Run enters a terminal state with a ``data: [DONE]`` message. + :vartype stream: bool + :ivar temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 + will make the output + more random, while lower values like 0.2 will make it more focused and deterministic. + :vartype temperature: float + :ivar top_p: An alternative to sampling with temperature, called nucleus sampling, where the + model + considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens + comprising the top 10% probability mass are considered. + + We generally recommend altering this or temperature but not both. + :vartype top_p: float + :ivar max_prompt_tokens: The maximum number of prompt tokens that may be used over the course + of the run. The run will make a best effort to use only + the number of prompt tokens specified, across multiple turns of the run. If the run exceeds + the number of prompt tokens specified, + the run will end with status ``incomplete``. See ``incomplete_details`` for more info. + :vartype max_prompt_tokens: int + :ivar max_completion_tokens: The maximum number of completion tokens that may be used over the + course of the run. The run will make a best effort + to use only the number of completion tokens specified, across multiple turns of the run. If + the run exceeds the number of + completion tokens specified, the run will end with status ``incomplete``. See + ``incomplete_details`` for more info. + :vartype max_completion_tokens: int + :ivar truncation_strategy: The strategy to use for dropping messages as the context windows + moves forward. + :vartype truncation_strategy: ~azure.ai.resources.autogen.models.TruncationObject + :ivar tool_choice: Controls whether or not and which tool is called by the model. Is one of the + following types: str, Union[str, "_models.AssistantsApiToolChoiceOptionMode"], + AssistantsNamedToolChoice + :vartype tool_choice: str or str or + ~azure.ai.resources.autogen.models.AssistantsApiToolChoiceOptionMode or + ~azure.ai.resources.autogen.models.AssistantsNamedToolChoice + :ivar response_format: Specifies the format that the model must output. Is one of the following + types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], AssistantsApiResponseFormat + :vartype response_format: str or str or + ~azure.ai.resources.autogen.models.AssistantsApiResponseFormatMode or + ~azure.ai.resources.autogen.models.AssistantsApiResponseFormat + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. + :vartype metadata: dict[str, str] + """ + + assistant_id: str = rest_field() + """The ID of the assistant that should run the thread. Required.""" + model: Optional[str] = rest_field() + """The overridden model name that the assistant should use to run the thread.""" + instructions: Optional[str] = rest_field() + """The overridden system instructions that the assistant should use to run the thread.""" + additional_instructions: Optional[str] = rest_field() + """Additional instructions to append at the end of the instructions for the run. This is useful + for modifying the behavior + on a per-run basis without overriding other instructions.""" + additional_messages: Optional[List["_models.ThreadMessage"]] = rest_field() + """Adds additional messages to the thread before creating the run.""" + tools: Optional[List["_models.ToolDefinition"]] = rest_field() + """The overridden list of enabled tools that the assistant should use to run the thread.""" + stream: Optional[bool] = rest_field() + """If ``true``\ , returns a stream of events that happen during the Run as server-sent events, + terminating when the Run enters a terminal state with a ``data: [DONE]`` message.""" + temperature: Optional[float] = rest_field() + """What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output + more random, while lower values like 0.2 will make it more focused and deterministic.""" + top_p: Optional[float] = rest_field() + """An alternative to sampling with temperature, called nucleus sampling, where the model + considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens + comprising the top 10% probability mass are considered. + + We generally recommend altering this or temperature but not both.""" + max_prompt_tokens: Optional[int] = rest_field() + """The maximum number of prompt tokens that may be used over the course of the run. The run will + make a best effort to use only + the number of prompt tokens specified, across multiple turns of the run. If the run exceeds the + number of prompt tokens specified, + the run will end with status ``incomplete``. See ``incomplete_details`` for more info.""" + max_completion_tokens: Optional[int] = rest_field() + """The maximum number of completion tokens that may be used over the course of the run. The run + will make a best effort + to use only the number of completion tokens specified, across multiple turns of the run. If the + run exceeds the number of + completion tokens specified, the run will end with status ``incomplete``. See + ``incomplete_details`` for more info.""" + truncation_strategy: Optional["_models.TruncationObject"] = rest_field() + """The strategy to use for dropping messages as the context windows moves forward.""" + tool_choice: Optional["_types.AssistantsApiToolChoiceOption"] = rest_field() + """Controls whether or not and which tool is called by the model. Is one of the following types: + str, Union[str, \"_models.AssistantsApiToolChoiceOptionMode\"], AssistantsNamedToolChoice""" + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = rest_field() + """Specifies the format that the model must output. Is one of the following types: str, Union[str, + \"_models.AssistantsApiResponseFormatMode\"], AssistantsApiResponseFormat""" + metadata: Optional[Dict[str, str]] = rest_field() + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length.""" + + @overload + def __init__( + self, + *, + assistant_id: str, + model: Optional[str] = None, + instructions: Optional[str] = None, + additional_instructions: Optional[str] = None, + additional_messages: Optional[List["_models.ThreadMessage"]] = None, + tools: Optional[List["_models.ToolDefinition"]] = None, + stream: Optional[bool] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + max_prompt_tokens: Optional[int] = None, + max_completion_tokens: Optional[int] = None, + truncation_strategy: Optional["_models.TruncationObject"] = None, + tool_choice: Optional["_types.AssistantsApiToolChoiceOption"] = None, + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = None, + metadata: Optional[Dict[str, str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class CreateToolResourcesOptions(_model_base.Model): + """Request object. A set of resources that are used by the assistant's tools. The resources are + specific to the type of tool. For example, the ``code_interpreter`` tool requires a list of + file IDs, while the ``file_search`` tool requires a list of vector store IDs. + + :ivar code_interpreter: A list of file IDs made available to the ``code_interpreter`` tool. + There can be a maximum of 20 files associated with the tool. + :vartype code_interpreter: + ~azure.ai.resources.autogen.models.CreateCodeInterpreterToolResourceOptions + :ivar file_search: A list of vector stores or their IDs made available to the ``file_search`` + tool. Is either a [str] type or a [CreateFileSearchToolResourceVectorStoreOptions] type. + :vartype file_search: list[str] or + list[~azure.ai.resources.autogen.models.CreateFileSearchToolResourceVectorStoreOptions] + """ + + code_interpreter: Optional["_models.CreateCodeInterpreterToolResourceOptions"] = rest_field() + """A list of file IDs made available to the ``code_interpreter`` tool. There can be a maximum of + 20 files associated with the tool.""" + file_search: Optional["_types.CreateFileSearchToolResourceOptions"] = rest_field() + """A list of vector stores or their IDs made available to the ``file_search`` tool. Is either a + [str] type or a [CreateFileSearchToolResourceVectorStoreOptions] type.""" + + @overload + def __init__( + self, + *, + code_interpreter: Optional["_models.CreateCodeInterpreterToolResourceOptions"] = None, + file_search: Optional["_types.CreateFileSearchToolResourceOptions"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class DataCollector(_model_base.Model): + """Data collector definition. + + + :ivar collections: [Required] The collection configuration. Each collection has it own + configuration to collect model data and the name of collection can be arbitrary string. Model + data collector can be used for either payload logging or custom logging or both of them. + Collection request and response are reserved for payload logging, others are for custom + logging. Required. + :vartype collections: dict[str, ~azure.ai.resources.autogen.models.Collection] + :ivar request_logging: The request logging configuration for mdc, it includes advanced logging + settings for all collections. It's optional. + :vartype request_logging: ~azure.ai.resources.autogen.models.RequestLogging + :ivar rolling_rate: When model data is collected to blob storage, we need to roll the data to + different path to avoid logging all of them in a single blob file. If the rolling rate is hour, + all data will be collected in the blob path /yyyy/MM/dd/HH/. If it's day, all data will be + collected in blob path /yyyy/MM/dd/. The other benefit of rolling path is that model monitoring + ui is able to select a time range of data very quickly. Known values are: "Year", "Month", + "Day", "Hour", and "Minute". + :vartype rolling_rate: str or ~azure.ai.resources.autogen.models.RollingRateType + """ + + collections: Dict[str, "_models.Collection"] = rest_field() + """[Required] The collection configuration. Each collection has it own configuration to collect + model data and the name of collection can be arbitrary string. Model data collector can be used + for either payload logging or custom logging or both of them. Collection request and response + are reserved for payload logging, others are for custom logging. Required.""" + request_logging: Optional["_models.RequestLogging"] = rest_field(name="requestLogging") + """The request logging configuration for mdc, it includes advanced logging settings for all + collections. It's optional.""" + rolling_rate: Optional[Union[str, "_models.RollingRateType"]] = rest_field(name="rollingRate") + """When model data is collected to blob storage, we need to roll the data to different path to + avoid logging all of them in a single blob file. If the rolling rate is hour, all data will be + collected in the blob path /yyyy/MM/dd/HH/. If it's day, all data will be collected in blob + path /yyyy/MM/dd/. The other benefit of rolling path is that model monitoring ui is able to + select a time range of data very quickly. Known values are: \"Year\", \"Month\", \"Day\", + \"Hour\", and \"Minute\".""" + + @overload + def __init__( + self, + *, + collections: Dict[str, "_models.Collection"], + request_logging: Optional["_models.RequestLogging"] = None, + rolling_rate: Optional[Union[str, "_models.RollingRateType"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class DataContainer(AssetContainer): + """DataContainer Definition. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar description: The asset description text. + :vartype description: str + :ivar properties: The asset property dictionary. + :vartype properties: dict[str, str] + :ivar tags: Tag dictionary. Tags can be added, removed, and updated. + :vartype tags: dict[str, str] + :ivar is_archived: Is the asset archived?. + :vartype is_archived: bool + :ivar latest_version: The latest version inside this container. + :vartype latest_version: str + :ivar next_version: The next auto incremental version. + :vartype next_version: str + :ivar data_type: [Required] Specifies the type of data. Required. Known values are: "uri_file" + and "uri_folder". + :vartype data_type: str or ~azure.ai.resources.autogen.models.DataType + """ + + data_type: Union[str, "_models.DataType"] = rest_discriminator(name="dataType", visibility=["read", "create"]) + """[Required] Specifies the type of data. Required. Known values are: \"uri_file\" and + \"uri_folder\".""" + + @overload + def __init__( + self, + *, + description: Optional[str] = None, + properties: Optional[Dict[str, str]] = None, + tags: Optional[Dict[str, str]] = None, + is_archived: Optional[bool] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class DataPathAssetReference(AssetReferenceBase, discriminator="DataPath"): + """Reference to an asset via its path in a datastore. + + + :ivar datastore_id: ARM resource ID of the datastore where the asset is located. + :vartype datastore_id: str + :ivar path: The path of the file/directory in the datastore. + :vartype path: str + :ivar reference_type: [Required] Specifies the type of asset reference. Required. DataPath + :vartype reference_type: str or ~azure.ai.resources.autogen.models.DATA_PATH + """ + + datastore_id: Optional[str] = rest_field(name="datastoreId") + """ARM resource ID of the datastore where the asset is located.""" + path: Optional[str] = rest_field() + """The path of the file/directory in the datastore.""" + reference_type: Literal[ReferenceType.DATA_PATH] = rest_discriminator(name="referenceType") # type: ignore + """[Required] Specifies the type of asset reference. Required. DataPath""" + + @overload + def __init__( + self, + *, + datastore_id: Optional[str] = None, + path: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, reference_type=ReferenceType.DATA_PATH, **kwargs) + + +class Dataset(InputData, discriminator="dataset"): + """Dataset as source for evaluation. + + + :ivar id: Evaluation input data. Required. + :vartype id: str + :ivar type: Required. Default value is "dataset". + :vartype type: str + """ + + type: Literal["dataset"] = rest_discriminator(name="type") # type: ignore + """Required. Default value is \"dataset\".""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="dataset", **kwargs) + + +class DataVersionBase(AssetBase): + """DataVersionBase Definition. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + UriFileDataVersion, UriFolderDataVersion + + + :ivar description: The asset description text. + :vartype description: str + :ivar properties: The asset property dictionary. + :vartype properties: dict[str, str] + :ivar tags: Tag dictionary. Tags can be added, removed, and updated. + :vartype tags: dict[str, str] + :ivar is_anonymous: If the name version are system generated (anonymous registration). + :vartype is_anonymous: bool + :ivar is_archived: Is the asset archived?. + :vartype is_archived: bool + :ivar data_uri: [Required] Uri of the data. Example: + https://go.microsoft.com/fwlink/?linkid=2202330. Required. + :vartype data_uri: str + :ivar data_type: Data type. Required. Known values are: "uri_file" and "uri_folder". + :vartype data_type: str or ~azure.ai.resources.autogen.models.DataType + """ + + __mapping__: Dict[str, _model_base.Model] = {} + data_uri: str = rest_field(name="dataUri", visibility=["read", "create"]) + """[Required] Uri of the data. Example: https://go.microsoft.com/fwlink/?linkid=2202330. Required.""" + data_type: str = rest_discriminator(name="dataType") + """Data type. Required. Known values are: \"uri_file\" and \"uri_folder\".""" + + @overload + def __init__( + self, + *, + data_uri: str, + data_type: str, + description: Optional[str] = None, + properties: Optional[Dict[str, str]] = None, + tags: Optional[Dict[str, str]] = None, + is_anonymous: Optional[bool] = None, + is_archived: Optional[bool] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class DeploymentLogs(_model_base.Model): + """Deployment logs. + + :ivar content: The retrieved online deployment logs. + :vartype content: str + """ + + content: Optional[str] = rest_field() + """The retrieved online deployment logs.""" + + @overload + def __init__( + self, + *, + content: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class DeploymentLogsRequest(_model_base.Model): + """Request to get deployment logs. + + :ivar container_type: The type of container to retrieve logs from. Known values are: + "StorageInitializer" and "InferenceServer". + :vartype container_type: str or ~azure.ai.resources.autogen.models.ContainerType + :ivar tail: The maximum number of lines to tail. + :vartype tail: int + """ + + container_type: Optional[Union[str, "_models.ContainerType"]] = rest_field(name="containerType") + """The type of container to retrieve logs from. Known values are: \"StorageInitializer\" and + \"InferenceServer\".""" + tail: Optional[int] = rest_field() + """The maximum number of lines to tail.""" + + @overload + def __init__( + self, + *, + container_type: Optional[Union[str, "_models.ContainerType"]] = None, + tail: Optional[int] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ResourceConfiguration(_model_base.Model): + """Resource configuration. + + :ivar instance_count: Optional number of instances or nodes used by the compute target. + :vartype instance_count: int + :ivar instance_type: Optional type of VM used as supported by the compute target. + :vartype instance_type: str + :ivar properties: Additional properties bag. + :vartype properties: dict[str, dict[str, any]] + """ + + instance_count: Optional[int] = rest_field(name="instanceCount", visibility=["read", "create"]) + """Optional number of instances or nodes used by the compute target.""" + instance_type: Optional[str] = rest_field(name="instanceType", visibility=["read", "create"]) + """Optional type of VM used as supported by the compute target.""" + properties: Optional[Dict[str, Dict[str, Any]]] = rest_field(visibility=["read", "create"]) + """Additional properties bag.""" + + @overload + def __init__( + self, + *, + instance_count: Optional[int] = None, + instance_type: Optional[str] = None, + properties: Optional[Dict[str, Dict[str, Any]]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class DeploymentResourceConfiguration(ResourceConfiguration): + """Deployment resource configuration. + + :ivar instance_count: Optional number of instances or nodes used by the compute target. + :vartype instance_count: int + :ivar instance_type: Optional type of VM used as supported by the compute target. + :vartype instance_type: str + :ivar properties: Additional properties bag. + :vartype properties: dict[str, dict[str, any]] + """ + + @overload + def __init__( + self, + *, + instance_count: Optional[int] = None, + instance_type: Optional[str] = None, + properties: Optional[Dict[str, Dict[str, Any]]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class DestinationAsset(_model_base.Model): + """Publishing destination registry asset information. + + :ivar destination_name: Destination asset name. + :vartype destination_name: str + :ivar destination_version: Destination asset version. + :vartype destination_version: str + :ivar registry_name: Destination registry name. + :vartype registry_name: str + """ + + destination_name: Optional[str] = rest_field(name="destinationName") + """Destination asset name.""" + destination_version: Optional[str] = rest_field(name="destinationVersion") + """Destination asset version.""" + registry_name: Optional[str] = rest_field(name="registryName") + """Destination registry name.""" + + @overload + def __init__( + self, + *, + destination_name: Optional[str] = None, + destination_version: Optional[str] = None, + registry_name: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class EndpointAuthKeys(_model_base.Model): + """Keys for endpoint authentication. + + :ivar primary_key: The primary key. + :vartype primary_key: str + :ivar secondary_key: The secondary key. + :vartype secondary_key: str + """ + + primary_key: Optional[str] = rest_field(name="primaryKey", visibility=["read", "create"]) + """The primary key.""" + secondary_key: Optional[str] = rest_field(name="secondaryKey", visibility=["read", "create"]) + """The secondary key.""" + + @overload + def __init__( + self, + *, + primary_key: Optional[str] = None, + secondary_key: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class EndpointAuthToken(_model_base.Model): + """Service Token. + + :ivar access_token: Access token for endpoint authentication. + :vartype access_token: str + :ivar expiry_time_utc: Access token expiry time (UTC). + :vartype expiry_time_utc: int + :ivar refresh_after_time_utc: Refresh access token after time (UTC). + :vartype refresh_after_time_utc: int + :ivar token_type: Access token type. + :vartype token_type: str + """ + + access_token: Optional[str] = rest_field(name="accessToken") + """Access token for endpoint authentication.""" + expiry_time_utc: Optional[int] = rest_field(name="expiryTimeUtc") + """Access token expiry time (UTC).""" + refresh_after_time_utc: Optional[int] = rest_field(name="refreshAfterTimeUtc") + """Refresh access token after time (UTC).""" + token_type: Optional[str] = rest_field(name="tokenType") + """Access token type.""" + + @overload + def __init__( + self, + *, + access_token: Optional[str] = None, + expiry_time_utc: Optional[int] = None, + refresh_after_time_utc: Optional[int] = None, + token_type: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class Evaluation(_model_base.Model): + """Evaluation Definition. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar id: Identifier of the evaluation. + :vartype id: str + :ivar data: Data for evaluation. Required. + :vartype data: ~azure.ai.resources.autogen.models.InputData + :ivar display_name: Update stage to 'Archive' to archive the asset. Default is Development, + which means the asset is under development. + :vartype display_name: str + :ivar description: Description of the evaluation. It can be used to store additional + information about the evaluation and is mutable. + :vartype description: str + :ivar system_data: Metadata containing createdBy and modifiedBy information. + :vartype system_data: ~azure.ai.resources.autogen.models.SystemData + :ivar status: Status of the evaluation. It is set by service and is read-only. + :vartype status: str + :ivar tags: Evaluation's tags. Unlike properties, tags are fully mutable. + :vartype tags: dict[str, str] + :ivar properties: Evaluation's properties. Unlike tags, properties are add-only. Once added, a + property cannot be removed. + :vartype properties: dict[str, str] + :ivar evaluators: Evaluators to be used for the evaluation. Required. + :vartype evaluators: dict[str, ~azure.ai.resources.autogen.models.EvaluatorConfiguration] + :ivar evaluation_target: Evaluation Target. + :vartype evaluation_target: ~azure.ai.resources.autogen.models.EvaluationTarget + """ + + id: Optional[str] = rest_field() + """Identifier of the evaluation.""" + data: "_models.InputData" = rest_field() + """Data for evaluation. Required.""" + display_name: Optional[str] = rest_field(name="displayName") + """Update stage to 'Archive' to archive the asset. Default is Development, which means the asset + is under development.""" + description: Optional[str] = rest_field() + """Description of the evaluation. It can be used to store additional information about the + evaluation and is mutable.""" + system_data: Optional["_models.SystemData"] = rest_field(name="systemData", visibility=["read"]) + """Metadata containing createdBy and modifiedBy information.""" + status: Optional[str] = rest_field(visibility=["read"]) + """Status of the evaluation. It is set by service and is read-only.""" + tags: Optional[Dict[str, str]] = rest_field() + """Evaluation's tags. Unlike properties, tags are fully mutable.""" + properties: Optional[Dict[str, str]] = rest_field() + """Evaluation's properties. Unlike tags, properties are add-only. Once added, a property cannot be + removed.""" + evaluators: Dict[str, "_models.EvaluatorConfiguration"] = rest_field() + """Evaluators to be used for the evaluation. Required.""" + evaluation_target: Optional["_models.EvaluationTarget"] = rest_field(name="evaluationTarget") + """Evaluation Target.""" + + @overload + def __init__( + self, + *, + data: "_models.InputData", + evaluators: Dict[str, "_models.EvaluatorConfiguration"], + id: Optional[str] = None, # pylint: disable=redefined-builtin + display_name: Optional[str] = None, + description: Optional[str] = None, + tags: Optional[Dict[str, str]] = None, + properties: Optional[Dict[str, str]] = None, + evaluation_target: Optional["_models.EvaluationTarget"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class EvaluatorConfiguration(_model_base.Model): + """Evaluator Configuration. + + + :ivar id: Identifier of the evaluator. Required. + :vartype id: str + :ivar init_params: Initialization parameters of the evaluator. + :vartype init_params: dict[str, str] + :ivar data_mapping: Data parameters of the evaluator. + :vartype data_mapping: dict[str, str] + """ + + id: str = rest_field() + """Identifier of the evaluator. Required.""" + init_params: Optional[Dict[str, str]] = rest_field(name="initParams") + """Initialization parameters of the evaluator.""" + data_mapping: Optional[Dict[str, str]] = rest_field(name="dataMapping") + """Data parameters of the evaluator.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + init_params: Optional[Dict[str, str]] = None, + data_mapping: Optional[Dict[str, str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class FileDeletionStatus(_model_base.Model): + """A status response from a file deletion operation. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar id: The ID of the resource specified for deletion. Required. + :vartype id: str + :ivar deleted: A value indicating whether deletion was successful. Required. + :vartype deleted: bool + :ivar object: The object type, which is always 'file'. Required. Default value is "file". + :vartype object: str + """ + + id: str = rest_field() + """The ID of the resource specified for deletion. Required.""" + deleted: bool = rest_field() + """A value indicating whether deletion was successful. Required.""" + object: Literal["file"] = rest_field() + """The object type, which is always 'file'. Required. Default value is \"file\".""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + deleted: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["file"] = "file" + + +class FileListResponse(_model_base.Model): + """The response data from a file list operation. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar object: The object type, which is always 'list'. Required. Default value is "list". + :vartype object: str + :ivar data: The files returned for the request. Required. + :vartype data: list[~azure.ai.resources.autogen.models.OpenAIFile] + """ + + object: Literal["list"] = rest_field() + """The object type, which is always 'list'. Required. Default value is \"list\".""" + data: List["_models.OpenAIFile"] = rest_field() + """The files returned for the request. Required.""" + + @overload + def __init__( + self, + *, + data: List["_models.OpenAIFile"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["list"] = "list" + + +class FileSearchToolDefinition(ToolDefinition, discriminator="file_search"): + """The input definition information for a file search tool as used to configure an assistant. + + + :ivar type: The object type, which is always 'file_search'. Required. Default value is + "file_search". + :vartype type: str + :ivar file_search: Options overrides for the file search tool. + :vartype file_search: ~azure.ai.resources.autogen.models.FileSearchToolDefinitionDetails + """ + + type: Literal["file_search"] = rest_discriminator(name="type") # type: ignore + """The object type, which is always 'file_search'. Required. Default value is \"file_search\".""" + file_search: Optional["_models.FileSearchToolDefinitionDetails"] = rest_field() + """Options overrides for the file search tool.""" + + @overload + def __init__( + self, + *, + file_search: Optional["_models.FileSearchToolDefinitionDetails"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="file_search", **kwargs) + + +class FileSearchToolDefinitionDetails(_model_base.Model): + """Options overrides for the file search tool. + + :ivar max_num_results: The maximum number of results the file search tool should output. The + default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 + inclusive. Note that the file search tool may output fewer than ``max_num_results`` results. + See the file search tool documentation for more information. + :vartype max_num_results: int + """ + + max_num_results: Optional[int] = rest_field() + """The maximum number of results the file search tool should output. The default is 20 for gpt-4* + models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive. Note that the + file search tool may output fewer than ``max_num_results`` results. See the file search tool + documentation for more information.""" + + @overload + def __init__( + self, + *, + max_num_results: Optional[int] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class FileSearchToolResource(_model_base.Model): + """A set of resources that are used by the ``file_search`` tool. + + :ivar vector_store_ids: The ID of the vector store attached to this assistant. There can be a + maximum of 1 vector store attached to the assistant. + :vartype vector_store_ids: list[str] + """ + + vector_store_ids: Optional[List[str]] = rest_field() + """The ID of the vector store attached to this assistant. There can be a maximum of 1 vector store + attached to the assistant.""" + + @overload + def __init__( + self, + *, + vector_store_ids: Optional[List[str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class FlavorData(_model_base.Model): + """Flavor Data Definition. + + :ivar data: Model flavor-specific data. + :vartype data: dict[str, str] + """ + + data: Optional[Dict[str, str]] = rest_field() + """Model flavor-specific data.""" + + @overload + def __init__( + self, + *, + data: Optional[Dict[str, str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class FunctionDefinition(_model_base.Model): + """The input definition information for a function. + + + :ivar name: The name of the function to be called. Required. + :vartype name: str + :ivar description: A description of what the function does, used by the model to choose when + and how to call the function. + :vartype description: str + :ivar parameters: The parameters the functions accepts, described as a JSON Schema object. + Required. + :vartype parameters: any + """ + + name: str = rest_field() + """The name of the function to be called. Required.""" + description: Optional[str] = rest_field() + """A description of what the function does, used by the model to choose when and how to call the + function.""" + parameters: Any = rest_field() + """The parameters the functions accepts, described as a JSON Schema object. Required.""" + + @overload + def __init__( + self, + *, + name: str, + parameters: Any, + description: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class FunctionName(_model_base.Model): + """The function name that will be used, if using the ``function`` tool. + + + :ivar name: The name of the function to call. Required. + :vartype name: str + """ + + name: str = rest_field() + """The name of the function to call. Required.""" + + @overload + def __init__( + self, + *, + name: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class FunctionToolDefinition(ToolDefinition, discriminator="function"): + """The input definition information for a function tool as used to configure an assistant. + + + :ivar type: The object type, which is always 'function'. Required. Default value is "function". + :vartype type: str + :ivar function: The definition of the concrete function that the function tool should call. + Required. + :vartype function: ~azure.ai.resources.autogen.models.FunctionDefinition + """ + + type: Literal["function"] = rest_discriminator(name="type") # type: ignore + """The object type, which is always 'function'. Required. Default value is \"function\".""" + function: "_models.FunctionDefinition" = rest_field() + """The definition of the concrete function that the function tool should call. Required.""" + + @overload + def __init__( + self, + *, + function: "_models.FunctionDefinition", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="function", **kwargs) + + +class IdAssetReference(AssetReferenceBase, discriminator="Id"): + """Reference to an asset via its ARM resource ID. + + + :ivar asset_id: [Required] ARM resource ID of the asset. Required. + :vartype asset_id: str + :ivar reference_type: [Required] Specifies the type of asset reference. Required. Id + :vartype reference_type: str or ~azure.ai.resources.autogen.models.ID + """ + + asset_id: str = rest_field(name="assetId") + """[Required] ARM resource ID of the asset. Required.""" + reference_type: Literal[ReferenceType.ID] = rest_discriminator(name="referenceType") # type: ignore + """[Required] Specifies the type of asset reference. Required. Id""" + + @overload + def __init__( + self, + *, + asset_id: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, reference_type=ReferenceType.ID, **kwargs) + + +class Index(_model_base.Model): + """Index resource Definition. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar id: Fully qualified resource Id: + azureml://workspace/{workspaceName}/indexes/{name}/versions/{version} of the index. Required. + :vartype id: str + :ivar stage: Update stage to 'Archive' to archive the asset. Default is Development, which + means the asset is under development. + :vartype stage: str + :ivar description: Description information of the asset. + :vartype description: str + :ivar system_data: Metadata containing createdBy and modifiedBy information. + :vartype system_data: ~azure.ai.resources.autogen.models.SystemData + :ivar tags: Asset's tags. Unlike properties, tags are fully mutable. + :vartype tags: dict[str, str] + :ivar properties: Asset's properties. Unlike tags, properties are add-only. Once added, a + property cannot be removed. + :vartype properties: dict[str, str] + :ivar storage_uri: Default workspace blob storage Uri. Should work across storage types and + auth scenarios. Required. + :vartype storage_uri: str + """ + + id: str = rest_field(visibility=["read"]) + """Fully qualified resource Id: + azureml://workspace/{workspaceName}/indexes/{name}/versions/{version} of the index. Required.""" + stage: Optional[str] = rest_field() + """Update stage to 'Archive' to archive the asset. Default is Development, which means the asset + is under development.""" + description: Optional[str] = rest_field() + """Description information of the asset.""" + system_data: Optional["_models.SystemData"] = rest_field(name="systemData", visibility=["read"]) + """Metadata containing createdBy and modifiedBy information.""" + tags: Optional[Dict[str, str]] = rest_field() + """Asset's tags. Unlike properties, tags are fully mutable.""" + properties: Optional[Dict[str, str]] = rest_field() + """Asset's properties. Unlike tags, properties are add-only. Once added, a property cannot be + removed.""" + storage_uri: str = rest_field(name="storageUri") + """Default workspace blob storage Uri. Should work across storage types and auth scenarios. + Required.""" + + @overload + def __init__( + self, + *, + storage_uri: str, + stage: Optional[str] = None, + description: Optional[str] = None, + tags: Optional[Dict[str, str]] = None, + properties: Optional[Dict[str, str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageAttachment(_model_base.Model): + """This describes to which tools a file has been attached. + + + :ivar file_id: The ID of the file to attach to the message. Required. + :vartype file_id: str + :ivar tools: The tools to add to this file. Required. + :vartype tools: list[~azure.ai.resources.autogen.models.CodeInterpreterToolDefinition or + ~azure.ai.resources.autogen.models.FileSearchToolDefinition] + """ + + file_id: str = rest_field() + """The ID of the file to attach to the message. Required.""" + tools: List["_types.MessageAttachmentToolDefinition"] = rest_field() + """The tools to add to this file. Required.""" + + @overload + def __init__( + self, + *, + file_id: str, + tools: List["_types.MessageAttachmentToolDefinition"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageContent(_model_base.Model): + """An abstract representation of a single item of thread message content. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + MessageImageFileContent, MessageTextContent + + + :ivar type: The object type. Required. Default value is None. + :vartype type: str + """ + + __mapping__: Dict[str, _model_base.Model] = {} + type: str = rest_discriminator(name="type") + """The object type. Required. Default value is None.""" + + @overload + def __init__( + self, + *, + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageImageFileContent(MessageContent, discriminator="image_file"): + """A representation of image file content in a thread message. + + + :ivar type: The object type, which is always 'image_file'. Required. Default value is + "image_file". + :vartype type: str + :ivar image_file: The image file for this thread message content item. Required. + :vartype image_file: ~azure.ai.resources.autogen.models.MessageImageFileDetails + """ + + type: Literal["image_file"] = rest_discriminator(name="type") # type: ignore + """The object type, which is always 'image_file'. Required. Default value is \"image_file\".""" + image_file: "_models.MessageImageFileDetails" = rest_field() + """The image file for this thread message content item. Required.""" + + @overload + def __init__( + self, + *, + image_file: "_models.MessageImageFileDetails", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="image_file", **kwargs) + + +class MessageImageFileDetails(_model_base.Model): + """An image reference, as represented in thread message content. + + + :ivar file_id: The ID for the file associated with this image. Required. + :vartype file_id: str + """ + + file_id: str = rest_field() + """The ID for the file associated with this image. Required.""" + + @overload + def __init__( + self, + *, + file_id: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageIncompleteDetails(_model_base.Model): + """Information providing additional detail about a message entering an incomplete status. + + + :ivar reason: The provided reason describing why the message was marked as incomplete. + Required. Known values are: "content_filter", "max_tokens", "run_cancelled", "run_failed", and + "run_expired". + :vartype reason: str or ~azure.ai.resources.autogen.models.MessageIncompleteDetailsReason + """ + + reason: Union[str, "_models.MessageIncompleteDetailsReason"] = rest_field() + """The provided reason describing why the message was marked as incomplete. Required. Known values + are: \"content_filter\", \"max_tokens\", \"run_cancelled\", \"run_failed\", and + \"run_expired\".""" + + @overload + def __init__( + self, + *, + reason: Union[str, "_models.MessageIncompleteDetailsReason"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageTextAnnotation(_model_base.Model): + """An abstract representation of an annotation to text thread message content. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + MessageTextFileCitationAnnotation, MessageTextFilePathAnnotation + + + :ivar type: The object type. Required. Default value is None. + :vartype type: str + :ivar text: The textual content associated with this text annotation item. Required. + :vartype text: str + """ + + __mapping__: Dict[str, _model_base.Model] = {} + type: str = rest_discriminator(name="type") + """The object type. Required. Default value is None.""" + text: str = rest_field() + """The textual content associated with this text annotation item. Required.""" + + @overload + def __init__( + self, + *, + type: str, + text: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageTextContent(MessageContent, discriminator="text"): + """A representation of a textual item of thread message content. + + + :ivar type: The object type, which is always 'text'. Required. Default value is "text". + :vartype type: str + :ivar text: The text and associated annotations for this thread message content item. Required. + :vartype text: ~azure.ai.resources.autogen.models.MessageTextDetails + """ + + type: Literal["text"] = rest_discriminator(name="type") # type: ignore + """The object type, which is always 'text'. Required. Default value is \"text\".""" + text: "_models.MessageTextDetails" = rest_field() + """The text and associated annotations for this thread message content item. Required.""" + + @overload + def __init__( + self, + *, + text: "_models.MessageTextDetails", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="text", **kwargs) + + +class MessageTextDetails(_model_base.Model): + """The text and associated annotations for a single item of assistant thread message content. + + + :ivar value: The text data. Required. + :vartype value: str + :ivar annotations: A list of annotations associated with this text. Required. + :vartype annotations: list[~azure.ai.resources.autogen.models.MessageTextAnnotation] + """ + + value: str = rest_field() + """The text data. Required.""" + annotations: List["_models.MessageTextAnnotation"] = rest_field() + """A list of annotations associated with this text. Required.""" + + @overload + def __init__( + self, + *, + value: str, + annotations: List["_models.MessageTextAnnotation"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageTextFileCitationAnnotation(MessageTextAnnotation, discriminator="file_citation"): + """A citation within the message that points to a specific quote from a specific File associated + with the assistant or the message. Generated when the assistant uses the 'file_search' tool to + search files. + + + :ivar text: The textual content associated with this text annotation item. Required. + :vartype text: str + :ivar type: The object type, which is always 'file_citation'. Required. Default value is + "file_citation". + :vartype type: str + :ivar file_citation: A citation within the message that points to a specific quote from a + specific file. + Generated when the assistant uses the "file_search" tool to search files. Required. + :vartype file_citation: ~azure.ai.resources.autogen.models.MessageTextFileCitationDetails + :ivar start_index: The first text index associated with this text annotation. + :vartype start_index: int + :ivar end_index: The last text index associated with this text annotation. + :vartype end_index: int + """ + + type: Literal["file_citation"] = rest_discriminator(name="type") # type: ignore + """The object type, which is always 'file_citation'. Required. Default value is \"file_citation\".""" + file_citation: "_models.MessageTextFileCitationDetails" = rest_field() + """A citation within the message that points to a specific quote from a specific file. + Generated when the assistant uses the \"file_search\" tool to search files. Required.""" + start_index: Optional[int] = rest_field() + """The first text index associated with this text annotation.""" + end_index: Optional[int] = rest_field() + """The last text index associated with this text annotation.""" + + @overload + def __init__( + self, + *, + text: str, + file_citation: "_models.MessageTextFileCitationDetails", + start_index: Optional[int] = None, + end_index: Optional[int] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="file_citation", **kwargs) + + +class MessageTextFileCitationDetails(_model_base.Model): + """A representation of a file-based text citation, as used in a file-based annotation of text + thread message content. + + + :ivar file_id: The ID of the file associated with this citation. Required. + :vartype file_id: str + :ivar quote: The specific quote cited in the associated file. Required. + :vartype quote: str + """ + + file_id: str = rest_field() + """The ID of the file associated with this citation. Required.""" + quote: str = rest_field() + """The specific quote cited in the associated file. Required.""" + + @overload + def __init__( + self, + *, + file_id: str, + quote: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MessageTextFilePathAnnotation(MessageTextAnnotation, discriminator="file_path"): + """A citation within the message that points to a file located at a specific path. + + + :ivar text: The textual content associated with this text annotation item. Required. + :vartype text: str + :ivar type: The object type, which is always 'file_path'. Required. Default value is + "file_path". + :vartype type: str + :ivar file_path: A URL for the file that's generated when the assistant used the + code_interpreter tool to generate a file. Required. + :vartype file_path: ~azure.ai.resources.autogen.models.MessageTextFilePathDetails + :ivar start_index: The first text index associated with this text annotation. + :vartype start_index: int + :ivar end_index: The last text index associated with this text annotation. + :vartype end_index: int + """ + + type: Literal["file_path"] = rest_discriminator(name="type") # type: ignore + """The object type, which is always 'file_path'. Required. Default value is \"file_path\".""" + file_path: "_models.MessageTextFilePathDetails" = rest_field() + """A URL for the file that's generated when the assistant used the code_interpreter tool to + generate a file. Required.""" + start_index: Optional[int] = rest_field() + """The first text index associated with this text annotation.""" + end_index: Optional[int] = rest_field() + """The last text index associated with this text annotation.""" + + @overload + def __init__( + self, + *, + text: str, + file_path: "_models.MessageTextFilePathDetails", + start_index: Optional[int] = None, + end_index: Optional[int] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="file_path", **kwargs) + + +class MessageTextFilePathDetails(_model_base.Model): + """An encapsulation of an image file ID, as used by message image content. + + + :ivar file_id: The ID of the specific file that the citation is from. Required. + :vartype file_id: str + """ + + file_id: str = rest_field() + """The ID of the specific file that the citation is from. Required.""" + + @overload + def __init__( + self, + *, + file_id: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ModelContainer(AssetContainer): + """Model Container Definition. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + :ivar description: The asset description text. + :vartype description: str + :ivar properties: The asset property dictionary. + :vartype properties: dict[str, str] + :ivar tags: Tag dictionary. Tags can be added, removed, and updated. + :vartype tags: dict[str, str] + :ivar is_archived: Is the asset archived?. + :vartype is_archived: bool + :ivar latest_version: The latest version inside this container. + :vartype latest_version: str + :ivar next_version: The next auto incremental version. + :vartype next_version: str + :ivar provisioning_state: Provisioning state for the model container. Known values are: + "Succeeded", "Failed", "Canceled", "Creating", "Updating", and "Deleting". + :vartype provisioning_state: str or ~azure.ai.resources.autogen.models.AssetProvisioningState + """ + + provisioning_state: Optional[Union[str, "_models.AssetProvisioningState"]] = rest_field( + name="provisioningState", visibility=["read"] + ) + """Provisioning state for the model container. Known values are: \"Succeeded\", \"Failed\", + \"Canceled\", \"Creating\", \"Updating\", and \"Deleting\".""" + + @overload + def __init__( + self, + *, + description: Optional[str] = None, + properties: Optional[Dict[str, str]] = None, + tags: Optional[Dict[str, str]] = None, + is_archived: Optional[bool] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ModelVersion(AssetBase): + """Model Version Definition. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar description: The asset description text. + :vartype description: str + :ivar properties: The asset property dictionary. + :vartype properties: dict[str, str] + :ivar tags: Tag dictionary. Tags can be added, removed, and updated. + :vartype tags: dict[str, str] + :ivar is_anonymous: If the name version are system generated (anonymous registration). + :vartype is_anonymous: bool + :ivar is_archived: Is the asset archived?. + :vartype is_archived: bool + :ivar flavors: Mapping of model flavors to their properties. + :vartype flavors: dict[str, ~azure.ai.resources.autogen.models.FlavorData] + :ivar job_name: Name of the training job which produced this model. + :vartype job_name: str + :ivar model_type: The storage format for this entity. Used for NCD. + :vartype model_type: str + :ivar model_uri: The URI path to the model contents. + :vartype model_uri: str + :ivar provisioning_state: Provisioning state for the model version. Required. Known values are: + "Succeeded", "Failed", "Canceled", "Creating", "Updating", and "Deleting". + :vartype provisioning_state: str or ~azure.ai.resources.autogen.models.AssetProvisioningState + :ivar stage: Stage in the model lifecycle assigned to this model. + :vartype stage: str + """ + + flavors: Optional[Dict[str, "_models.FlavorData"]] = rest_field() + """Mapping of model flavors to their properties.""" + job_name: Optional[str] = rest_field(name="jobName") + """Name of the training job which produced this model.""" + model_type: Optional[str] = rest_field(name="modelType") + """The storage format for this entity. Used for NCD.""" + model_uri: Optional[str] = rest_field(name="modelUri") + """The URI path to the model contents.""" + provisioning_state: Union[str, "_models.AssetProvisioningState"] = rest_field( + name="provisioningState", visibility=["read"] + ) + """Provisioning state for the model version. Required. Known values are: \"Succeeded\", + \"Failed\", \"Canceled\", \"Creating\", \"Updating\", and \"Deleting\".""" + stage: Optional[str] = rest_field() + """Stage in the model lifecycle assigned to this model.""" + + @overload + def __init__( + self, + *, + description: Optional[str] = None, + properties: Optional[Dict[str, str]] = None, + tags: Optional[Dict[str, str]] = None, + is_anonymous: Optional[bool] = None, + is_archived: Optional[bool] = None, + flavors: Optional[Dict[str, "_models.FlavorData"]] = None, + job_name: Optional[str] = None, + model_type: Optional[str] = None, + model_uri: Optional[str] = None, + stage: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class OnlineDeployment(EndpointDeploymentBase): + """Online deployment definition. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + :ivar code_configuration: Code configuration for the endpoint deployment. + :vartype code_configuration: ~azure.ai.resources.autogen.models.CodeConfiguration + :ivar description: Description of the endpoint deployment. + :vartype description: str + :ivar environment_id: ARM resource ID or AssetId of the environment specification for the + endpoint deployment. + :vartype environment_id: str + :ivar environment_variables: Environment variables configuration for the deployment. + :vartype environment_variables: dict[str, str] + :ivar properties: Property dictionary. Properties can be added, but not removed or altered. + :vartype properties: dict[str, str] + :ivar app_insights_enabled: If true, enables Application Insights logging. + :vartype app_insights_enabled: bool + :ivar data_collector: The mdc configuration, we disable mdc when it's null. + :vartype data_collector: ~azure.ai.resources.autogen.models.DataCollector + :ivar egress_public_network_access: If Enabled, allow egress public network access. If + Disabled, this will create secure egress. Default: Enabled. Known values are: "Enabled" and + "Disabled". + :vartype egress_public_network_access: str or + ~azure.ai.resources.autogen.models.EgressPublicNetworkAccessType + :ivar instance_type: Compute instance type. + :vartype instance_type: str + :ivar liveness_probe: Liveness probe monitors the health of the container regularly. + :vartype liveness_probe: ~azure.ai.resources.autogen.models.ProbeSettings + :ivar model: The URI path to the model. + :vartype model: str + :ivar model_mount_path: The path to mount the model in custom container. + :vartype model_mount_path: str + :ivar provisioning_state: Provisioning state for the endpoint deployment. Known values are: + "Creating", "Deleting", "Scaling", "Updating", "Succeeded", "Failed", and "Canceled". + :vartype provisioning_state: str or + ~azure.ai.resources.autogen.models.DeploymentProvisioningState + :ivar readiness_probe: Readiness probe validates if the container is ready to serve traffic. + The properties and defaults are the same as liveness probe. + :vartype readiness_probe: ~azure.ai.resources.autogen.models.ProbeSettings + :ivar request_settings: Request settings for the deployment. + :vartype request_settings: ~azure.ai.resources.autogen.models.OnlineRequestSettings + :ivar scale_settings: Scale settings for the deployment. If it is null or not provided, it + defaults to TargetUtilizationScaleSettings for KubernetesOnlineDeployment and to + DefaultScaleSettings for ManagedOnlineDeployment. + :vartype scale_settings: ~azure.ai.resources.autogen.models.OnlineScaleSettings + """ + + app_insights_enabled: Optional[bool] = rest_field(name="appInsightsEnabled") + """If true, enables Application Insights logging.""" + data_collector: Optional["_models.DataCollector"] = rest_field(name="dataCollector") + """The mdc configuration, we disable mdc when it's null.""" + egress_public_network_access: Optional[Union[str, "_models.EgressPublicNetworkAccessType"]] = rest_field( + name="egressPublicNetworkAccess" + ) + """If Enabled, allow egress public network access. If Disabled, this will create secure egress. + Default: Enabled. Known values are: \"Enabled\" and \"Disabled\".""" + instance_type: Optional[str] = rest_field(name="instanceType", visibility=["read", "create"]) + """Compute instance type.""" + liveness_probe: Optional["_models.ProbeSettings"] = rest_field(name="livenessProbe") + """Liveness probe monitors the health of the container regularly.""" + model: Optional[str] = rest_field() + """The URI path to the model.""" + model_mount_path: Optional[str] = rest_field(name="modelMountPath") + """The path to mount the model in custom container.""" + provisioning_state: Optional[Union[str, "_models.DeploymentProvisioningState"]] = rest_field( + name="provisioningState", visibility=["read"] + ) + """Provisioning state for the endpoint deployment. Known values are: \"Creating\", \"Deleting\", + \"Scaling\", \"Updating\", \"Succeeded\", \"Failed\", and \"Canceled\".""" + readiness_probe: Optional["_models.ProbeSettings"] = rest_field(name="readinessProbe") + """Readiness probe validates if the container is ready to serve traffic. The properties and + defaults are the same as liveness probe.""" + request_settings: Optional["_models.OnlineRequestSettings"] = rest_field(name="requestSettings") + """Request settings for the deployment.""" + scale_settings: Optional["_models.OnlineScaleSettings"] = rest_field(name="scaleSettings") + """Scale settings for the deployment. If it is null or not provided, it defaults to + TargetUtilizationScaleSettings for KubernetesOnlineDeployment and to DefaultScaleSettings for + ManagedOnlineDeployment.""" + + @overload + def __init__( + self, + *, + code_configuration: Optional["_models.CodeConfiguration"] = None, + description: Optional[str] = None, + environment_id: Optional[str] = None, + environment_variables: Optional[Dict[str, str]] = None, + properties: Optional[Dict[str, str]] = None, + app_insights_enabled: Optional[bool] = None, + data_collector: Optional["_models.DataCollector"] = None, + egress_public_network_access: Optional[Union[str, "_models.EgressPublicNetworkAccessType"]] = None, + instance_type: Optional[str] = None, + liveness_probe: Optional["_models.ProbeSettings"] = None, + model: Optional[str] = None, + model_mount_path: Optional[str] = None, + readiness_probe: Optional["_models.ProbeSettings"] = None, + request_settings: Optional["_models.OnlineRequestSettings"] = None, + scale_settings: Optional["_models.OnlineScaleSettings"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class OnlineEndpoint(EndpointBase): + """Online endpoint definition. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar auth_mode: [Required] Use 'Key' for key based authentication and 'AMLToken' for Azure + Machine Learning token-based authentication. 'Key' doesn't expire but 'AMLToken' does. + Required. Known values are: "AMLToken", "Key", and "AADToken". + :vartype auth_mode: str or ~azure.ai.resources.autogen.models.EndpointAuthMode + :ivar description: Description of the inference endpoint. + :vartype description: str + :ivar keys_property: EndpointAuthKeys to set initially on an Endpoint. This property will + always be returned as null. AuthKey values must be retrieved using the ListKeys API. + :vartype keys_property: ~azure.ai.resources.autogen.models.EndpointAuthKeys + :ivar properties: Property dictionary. Properties can be added, but not removed or altered. + :vartype properties: dict[str, str] + :ivar scoring_uri: Endpoint URI. + :vartype scoring_uri: str + :ivar swagger_uri: Endpoint Swagger URI. + :vartype swagger_uri: str + :ivar compute: ARM resource ID of the compute if it exists. optional. + :vartype compute: str + :ivar mirror_traffic: Percentage of traffic to be mirrored to each deployment without using + returned scoring. Traffic values need to sum to utmost 50. + :vartype mirror_traffic: dict[str, int] + :ivar provisioning_state: Provisioning state for the endpoint. Known values are: "Creating", + "Deleting", "Succeeded", "Failed", "Updating", and "Canceled". + :vartype provisioning_state: str or + ~azure.ai.resources.autogen.models.EndpointProvisioningState + :ivar public_network_access: Set to 'Enabled' for endpoints that should allow public access + when Private Link is enabled. Known values are: "Enabled" and "Disabled". + :vartype public_network_access: str or + ~azure.ai.resources.autogen.models.PublicNetworkAccessType + :ivar traffic: Percentage of traffic from endpoint to divert to each deployment. Traffic values + need to sum to 100. + :vartype traffic: dict[str, int] + """ + + compute: Optional[str] = rest_field() + """ARM resource ID of the compute if it exists. optional.""" + mirror_traffic: Optional[Dict[str, int]] = rest_field(name="mirrorTraffic") + """Percentage of traffic to be mirrored to each deployment without using returned scoring. Traffic + values need to sum to utmost 50.""" + provisioning_state: Optional[Union[str, "_models.EndpointProvisioningState"]] = rest_field( + name="provisioningState", visibility=["read"] + ) + """Provisioning state for the endpoint. Known values are: \"Creating\", \"Deleting\", + \"Succeeded\", \"Failed\", \"Updating\", and \"Canceled\".""" + public_network_access: Optional[Union[str, "_models.PublicNetworkAccessType"]] = rest_field( + name="publicNetworkAccess" + ) + """Set to 'Enabled' for endpoints that should allow public access when Private Link is enabled. + Known values are: \"Enabled\" and \"Disabled\".""" + traffic: Optional[Dict[str, int]] = rest_field() + """Percentage of traffic from endpoint to divert to each deployment. Traffic values need to sum to + 100.""" + + @overload + def __init__( + self, + *, + auth_mode: Union[str, "_models.EndpointAuthMode"], + description: Optional[str] = None, + keys_property: Optional["_models.EndpointAuthKeys"] = None, + properties: Optional[Dict[str, str]] = None, + compute: Optional[str] = None, + mirror_traffic: Optional[Dict[str, int]] = None, + public_network_access: Optional[Union[str, "_models.PublicNetworkAccessType"]] = None, + traffic: Optional[Dict[str, int]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class OnlineRequestSettings(_model_base.Model): + """Online deployment scoring requests configuration. + + :ivar max_concurrent_requests_per_instance: The number of maximum concurrent requests per node + allowed per deployment. Defaults to 1. + :vartype max_concurrent_requests_per_instance: int + :ivar max_queue_wait: (Deprecated for Managed Online Endpoints) The maximum amount of time a + request will stay in the queue in ISO 8601 format. Defaults to 500ms. (Now increase + ``request_timeout_ms`` to account for any networking/queue delays). + :vartype max_queue_wait: ~datetime.timedelta + :ivar request_timeout: The scoring timeout in ISO 8601 format. Defaults to 5000ms. + :vartype request_timeout: ~datetime.timedelta + """ + + max_concurrent_requests_per_instance: Optional[int] = rest_field(name="maxConcurrentRequestsPerInstance") + """The number of maximum concurrent requests per node allowed per deployment. Defaults to 1.""" + max_queue_wait: Optional[datetime.timedelta] = rest_field(name="maxQueueWait") + """(Deprecated for Managed Online Endpoints) The maximum amount of time a request will stay in the + queue in ISO 8601 format. Defaults to 500ms. (Now increase ``request_timeout_ms`` to account + for any networking/queue delays).""" + request_timeout: Optional[datetime.timedelta] = rest_field(name="requestTimeout") + """The scoring timeout in ISO 8601 format. Defaults to 5000ms.""" + + @overload + def __init__( + self, + *, + max_concurrent_requests_per_instance: Optional[int] = None, + max_queue_wait: Optional[datetime.timedelta] = None, + request_timeout: Optional[datetime.timedelta] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class OnlineScaleSettings(_model_base.Model): + """Online deployment scaling configuration. + + + :ivar scale_type: The scale type for the deployment. Required. Known values are: "Default" and + "TargetUtilization". + :vartype scale_type: str or ~azure.ai.resources.autogen.models.ScaleType + """ + + scale_type: Union[str, "_models.ScaleType"] = rest_discriminator(name="scaleType") + """The scale type for the deployment. Required. Known values are: \"Default\" and + \"TargetUtilization\".""" + + @overload + def __init__( + self, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class OpenAIFile(_model_base.Model): + """Represents an assistant that can call the model and use tools. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar object: The object type, which is always 'file'. Required. Default value is "file". + :vartype object: str + :ivar id: The identifier, which can be referenced in API endpoints. Required. + :vartype id: str + :ivar bytes: The size of the file, in bytes. Required. + :vartype bytes: int + :ivar filename: The name of the file. Required. + :vartype filename: str + :ivar created_at: The Unix timestamp, in seconds, representing when this object was created. + Required. + :vartype created_at: ~datetime.datetime + :ivar purpose: The intended purpose of a file. Required. Known values are: "fine-tune", + "fine-tune-results", "assistants", "assistants_output", "batch", "batch_output", and "vision". + :vartype purpose: str or ~azure.ai.resources.autogen.models.FilePurpose + :ivar status: The state of the file. This field is available in Azure OpenAI only. Known values + are: "uploaded", "pending", "running", "processed", "error", "deleting", and "deleted". + :vartype status: str or ~azure.ai.resources.autogen.models.FileState + :ivar status_details: The error message with details in case processing of this file failed. + This field is available in Azure OpenAI only. + :vartype status_details: str + """ + + object: Literal["file"] = rest_field() + """The object type, which is always 'file'. Required. Default value is \"file\".""" + id: str = rest_field() + """The identifier, which can be referenced in API endpoints. Required.""" + bytes: int = rest_field() + """The size of the file, in bytes. Required.""" + filename: str = rest_field() + """The name of the file. Required.""" + created_at: datetime.datetime = rest_field(format="unix-timestamp") + """The Unix timestamp, in seconds, representing when this object was created. Required.""" + purpose: Union[str, "_models.FilePurpose"] = rest_field() + """The intended purpose of a file. Required. Known values are: \"fine-tune\", + \"fine-tune-results\", \"assistants\", \"assistants_output\", \"batch\", \"batch_output\", and + \"vision\".""" + status: Optional[Union[str, "_models.FileState"]] = rest_field() + """The state of the file. This field is available in Azure OpenAI only. Known values are: + \"uploaded\", \"pending\", \"running\", \"processed\", \"error\", \"deleting\", and + \"deleted\".""" + status_details: Optional[str] = rest_field() + """The error message with details in case processing of this file failed. This field is available + in Azure OpenAI only.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + bytes: int, + filename: str, + created_at: datetime.datetime, + purpose: Union[str, "_models.FilePurpose"], + status: Optional[Union[str, "_models.FileState"]] = None, + status_details: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["file"] = "file" + + +class OpenAIPageableListOfAssistant(_model_base.Model): + """The response data for a requested list of items. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar object: The object type, which is always list. Required. Default value is "list". + :vartype object: str + :ivar data: The requested list of items. Required. + :vartype data: list[~azure.ai.resources.autogen.models.Assistant] + :ivar first_id: The first ID represented in this list. Required. + :vartype first_id: str + :ivar last_id: The last ID represented in this list. Required. + :vartype last_id: str + :ivar has_more: A value indicating whether there are additional values available not captured + in this list. Required. + :vartype has_more: bool + """ + + object: Literal["list"] = rest_field() + """The object type, which is always list. Required. Default value is \"list\".""" + data: List["_models.Assistant"] = rest_field() + """The requested list of items. Required.""" + first_id: str = rest_field() + """The first ID represented in this list. Required.""" + last_id: str = rest_field() + """The last ID represented in this list. Required.""" + has_more: bool = rest_field() + """A value indicating whether there are additional values available not captured in this list. + Required.""" + + @overload + def __init__( + self, + *, + data: List["_models.Assistant"], + first_id: str, + last_id: str, + has_more: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["list"] = "list" + + +class OpenAIPageableListOfRunStep(_model_base.Model): + """The response data for a requested list of items. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar object: The object type, which is always list. Required. Default value is "list". + :vartype object: str + :ivar data: The requested list of items. Required. + :vartype data: list[~azure.ai.resources.autogen.models.RunStep] + :ivar first_id: The first ID represented in this list. Required. + :vartype first_id: str + :ivar last_id: The last ID represented in this list. Required. + :vartype last_id: str + :ivar has_more: A value indicating whether there are additional values available not captured + in this list. Required. + :vartype has_more: bool + """ + + object: Literal["list"] = rest_field() + """The object type, which is always list. Required. Default value is \"list\".""" + data: List["_models.RunStep"] = rest_field() + """The requested list of items. Required.""" + first_id: str = rest_field() + """The first ID represented in this list. Required.""" + last_id: str = rest_field() + """The last ID represented in this list. Required.""" + has_more: bool = rest_field() + """A value indicating whether there are additional values available not captured in this list. + Required.""" + + @overload + def __init__( + self, + *, + data: List["_models.RunStep"], + first_id: str, + last_id: str, + has_more: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["list"] = "list" + + +class OpenAIPageableListOfThreadMessage(_model_base.Model): + """The response data for a requested list of items. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar object: The object type, which is always list. Required. Default value is "list". + :vartype object: str + :ivar data: The requested list of items. Required. + :vartype data: list[~azure.ai.resources.autogen.models.ThreadMessage] + :ivar first_id: The first ID represented in this list. Required. + :vartype first_id: str + :ivar last_id: The last ID represented in this list. Required. + :vartype last_id: str + :ivar has_more: A value indicating whether there are additional values available not captured + in this list. Required. + :vartype has_more: bool + """ + + object: Literal["list"] = rest_field() + """The object type, which is always list. Required. Default value is \"list\".""" + data: List["_models.ThreadMessage"] = rest_field() + """The requested list of items. Required.""" + first_id: str = rest_field() + """The first ID represented in this list. Required.""" + last_id: str = rest_field() + """The last ID represented in this list. Required.""" + has_more: bool = rest_field() + """A value indicating whether there are additional values available not captured in this list. + Required.""" + + @overload + def __init__( + self, + *, + data: List["_models.ThreadMessage"], + first_id: str, + last_id: str, + has_more: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["list"] = "list" + + +class OpenAIPageableListOfThreadRun(_model_base.Model): + """The response data for a requested list of items. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar object: The object type, which is always list. Required. Default value is "list". + :vartype object: str + :ivar data: The requested list of items. Required. + :vartype data: list[~azure.ai.resources.autogen.models.ThreadRun] + :ivar first_id: The first ID represented in this list. Required. + :vartype first_id: str + :ivar last_id: The last ID represented in this list. Required. + :vartype last_id: str + :ivar has_more: A value indicating whether there are additional values available not captured + in this list. Required. + :vartype has_more: bool + """ + + object: Literal["list"] = rest_field() + """The object type, which is always list. Required. Default value is \"list\".""" + data: List["_models.ThreadRun"] = rest_field() + """The requested list of items. Required.""" + first_id: str = rest_field() + """The first ID represented in this list. Required.""" + last_id: str = rest_field() + """The last ID represented in this list. Required.""" + has_more: bool = rest_field() + """A value indicating whether there are additional values available not captured in this list. + Required.""" + + @overload + def __init__( + self, + *, + data: List["_models.ThreadRun"], + first_id: str, + last_id: str, + has_more: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["list"] = "list" + + +class OpenAIPageableListOfVectorStore(_model_base.Model): + """The response data for a requested list of items. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar object: The object type, which is always list. Required. Default value is "list". + :vartype object: str + :ivar data: The requested list of items. Required. + :vartype data: list[~azure.ai.resources.autogen.models.VectorStore] + :ivar first_id: The first ID represented in this list. Required. + :vartype first_id: str + :ivar last_id: The last ID represented in this list. Required. + :vartype last_id: str + :ivar has_more: A value indicating whether there are additional values available not captured + in this list. Required. + :vartype has_more: bool + """ + + object: Literal["list"] = rest_field() + """The object type, which is always list. Required. Default value is \"list\".""" + data: List["_models.VectorStore"] = rest_field() + """The requested list of items. Required.""" + first_id: str = rest_field() + """The first ID represented in this list. Required.""" + last_id: str = rest_field() + """The last ID represented in this list. Required.""" + has_more: bool = rest_field() + """A value indicating whether there are additional values available not captured in this list. + Required.""" + + @overload + def __init__( + self, + *, + data: List["_models.VectorStore"], + first_id: str, + last_id: str, + has_more: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["list"] = "list" + + +class OpenAIPageableListOfVectorStoreFile(_model_base.Model): + """The response data for a requested list of items. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar object: The object type, which is always list. Required. Default value is "list". + :vartype object: str + :ivar data: The requested list of items. Required. + :vartype data: list[~azure.ai.resources.autogen.models.VectorStoreFile] + :ivar first_id: The first ID represented in this list. Required. + :vartype first_id: str + :ivar last_id: The last ID represented in this list. Required. + :vartype last_id: str + :ivar has_more: A value indicating whether there are additional values available not captured + in this list. Required. + :vartype has_more: bool + """ + + object: Literal["list"] = rest_field() + """The object type, which is always list. Required. Default value is \"list\".""" + data: List["_models.VectorStoreFile"] = rest_field() + """The requested list of items. Required.""" + first_id: str = rest_field() + """The first ID represented in this list. Required.""" + last_id: str = rest_field() + """The last ID represented in this list. Required.""" + has_more: bool = rest_field() + """A value indicating whether there are additional values available not captured in this list. + Required.""" + + @overload + def __init__( + self, + *, + data: List["_models.VectorStoreFile"], + first_id: str, + last_id: str, + has_more: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["list"] = "list" + + +class OutputPathAssetReference(AssetReferenceBase, discriminator="OutputPath"): + """Reference to an asset via its path in a job output. + + + :ivar job_id: ARM resource ID of the job. + :vartype job_id: str + :ivar path: The path of the file/directory in the job output. + :vartype path: str + :ivar reference_type: [Required] Specifies the type of asset reference. Required. OutputPath + :vartype reference_type: str or ~azure.ai.resources.autogen.models.OUTPUT_PATH + """ + + job_id: Optional[str] = rest_field(name="jobId") + """ARM resource ID of the job.""" + path: Optional[str] = rest_field() + """The path of the file/directory in the job output.""" + reference_type: Literal[ReferenceType.OUTPUT_PATH] = rest_discriminator(name="referenceType") # type: ignore + """[Required] Specifies the type of asset reference. Required. OutputPath""" + + @overload + def __init__( + self, + *, + job_id: Optional[str] = None, + path: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, reference_type=ReferenceType.OUTPUT_PATH, **kwargs) + + +class PartialBatchDeployment(_model_base.Model): + """Mutable batch inference settings per deployment. + + :ivar description: Description of the endpoint deployment. + :vartype description: str + """ + + description: Optional[str] = rest_field() + """Description of the endpoint deployment.""" + + @overload + def __init__( + self, + *, + description: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties( + _model_base.Model +): # pylint: disable=name-too-long + """Strictly used in update requests. + + :ivar properties: Additional attributes of the entity. + :vartype properties: ~azure.ai.resources.autogen.models.PartialBatchDeployment + :ivar tags: Resource tags. + :vartype tags: dict[str, str] + """ + + properties: Optional["_models.PartialBatchDeployment"] = rest_field() + """Additional attributes of the entity.""" + tags: Optional[Dict[str, str]] = rest_field() + """Resource tags.""" + + @overload + def __init__( + self, + *, + properties: Optional["_models.PartialBatchDeployment"] = None, + tags: Optional[Dict[str, str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class PartialManagedServiceIdentity(_model_base.Model): + """Managed service identity (system assigned and/or user assigned identities). + + :ivar type: Managed service identity (system assigned and/or user assigned identities). Known + values are: "None", "SystemAssigned", "UserAssigned", and "SystemAssigned,UserAssigned". + :vartype type: str or ~azure.ai.resources.autogen.models.ManagedServiceIdentityType + :ivar user_assigned_identities: The set of user assigned identities associated with the + resource. The userAssignedIdentities dictionary keys will be ARM resource ids in the form: + '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{identityName}. # pylint: disable=line-too-long + The dictionary values can be empty objects ({}) in requests. + :vartype user_assigned_identities: dict[str, dict[str, any]] + """ + + type: Optional[Union[str, "_models.ManagedServiceIdentityType"]] = rest_field() + """Managed service identity (system assigned and/or user assigned identities). Known values are: + \"None\", \"SystemAssigned\", \"UserAssigned\", and \"SystemAssigned,UserAssigned\".""" + user_assigned_identities: Optional[Dict[str, Dict[str, Any]]] = rest_field(name="userAssignedIdentities") + """The set of user assigned identities associated with the resource. The userAssignedIdentities + dictionary keys will be ARM resource ids in the form: + '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{identityName}. # pylint: disable=line-too-long + The dictionary values can be empty objects ({}) in requests.""" + + @overload + def __init__( + self, + *, + type: Optional[Union[str, "_models.ManagedServiceIdentityType"]] = None, + user_assigned_identities: Optional[Dict[str, Dict[str, Any]]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class PartialMinimalTrackedResource(_model_base.Model): + """Strictly used in update requests. + + :ivar tags: Resource tags. + :vartype tags: dict[str, str] + """ + + tags: Optional[Dict[str, str]] = rest_field() + """Resource tags.""" + + @overload + def __init__( + self, + *, + tags: Optional[Dict[str, str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class PartialMinimalTrackedResourceWithIdentity(PartialMinimalTrackedResource): # pylint: disable=name-too-long + """Strictly used in update requests. + + :ivar tags: Resource tags. + :vartype tags: dict[str, str] + :ivar identity: Managed service identity (system assigned and/or user assigned identities). + :vartype identity: ~azure.ai.resources.autogen.models.PartialManagedServiceIdentity + """ + + identity: Optional["_models.PartialManagedServiceIdentity"] = rest_field() + """Managed service identity (system assigned and/or user assigned identities).""" + + @overload + def __init__( + self, + *, + tags: Optional[Dict[str, str]] = None, + identity: Optional["_models.PartialManagedServiceIdentity"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class PartialMinimalTrackedResourceWithSku(PartialMinimalTrackedResource): + """Strictly used in update requests. + + :ivar tags: Resource tags. + :vartype tags: dict[str, str] + :ivar sku: Sku details required for ARM contract for Autoscaling. + :vartype sku: ~azure.ai.resources.autogen.models.PartialSku + """ + + sku: Optional["_models.PartialSku"] = rest_field() + """Sku details required for ARM contract for Autoscaling.""" + + @overload + def __init__( + self, + *, + tags: Optional[Dict[str, str]] = None, + sku: Optional["_models.PartialSku"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class PartialSku(_model_base.Model): + """Common SKU definition. + + :ivar capacity: If the SKU supports scale out/in then the capacity integer should be included. + If scale out/in is not possible for the resource this may be omitted. + :vartype capacity: int + :ivar family: If the service has different generations of hardware, for the same SKU, then that + can be captured here. + :vartype family: str + :ivar name: The name of the SKU. Ex - P3. It is typically a letter+number code. + :vartype name: str + :ivar size: The SKU size. When the name field is the combination of tier and some other value, + this would be the standalone code. + :vartype size: str + :ivar tier: This field is required to be implemented by the Resource Provider if the service + has more than one tier, but is not required on a PUT. Known values are: "Free", "Basic", + "Standard", and "Premium". + :vartype tier: str or ~azure.ai.resources.autogen.models.SkuTier + """ + + capacity: Optional[int] = rest_field() + """If the SKU supports scale out/in then the capacity integer should be included. If scale out/in + is not possible for the resource this may be omitted.""" + family: Optional[str] = rest_field() + """If the service has different generations of hardware, for the same SKU, then that can be + captured here.""" + name: Optional[str] = rest_field() + """The name of the SKU. Ex - P3. It is typically a letter+number code.""" + size: Optional[str] = rest_field() + """The SKU size. When the name field is the combination of tier and some other value, this would + be the standalone code.""" + tier: Optional[Union[str, "_models.SkuTier"]] = rest_field() + """This field is required to be implemented by the Resource Provider if the service has more than + one tier, but is not required on a PUT. Known values are: \"Free\", \"Basic\", \"Standard\", + and \"Premium\".""" + + @overload + def __init__( + self, + *, + capacity: Optional[int] = None, + family: Optional[str] = None, + name: Optional[str] = None, + size: Optional[str] = None, + tier: Optional[Union[str, "_models.SkuTier"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ProbeSettings(_model_base.Model): + """Deployment container liveness/readiness probe configuration. + + :ivar failure_threshold: The number of failures to allow before returning an unhealthy status. + :vartype failure_threshold: int + :ivar initial_delay: The delay before the first probe in ISO 8601 format. + :vartype initial_delay: ~datetime.timedelta + :ivar period: The length of time between probes in ISO 8601 format. + :vartype period: ~datetime.timedelta + :ivar success_threshold: The number of successful probes before returning a healthy status. + :vartype success_threshold: int + :ivar timeout: The probe timeout in ISO 8601 format. + :vartype timeout: ~datetime.timedelta + """ + + failure_threshold: Optional[int] = rest_field(name="failureThreshold") + """The number of failures to allow before returning an unhealthy status.""" + initial_delay: Optional[datetime.timedelta] = rest_field(name="initialDelay") + """The delay before the first probe in ISO 8601 format.""" + period: Optional[datetime.timedelta] = rest_field() + """The length of time between probes in ISO 8601 format.""" + success_threshold: Optional[int] = rest_field(name="successThreshold") + """The number of successful probes before returning a healthy status.""" + timeout: Optional[datetime.timedelta] = rest_field() + """The probe timeout in ISO 8601 format.""" + + @overload + def __init__( + self, + *, + failure_threshold: Optional[int] = None, + initial_delay: Optional[datetime.timedelta] = None, + period: Optional[datetime.timedelta] = None, + success_threshold: Optional[int] = None, + timeout: Optional[datetime.timedelta] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RegenerateEndpointKeysRequest(_model_base.Model): + """Request to regenerate endpoint keys. + + All required parameters must be populated in order to send to server. + + :ivar key_type: [Required] Specification for which type of key to generate. Primary or + Secondary. Required. Known values are: "Primary" and "Secondary". + :vartype key_type: str or ~azure.ai.resources.autogen.models.KeyType + :ivar key_value: The value the key is set to. + :vartype key_value: str + """ + + key_type: Union[str, "_models.KeyType"] = rest_field(name="keyType") + """[Required] Specification for which type of key to generate. Primary or Secondary. Required. + Known values are: \"Primary\" and \"Secondary\".""" + key_value: Optional[str] = rest_field(name="keyValue") + """The value the key is set to.""" + + @overload + def __init__( + self, + *, + key_type: Union[str, "_models.KeyType"], + key_value: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RequestLogging(_model_base.Model): + """Definition for RequestLogging. + + :ivar capture_headers: For payload logging, we only collect payload by default. If customers + also want to collect the specified headers, they can set them in captureHeaders so that backend + will collect those headers along with payload. + :vartype capture_headers: list[str] + """ + + capture_headers: Optional[List[str]] = rest_field(name="captureHeaders") + """For payload logging, we only collect payload by default. If customers also want to collect the + specified headers, they can set them in captureHeaders so that backend will collect those + headers along with payload.""" + + @overload + def __init__( + self, + *, + capture_headers: Optional[List[str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RequiredAction(_model_base.Model): + """An abstract representation of a required action for an assistant thread run to continue. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + SubmitToolOutputsAction + + + :ivar type: The object type. Required. Default value is None. + :vartype type: str + """ + + __mapping__: Dict[str, _model_base.Model] = {} + type: str = rest_discriminator(name="type") + """The object type. Required. Default value is None.""" + + @overload + def __init__( + self, + *, + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RequiredToolCall(_model_base.Model): + """An abstract representation a a tool invocation needed by the model to continue a run. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + RequiredFunctionToolCall + + + :ivar type: The object type for the required tool call. Required. Default value is None. + :vartype type: str + :ivar id: The ID of the tool call. This ID must be referenced when submitting tool outputs. + Required. + :vartype id: str + """ + + __mapping__: Dict[str, _model_base.Model] = {} + type: str = rest_discriminator(name="type") + """The object type for the required tool call. Required. Default value is None.""" + id: str = rest_field() + """The ID of the tool call. This ID must be referenced when submitting tool outputs. Required.""" + + @overload + def __init__( + self, + *, + type: str, + id: str, # pylint: disable=redefined-builtin + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RequiredFunctionToolCall(RequiredToolCall, discriminator="function"): + """A representation of a requested call to a function tool, needed by the model to continue + evaluation of a run. + + + :ivar id: The ID of the tool call. This ID must be referenced when submitting tool outputs. + Required. + :vartype id: str + :ivar type: The object type of the required tool call. Always 'function' for function tools. + Required. Default value is "function". + :vartype type: str + :ivar function: Detailed information about the function to be executed by the tool that + includes name and arguments. Required. + :vartype function: ~azure.ai.resources.autogen.models.RequiredFunctionToolCallDetails + """ + + type: Literal["function"] = rest_discriminator(name="type") # type: ignore + """The object type of the required tool call. Always 'function' for function tools. Required. + Default value is \"function\".""" + function: "_models.RequiredFunctionToolCallDetails" = rest_field() + """Detailed information about the function to be executed by the tool that includes name and + arguments. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + function: "_models.RequiredFunctionToolCallDetails", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="function", **kwargs) + + +class RequiredFunctionToolCallDetails(_model_base.Model): + """The detailed information for a function invocation, as provided by a required action invoking a + function tool, that includes the name of and arguments to the function. + + + :ivar name: The name of the function. Required. + :vartype name: str + :ivar arguments: The arguments to use when invoking the named function, as provided by the + model. Arguments are presented as a JSON document that should be validated and parsed for + evaluation. Required. + :vartype arguments: str + """ + + name: str = rest_field() + """The name of the function. Required.""" + arguments: str = rest_field() + """The arguments to use when invoking the named function, as provided by the model. Arguments are + presented as a JSON document that should be validated and parsed for evaluation. Required.""" + + @overload + def __init__( + self, + *, + name: str, + arguments: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunCompletionUsage(_model_base.Model): + """Usage statistics related to the run. This value will be ``null`` if the run is not in a + terminal state (i.e. ``in_progress``\\ , ``queued``\\ , etc.). + + + :ivar completion_tokens: Number of completion tokens used over the course of the run. Required. + :vartype completion_tokens: int + :ivar prompt_tokens: Number of prompt tokens used over the course of the run. Required. + :vartype prompt_tokens: int + :ivar total_tokens: Total number of tokens used (prompt + completion). Required. + :vartype total_tokens: int + """ + + completion_tokens: int = rest_field() + """Number of completion tokens used over the course of the run. Required.""" + prompt_tokens: int = rest_field() + """Number of prompt tokens used over the course of the run. Required.""" + total_tokens: int = rest_field() + """Total number of tokens used (prompt + completion). Required.""" + + @overload + def __init__( + self, + *, + completion_tokens: int, + prompt_tokens: int, + total_tokens: int, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunError(_model_base.Model): + """The details of an error as encountered by an assistant thread run. + + + :ivar code: The status for the error. Required. + :vartype code: str + :ivar message: The human-readable text associated with the error. Required. + :vartype message: str + """ + + code: str = rest_field() + """The status for the error. Required.""" + message: str = rest_field() + """The human-readable text associated with the error. Required.""" + + @overload + def __init__( + self, + *, + code: str, + message: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStep(_model_base.Model): + """Detailed information about a single step of an assistant thread run. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar id: The identifier, which can be referenced in API endpoints. Required. + :vartype id: str + :ivar object: The object type, which is always 'thread.run.step'. Required. Default value is + "thread.run.step". + :vartype object: str + :ivar type: The type of run step, which can be either message_creation or tool_calls. Required. + Known values are: "message_creation" and "tool_calls". + :vartype type: str or ~azure.ai.resources.autogen.models.RunStepType + :ivar assistant_id: The ID of the assistant associated with the run step. Required. + :vartype assistant_id: str + :ivar thread_id: The ID of the thread that was run. Required. + :vartype thread_id: str + :ivar run_id: The ID of the run that this run step is a part of. Required. + :vartype run_id: str + :ivar status: The status of this run step. Required. Known values are: "in_progress", + "cancelled", "failed", "completed", and "expired". + :vartype status: str or ~azure.ai.resources.autogen.models.RunStepStatus + :ivar step_details: The details for this run step. Required. + :vartype step_details: ~azure.ai.resources.autogen.models.RunStepDetails + :ivar last_error: If applicable, information about the last error encountered by this run step. + Required. + :vartype last_error: ~azure.ai.resources.autogen.models.RunStepError + :ivar created_at: The Unix timestamp, in seconds, representing when this object was created. + Required. + :vartype created_at: ~datetime.datetime + :ivar expired_at: The Unix timestamp, in seconds, representing when this item expired. + Required. + :vartype expired_at: ~datetime.datetime + :ivar completed_at: The Unix timestamp, in seconds, representing when this completed. Required. + :vartype completed_at: ~datetime.datetime + :ivar cancelled_at: The Unix timestamp, in seconds, representing when this was cancelled. + Required. + :vartype cancelled_at: ~datetime.datetime + :ivar failed_at: The Unix timestamp, in seconds, representing when this failed. Required. + :vartype failed_at: ~datetime.datetime + :ivar usage: Usage statistics related to the run step. This value will be ``null`` while the + run step's status is ``in_progress``. + :vartype usage: ~azure.ai.resources.autogen.models.RunStepCompletionUsage + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required. + :vartype metadata: dict[str, str] + """ + + id: str = rest_field() + """The identifier, which can be referenced in API endpoints. Required.""" + object: Literal["thread.run.step"] = rest_field() + """The object type, which is always 'thread.run.step'. Required. Default value is + \"thread.run.step\".""" + type: Union[str, "_models.RunStepType"] = rest_field() + """The type of run step, which can be either message_creation or tool_calls. Required. Known + values are: \"message_creation\" and \"tool_calls\".""" + assistant_id: str = rest_field() + """The ID of the assistant associated with the run step. Required.""" + thread_id: str = rest_field() + """The ID of the thread that was run. Required.""" + run_id: str = rest_field() + """The ID of the run that this run step is a part of. Required.""" + status: Union[str, "_models.RunStepStatus"] = rest_field() + """The status of this run step. Required. Known values are: \"in_progress\", \"cancelled\", + \"failed\", \"completed\", and \"expired\".""" + step_details: "_models.RunStepDetails" = rest_field() + """The details for this run step. Required.""" + last_error: "_models.RunStepError" = rest_field() + """If applicable, information about the last error encountered by this run step. Required.""" + created_at: datetime.datetime = rest_field(format="unix-timestamp") + """The Unix timestamp, in seconds, representing when this object was created. Required.""" + expired_at: datetime.datetime = rest_field(format="unix-timestamp") + """The Unix timestamp, in seconds, representing when this item expired. Required.""" + completed_at: datetime.datetime = rest_field(format="unix-timestamp") + """The Unix timestamp, in seconds, representing when this completed. Required.""" + cancelled_at: datetime.datetime = rest_field(format="unix-timestamp") + """The Unix timestamp, in seconds, representing when this was cancelled. Required.""" + failed_at: datetime.datetime = rest_field(format="unix-timestamp") + """The Unix timestamp, in seconds, representing when this failed. Required.""" + usage: Optional["_models.RunStepCompletionUsage"] = rest_field() + """Usage statistics related to the run step. This value will be ``null`` while the run step's + status is ``in_progress``.""" + metadata: Dict[str, str] = rest_field() + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + type: Union[str, "_models.RunStepType"], + assistant_id: str, + thread_id: str, + run_id: str, + status: Union[str, "_models.RunStepStatus"], + step_details: "_models.RunStepDetails", + last_error: "_models.RunStepError", + created_at: datetime.datetime, + expired_at: datetime.datetime, + completed_at: datetime.datetime, + cancelled_at: datetime.datetime, + failed_at: datetime.datetime, + metadata: Dict[str, str], + usage: Optional["_models.RunStepCompletionUsage"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["thread.run.step"] = "thread.run.step" + + +class RunStepCodeInterpreterToolCallOutput(_model_base.Model): + """An abstract representation of an emitted output from a code interpreter tool. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + RunStepCodeInterpreterImageOutput, RunStepCodeInterpreterLogOutput + + + :ivar type: The object type. Required. Default value is None. + :vartype type: str + """ + + __mapping__: Dict[str, _model_base.Model] = {} + type: str = rest_discriminator(name="type") + """The object type. Required. Default value is None.""" + + @overload + def __init__( + self, + *, + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepCodeInterpreterImageOutput(RunStepCodeInterpreterToolCallOutput, discriminator="image"): + """A representation of an image output emitted by a code interpreter tool in response to a tool + call by the model. + + + :ivar type: The object type, which is always 'image'. Required. Default value is "image". + :vartype type: str + :ivar image: Referential information for the image associated with this output. Required. + :vartype image: ~azure.ai.resources.autogen.models.RunStepCodeInterpreterImageReference + """ + + type: Literal["image"] = rest_discriminator(name="type") # type: ignore + """The object type, which is always 'image'. Required. Default value is \"image\".""" + image: "_models.RunStepCodeInterpreterImageReference" = rest_field() + """Referential information for the image associated with this output. Required.""" + + @overload + def __init__( + self, + *, + image: "_models.RunStepCodeInterpreterImageReference", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="image", **kwargs) + + +class RunStepCodeInterpreterImageReference(_model_base.Model): + """An image reference emitted by a code interpreter tool in response to a tool call by the model. + + + :ivar file_id: The ID of the file associated with this image. Required. + :vartype file_id: str + """ + + file_id: str = rest_field() + """The ID of the file associated with this image. Required.""" + + @overload + def __init__( + self, + *, + file_id: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepCodeInterpreterLogOutput(RunStepCodeInterpreterToolCallOutput, discriminator="logs"): + """A representation of a log output emitted by a code interpreter tool in response to a tool call + by the model. + + + :ivar type: The object type, which is always 'logs'. Required. Default value is "logs". + :vartype type: str + :ivar logs: The serialized log output emitted by the code interpreter. Required. + :vartype logs: str + """ + + type: Literal["logs"] = rest_discriminator(name="type") # type: ignore + """The object type, which is always 'logs'. Required. Default value is \"logs\".""" + logs: str = rest_field() + """The serialized log output emitted by the code interpreter. Required.""" + + @overload + def __init__( + self, + *, + logs: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="logs", **kwargs) + + +class RunStepToolCall(_model_base.Model): + """An abstract representation of a detailed tool call as recorded within a run step for an + existing run. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + RunStepCodeInterpreterToolCall, RunStepFileSearchToolCall, RunStepFunctionToolCall + + + :ivar type: The object type. Required. Default value is None. + :vartype type: str + :ivar id: The ID of the tool call. This ID must be referenced when you submit tool outputs. + Required. + :vartype id: str + """ + + __mapping__: Dict[str, _model_base.Model] = {} + type: str = rest_discriminator(name="type") + """The object type. Required. Default value is None.""" + id: str = rest_field() + """The ID of the tool call. This ID must be referenced when you submit tool outputs. Required.""" + + @overload + def __init__( + self, + *, + type: str, + id: str, # pylint: disable=redefined-builtin + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepCodeInterpreterToolCall(RunStepToolCall, discriminator="code_interpreter"): + """A record of a call to a code interpreter tool, issued by the model in evaluation of a defined + tool, that represents inputs and outputs consumed and emitted by the code interpreter. + + + :ivar id: The ID of the tool call. This ID must be referenced when you submit tool outputs. + Required. + :vartype id: str + :ivar type: The object type, which is always 'code_interpreter'. Required. Default value is + "code_interpreter". + :vartype type: str + :ivar code_interpreter: The details of the tool call to the code interpreter tool. Required. + :vartype code_interpreter: + ~azure.ai.resources.autogen.models.RunStepCodeInterpreterToolCallDetails + """ + + type: Literal["code_interpreter"] = rest_discriminator(name="type") # type: ignore + """The object type, which is always 'code_interpreter'. Required. Default value is + \"code_interpreter\".""" + code_interpreter: "_models.RunStepCodeInterpreterToolCallDetails" = rest_field() + """The details of the tool call to the code interpreter tool. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + code_interpreter: "_models.RunStepCodeInterpreterToolCallDetails", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="code_interpreter", **kwargs) + + +class RunStepCodeInterpreterToolCallDetails(_model_base.Model): + """The detailed information about a code interpreter invocation by the model. + + + :ivar input: The input provided by the model to the code interpreter tool. Required. + :vartype input: str + :ivar outputs: The outputs produced by the code interpreter tool back to the model in response + to the tool call. Required. + :vartype outputs: list[~azure.ai.resources.autogen.models.RunStepCodeInterpreterToolCallOutput] + """ + + input: str = rest_field() + """The input provided by the model to the code interpreter tool. Required.""" + outputs: List["_models.RunStepCodeInterpreterToolCallOutput"] = rest_field() + """The outputs produced by the code interpreter tool back to the model in response to the tool + call. Required.""" + + @overload + def __init__( + self, + *, + input: str, + outputs: List["_models.RunStepCodeInterpreterToolCallOutput"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepCompletionUsage(_model_base.Model): + """Usage statistics related to the run step. + + + :ivar completion_tokens: Number of completion tokens used over the course of the run step. + Required. + :vartype completion_tokens: int + :ivar prompt_tokens: Number of prompt tokens used over the course of the run step. Required. + :vartype prompt_tokens: int + :ivar total_tokens: Total number of tokens used (prompt + completion). Required. + :vartype total_tokens: int + """ + + completion_tokens: int = rest_field() + """Number of completion tokens used over the course of the run step. Required.""" + prompt_tokens: int = rest_field() + """Number of prompt tokens used over the course of the run step. Required.""" + total_tokens: int = rest_field() + """Total number of tokens used (prompt + completion). Required.""" + + @overload + def __init__( + self, + *, + completion_tokens: int, + prompt_tokens: int, + total_tokens: int, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepDetails(_model_base.Model): + """An abstract representation of the details for a run step. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + RunStepMessageCreationDetails, RunStepToolCallDetails + + + :ivar type: The object type. Required. Known values are: "message_creation" and "tool_calls". + :vartype type: str or ~azure.ai.resources.autogen.models.RunStepType + """ + + __mapping__: Dict[str, _model_base.Model] = {} + type: str = rest_discriminator(name="type") + """The object type. Required. Known values are: \"message_creation\" and \"tool_calls\".""" + + @overload + def __init__( + self, + *, + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepError(_model_base.Model): + """The error information associated with a failed run step. + + + :ivar code: The error code for this error. Required. Known values are: "server_error" and + "rate_limit_exceeded". + :vartype code: str or ~azure.ai.resources.autogen.models.RunStepErrorCode + :ivar message: The human-readable text associated with this error. Required. + :vartype message: str + """ + + code: Union[str, "_models.RunStepErrorCode"] = rest_field() + """The error code for this error. Required. Known values are: \"server_error\" and + \"rate_limit_exceeded\".""" + message: str = rest_field() + """The human-readable text associated with this error. Required.""" + + @overload + def __init__( + self, + *, + code: Union[str, "_models.RunStepErrorCode"], + message: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepFileSearchToolCall(RunStepToolCall, discriminator="file_search"): + """A record of a call to a file search tool, issued by the model in evaluation of a defined tool, + that represents executed file search. + + + :ivar id: The ID of the tool call. This ID must be referenced when you submit tool outputs. + Required. + :vartype id: str + :ivar type: The object type, which is always 'file_search'. Required. Default value is + "file_search". + :vartype type: str + :ivar file_search: Reserved for future use. Required. + :vartype file_search: dict[str, str] + """ + + type: Literal["file_search"] = rest_discriminator(name="type") # type: ignore + """The object type, which is always 'file_search'. Required. Default value is \"file_search\".""" + file_search: Dict[str, str] = rest_field() + """Reserved for future use. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + file_search: Dict[str, str], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="file_search", **kwargs) + + +class RunStepFunctionToolCall(RunStepToolCall, discriminator="function"): + """A record of a call to a function tool, issued by the model in evaluation of a defined tool, + that represents the inputs and output consumed and emitted by the specified function. + + + :ivar id: The ID of the tool call. This ID must be referenced when you submit tool outputs. + Required. + :vartype id: str + :ivar type: The object type, which is always 'function'. Required. Default value is "function". + :vartype type: str + :ivar function: The detailed information about the function called by the model. Required. + :vartype function: ~azure.ai.resources.autogen.models.RunStepFunctionToolCallDetails + """ + + type: Literal["function"] = rest_discriminator(name="type") # type: ignore + """The object type, which is always 'function'. Required. Default value is \"function\".""" + function: "_models.RunStepFunctionToolCallDetails" = rest_field() + """The detailed information about the function called by the model. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + function: "_models.RunStepFunctionToolCallDetails", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="function", **kwargs) + + +class RunStepFunctionToolCallDetails(_model_base.Model): + """The detailed information about the function called by the model. + + + :ivar name: The name of the function. Required. + :vartype name: str + :ivar arguments: The arguments that the model requires are provided to the named function. + Required. + :vartype arguments: str + :ivar output: The output of the function, only populated for function calls that have already + have had their outputs submitted. Required. + :vartype output: str + """ + + name: str = rest_field() + """The name of the function. Required.""" + arguments: str = rest_field() + """The arguments that the model requires are provided to the named function. Required.""" + output: str = rest_field() + """The output of the function, only populated for function calls that have already have had their + outputs submitted. Required.""" + + @overload + def __init__( + self, + *, + name: str, + arguments: str, + output: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepMessageCreationDetails(RunStepDetails, discriminator="message_creation"): + """The detailed information associated with a message creation run step. + + + :ivar type: The object type, which is always 'message_creation'. Required. Represents a run + step to create a message. + :vartype type: str or ~azure.ai.resources.autogen.models.MESSAGE_CREATION + :ivar message_creation: Information about the message creation associated with this run step. + Required. + :vartype message_creation: ~azure.ai.resources.autogen.models.RunStepMessageCreationReference + """ + + type: Literal[RunStepType.MESSAGE_CREATION] = rest_discriminator(name="type") # type: ignore + """The object type, which is always 'message_creation'. Required. Represents a run step to create + a message.""" + message_creation: "_models.RunStepMessageCreationReference" = rest_field() + """Information about the message creation associated with this run step. Required.""" + + @overload + def __init__( + self, + *, + message_creation: "_models.RunStepMessageCreationReference", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type=RunStepType.MESSAGE_CREATION, **kwargs) + + +class RunStepMessageCreationReference(_model_base.Model): + """The details of a message created as a part of a run step. + + + :ivar message_id: The ID of the message created by this run step. Required. + :vartype message_id: str + """ + + message_id: str = rest_field() + """The ID of the message created by this run step. Required.""" + + @overload + def __init__( + self, + *, + message_id: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class RunStepToolCallDetails(RunStepDetails, discriminator="tool_calls"): + """The detailed information associated with a run step calling tools. + + + :ivar type: The object type, which is always 'tool_calls'. Required. Represents a run step that + calls tools. + :vartype type: str or ~azure.ai.resources.autogen.models.TOOL_CALLS + :ivar tool_calls: A list of tool call details for this run step. Required. + :vartype tool_calls: list[~azure.ai.resources.autogen.models.RunStepToolCall] + """ + + type: Literal[RunStepType.TOOL_CALLS] = rest_discriminator(name="type") # type: ignore + """The object type, which is always 'tool_calls'. Required. Represents a run step that calls + tools.""" + tool_calls: List["_models.RunStepToolCall"] = rest_field() + """A list of tool call details for this run step. Required.""" + + @overload + def __init__( + self, + *, + tool_calls: List["_models.RunStepToolCall"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type=RunStepType.TOOL_CALLS, **kwargs) + + +class SasCredential(BaseCredential, discriminator="SAS"): + """SAS Credential definition. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar name: Credential name. Required. + :vartype name: str + :ivar sas_token: SAS Token. Required. + :vartype sas_token: str + :ivar type: Required. + :vartype type: str or ~azure.ai.resources.autogen.models.SAS + """ + + sas_token: str = rest_field(name="sasToken", visibility=["read"]) + """SAS Token. Required.""" + type: Literal[CredentialType.SAS] = rest_discriminator(name="type") # type: ignore + """Required.""" + + @overload + def __init__( + self, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type=CredentialType.SAS, **kwargs) + + +class SkuCapacity(_model_base.Model): + """SKU capacity information. + + :ivar default: Gets or sets the default capacity. + :vartype default: int + :ivar maximum: Gets or sets the maximum. + :vartype maximum: int + :ivar minimum: Gets or sets the minimum. + :vartype minimum: int + :ivar scale_type: Gets or sets the type of the scale. Known values are: "Automatic", "Manual", + and "None". + :vartype scale_type: str or ~azure.ai.resources.autogen.models.SkuScaleType + """ + + default: Optional[int] = rest_field() + """Gets or sets the default capacity.""" + maximum: Optional[int] = rest_field() + """Gets or sets the maximum.""" + minimum: Optional[int] = rest_field() + """Gets or sets the minimum.""" + scale_type: Optional[Union[str, "_models.SkuScaleType"]] = rest_field(name="scaleType") + """Gets or sets the type of the scale. Known values are: \"Automatic\", \"Manual\", and \"None\".""" + + @overload + def __init__( + self, + *, + default: Optional[int] = None, + maximum: Optional[int] = None, + minimum: Optional[int] = None, + scale_type: Optional[Union[str, "_models.SkuScaleType"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class SkuResource(_model_base.Model): + """Fulfills ARM Contract requirement to list all available SKUS for a resource. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + :ivar capacity: Gets or sets the Sku Capacity. + :vartype capacity: ~azure.ai.resources.autogen.models.SkuCapacity + :ivar resource_type: The resource type name. + :vartype resource_type: str + :ivar sku: Gets or sets the Sku. + :vartype sku: ~azure.ai.resources.autogen.models.SkuSetting + """ + + capacity: Optional["_models.SkuCapacity"] = rest_field() + """Gets or sets the Sku Capacity.""" + resource_type: Optional[str] = rest_field(name="resourceType", visibility=["read"]) + """The resource type name.""" + sku: Optional["_models.SkuSetting"] = rest_field() + """Gets or sets the Sku.""" + + @overload + def __init__( + self, + *, + capacity: Optional["_models.SkuCapacity"] = None, + sku: Optional["_models.SkuSetting"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class SkuSetting(_model_base.Model): + """SkuSetting fulfills the need for stripped down SKU info in ARM contract. + + + :ivar name: [Required] The name of the SKU. Ex - P3. It is typically a letter+number code. + Required. + :vartype name: str + :ivar tier: This field is required to be implemented by the Resource Provider if the service + has more than one tier, but is not required on a PUT. Known values are: "Free", "Basic", + "Standard", and "Premium". + :vartype tier: str or ~azure.ai.resources.autogen.models.SkuTier + """ + + name: str = rest_field() + """[Required] The name of the SKU. Ex - P3. It is typically a letter+number code. Required.""" + tier: Optional[Union[str, "_models.SkuTier"]] = rest_field() + """This field is required to be implemented by the Resource Provider if the service has more than + one tier, but is not required on a PUT. Known values are: \"Free\", \"Basic\", \"Standard\", + and \"Premium\".""" + + @overload + def __init__( + self, + *, + name: str, + tier: Optional[Union[str, "_models.SkuTier"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class SubmitToolOutputsAction(RequiredAction, discriminator="submit_tool_outputs"): + """The details for required tool calls that must be submitted for an assistant thread run to + continue. + + + :ivar type: The object type, which is always 'submit_tool_outputs'. Required. Default value is + "submit_tool_outputs". + :vartype type: str + :ivar submit_tool_outputs: The details describing tools that should be called to submit tool + outputs. Required. + :vartype submit_tool_outputs: ~azure.ai.resources.autogen.models.SubmitToolOutputsDetails + """ + + type: Literal["submit_tool_outputs"] = rest_discriminator(name="type") # type: ignore + """The object type, which is always 'submit_tool_outputs'. Required. Default value is + \"submit_tool_outputs\".""" + submit_tool_outputs: "_models.SubmitToolOutputsDetails" = rest_field() + """The details describing tools that should be called to submit tool outputs. Required.""" + + @overload + def __init__( + self, + *, + submit_tool_outputs: "_models.SubmitToolOutputsDetails", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="submit_tool_outputs", **kwargs) + + +class SubmitToolOutputsDetails(_model_base.Model): + """The details describing tools that should be called to submit tool outputs. + + + :ivar tool_calls: The list of tool calls that must be resolved for the assistant thread run to + continue. Required. + :vartype tool_calls: list[~azure.ai.resources.autogen.models.RequiredToolCall] + """ + + tool_calls: List["_models.RequiredToolCall"] = rest_field() + """The list of tool calls that must be resolved for the assistant thread run to continue. + Required.""" + + @overload + def __init__( + self, + *, + tool_calls: List["_models.RequiredToolCall"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class SystemData(_model_base.Model): + """Metadata pertaining to creation and last modification of the resource. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + :ivar created_at: The timestamp the resource was created at. + :vartype created_at: ~datetime.datetime + :ivar created_by: The identity that created the resource. + :vartype created_by: str + :ivar created_by_type: The identity type that created the resource. + :vartype created_by_type: str + :ivar last_modified_at: The timestamp of resource last modification (UTC). + :vartype last_modified_at: ~datetime.datetime + """ + + created_at: Optional[datetime.datetime] = rest_field(name="createdAt", visibility=["read"], format="rfc3339") + """The timestamp the resource was created at.""" + created_by: Optional[str] = rest_field(name="createdBy", visibility=["read"]) + """The identity that created the resource.""" + created_by_type: Optional[str] = rest_field(name="createdByType", visibility=["read"]) + """The identity type that created the resource.""" + last_modified_at: Optional[datetime.datetime] = rest_field( + name="lastModifiedAt", visibility=["read"], format="rfc3339" + ) + """The timestamp of resource last modification (UTC).""" + + +class ThreadDeletionStatus(_model_base.Model): + """The status of a thread deletion operation. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar id: The ID of the resource specified for deletion. Required. + :vartype id: str + :ivar deleted: A value indicating whether deletion was successful. Required. + :vartype deleted: bool + :ivar object: The object type, which is always 'thread.deleted'. Required. Default value is + "thread.deleted". + :vartype object: str + """ + + id: str = rest_field() + """The ID of the resource specified for deletion. Required.""" + deleted: bool = rest_field() + """A value indicating whether deletion was successful. Required.""" + object: Literal["thread.deleted"] = rest_field() + """The object type, which is always 'thread.deleted'. Required. Default value is + \"thread.deleted\".""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + deleted: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["thread.deleted"] = "thread.deleted" + + +class ThreadMessage(_model_base.Model): + """A single, existing message within an assistant thread. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar id: The identifier, which can be referenced in API endpoints. Required. + :vartype id: str + :ivar object: The object type, which is always 'thread.message'. Required. Default value is + "thread.message". + :vartype object: str + :ivar created_at: The Unix timestamp, in seconds, representing when this object was created. + Required. + :vartype created_at: ~datetime.datetime + :ivar thread_id: The ID of the thread that this message belongs to. Required. + :vartype thread_id: str + :ivar status: The status of the message. Required. Known values are: "in_progress", + "incomplete", and "completed". + :vartype status: str or ~azure.ai.resources.autogen.models.MessageStatus + :ivar incomplete_details: On an incomplete message, details about why the message is + incomplete. Required. + :vartype incomplete_details: ~azure.ai.resources.autogen.models.MessageIncompleteDetails + :ivar completed_at: The Unix timestamp (in seconds) for when the message was completed. + Required. + :vartype completed_at: ~datetime.datetime + :ivar incomplete_at: The Unix timestamp (in seconds) for when the message was marked as + incomplete. Required. + :vartype incomplete_at: ~datetime.datetime + :ivar role: The role associated with the assistant thread message. Required. Known values are: + "user" and "assistant". + :vartype role: str or ~azure.ai.resources.autogen.models.MessageRole + :ivar content: The list of content items associated with the assistant thread message. + Required. + :vartype content: list[~azure.ai.resources.autogen.models.MessageContent] + :ivar assistant_id: If applicable, the ID of the assistant that authored this message. + Required. + :vartype assistant_id: str + :ivar run_id: If applicable, the ID of the run associated with the authoring of this message. + Required. + :vartype run_id: str + :ivar attachments: A list of files attached to the message, and the tools they were added to. + Required. + :vartype attachments: list[~azure.ai.resources.autogen.models.MessageAttachment] + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required. + :vartype metadata: dict[str, str] + """ + + id: str = rest_field() + """The identifier, which can be referenced in API endpoints. Required.""" + object: Literal["thread.message"] = rest_field() + """The object type, which is always 'thread.message'. Required. Default value is + \"thread.message\".""" + created_at: datetime.datetime = rest_field(format="unix-timestamp") + """The Unix timestamp, in seconds, representing when this object was created. Required.""" + thread_id: str = rest_field() + """The ID of the thread that this message belongs to. Required.""" + status: Union[str, "_models.MessageStatus"] = rest_field() + """The status of the message. Required. Known values are: \"in_progress\", \"incomplete\", and + \"completed\".""" + incomplete_details: "_models.MessageIncompleteDetails" = rest_field() + """On an incomplete message, details about why the message is incomplete. Required.""" + completed_at: datetime.datetime = rest_field(format="unix-timestamp") + """The Unix timestamp (in seconds) for when the message was completed. Required.""" + incomplete_at: datetime.datetime = rest_field(format="unix-timestamp") + """The Unix timestamp (in seconds) for when the message was marked as incomplete. Required.""" + role: Union[str, "_models.MessageRole"] = rest_field() + """The role associated with the assistant thread message. Required. Known values are: \"user\" and + \"assistant\".""" + content: List["_models.MessageContent"] = rest_field() + """The list of content items associated with the assistant thread message. Required.""" + assistant_id: str = rest_field() + """If applicable, the ID of the assistant that authored this message. Required.""" + run_id: str = rest_field() + """If applicable, the ID of the run associated with the authoring of this message. Required.""" + attachments: List["_models.MessageAttachment"] = rest_field() + """A list of files attached to the message, and the tools they were added to. Required.""" + metadata: Dict[str, str] = rest_field() + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + created_at: datetime.datetime, + thread_id: str, + status: Union[str, "_models.MessageStatus"], + incomplete_details: "_models.MessageIncompleteDetails", + completed_at: datetime.datetime, + incomplete_at: datetime.datetime, + role: Union[str, "_models.MessageRole"], + content: List["_models.MessageContent"], + assistant_id: str, + run_id: str, + attachments: List["_models.MessageAttachment"], + metadata: Dict[str, str], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["thread.message"] = "thread.message" + + +class ThreadMessageOptions(_model_base.Model): + """A single message within an assistant thread, as provided during that thread's creation for its + initial state. + + All required parameters must be populated in order to send to server. + + :ivar role: The role of the entity that is creating the message. Allowed values include: + + + * ``user``\\ : Indicates the message is sent by an actual user and should be used in most + cases to represent user-generated messages. + * ``assistant``\\ : Indicates the message is generated by the assistant. Use this value to + insert messages from the assistant into + the conversation. Required. Known values are: "user" and "assistant". + :vartype role: str or ~azure.ai.resources.autogen.models.MessageRole + :ivar content: The textual content of the initial message. Currently, robust input including + images and annotated text may only be provided via + a separate call to the create message API. Required. + :vartype content: str + :ivar attachments: A list of files attached to the message, and the tools they should be added + to. + :vartype attachments: list[~azure.ai.resources.autogen.models.MessageAttachment] + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. + :vartype metadata: dict[str, str] + """ + + role: Union[str, "_models.MessageRole"] = rest_field() + """The role of the entity that is creating the message. Allowed values include: + + + * ``user``\ : Indicates the message is sent by an actual user and should be used in most cases + to represent user-generated messages. + * ``assistant``\ : Indicates the message is generated by the assistant. Use this value to + insert messages from the assistant into + the conversation. Required. Known values are: \"user\" and \"assistant\".""" + content: str = rest_field() + """The textual content of the initial message. Currently, robust input including images and + annotated text may only be provided via + a separate call to the create message API. Required.""" + attachments: Optional[List["_models.MessageAttachment"]] = rest_field() + """A list of files attached to the message, and the tools they should be added to.""" + metadata: Optional[Dict[str, str]] = rest_field() + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length.""" + + @overload + def __init__( + self, + *, + role: Union[str, "_models.MessageRole"], + content: str, + attachments: Optional[List["_models.MessageAttachment"]] = None, + metadata: Optional[Dict[str, str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ThreadRun(_model_base.Model): + """Data representing a single evaluation run of an assistant thread. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar id: The identifier, which can be referenced in API endpoints. Required. + :vartype id: str + :ivar object: The object type, which is always 'thread.run'. Required. Default value is + "thread.run". + :vartype object: str + :ivar thread_id: The ID of the thread associated with this run. Required. + :vartype thread_id: str + :ivar assistant_id: The ID of the assistant associated with the thread this run was performed + against. Required. + :vartype assistant_id: str + :ivar status: The status of the assistant thread run. Required. Known values are: "queued", + "in_progress", "requires_action", "cancelling", "cancelled", "failed", "completed", and + "expired". + :vartype status: str or ~azure.ai.resources.autogen.models.RunStatus + :ivar required_action: The details of the action required for the assistant thread run to + continue. + :vartype required_action: ~azure.ai.resources.autogen.models.RequiredAction + :ivar last_error: The last error, if any, encountered by this assistant thread run. Required. + :vartype last_error: ~azure.ai.resources.autogen.models.RunError + :ivar model: The ID of the model to use. Required. + :vartype model: str + :ivar instructions: The overridden system instructions used for this assistant thread run. + Required. + :vartype instructions: str + :ivar tools: The overridden enabled tools used for this assistant thread run. Required. + :vartype tools: list[~azure.ai.resources.autogen.models.ToolDefinition] + :ivar created_at: The Unix timestamp, in seconds, representing when this object was created. + Required. + :vartype created_at: ~datetime.datetime + :ivar expires_at: The Unix timestamp, in seconds, representing when this item expires. + Required. + :vartype expires_at: ~datetime.datetime + :ivar started_at: The Unix timestamp, in seconds, representing when this item was started. + Required. + :vartype started_at: ~datetime.datetime + :ivar completed_at: The Unix timestamp, in seconds, representing when this completed. Required. + :vartype completed_at: ~datetime.datetime + :ivar cancelled_at: The Unix timestamp, in seconds, representing when this was cancelled. + Required. + :vartype cancelled_at: ~datetime.datetime + :ivar failed_at: The Unix timestamp, in seconds, representing when this failed. Required. + :vartype failed_at: ~datetime.datetime + :ivar incomplete_details: Details on why the run is incomplete. Will be ``null`` if the run is + not incomplete. Required. Known values are: "max_completion_tokens" and "max_prompt_tokens". + :vartype incomplete_details: str or ~azure.ai.resources.autogen.models.IncompleteRunDetails + :ivar usage: Usage statistics related to the run. This value will be ``null`` if the run is not + in a terminal state (i.e. ``in_progress``\\ , ``queued``\\ , etc.). Required. + :vartype usage: ~azure.ai.resources.autogen.models.RunCompletionUsage + :ivar temperature: The sampling temperature used for this run. If not set, defaults to 1. + :vartype temperature: float + :ivar top_p: The nucleus sampling value used for this run. If not set, defaults to 1. + :vartype top_p: float + :ivar max_prompt_tokens: The maximum number of prompt tokens specified to have been used over + the course of the run. Required. + :vartype max_prompt_tokens: int + :ivar max_completion_tokens: The maximum number of completion tokens specified to have been + used over the course of the run. Required. + :vartype max_completion_tokens: int + :ivar truncation_strategy: The strategy to use for dropping messages as the context windows + moves forward. Required. + :vartype truncation_strategy: ~azure.ai.resources.autogen.models.TruncationObject + :ivar tool_choice: Controls whether or not and which tool is called by the model. Required. Is + one of the following types: str, Union[str, "_models.AssistantsApiToolChoiceOptionMode"], + AssistantsNamedToolChoice + :vartype tool_choice: str or str or + ~azure.ai.resources.autogen.models.AssistantsApiToolChoiceOptionMode or + ~azure.ai.resources.autogen.models.AssistantsNamedToolChoice + :ivar response_format: The response format of the tool calls used in this run. Required. Is one + of the following types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], + AssistantsApiResponseFormat + :vartype response_format: str or str or + ~azure.ai.resources.autogen.models.AssistantsApiResponseFormatMode or + ~azure.ai.resources.autogen.models.AssistantsApiResponseFormat + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required. + :vartype metadata: dict[str, str] + """ + + id: str = rest_field() + """The identifier, which can be referenced in API endpoints. Required.""" + object: Literal["thread.run"] = rest_field() + """The object type, which is always 'thread.run'. Required. Default value is \"thread.run\".""" + thread_id: str = rest_field() + """The ID of the thread associated with this run. Required.""" + assistant_id: str = rest_field() + """The ID of the assistant associated with the thread this run was performed against. Required.""" + status: Union[str, "_models.RunStatus"] = rest_field() + """The status of the assistant thread run. Required. Known values are: \"queued\", + \"in_progress\", \"requires_action\", \"cancelling\", \"cancelled\", \"failed\", \"completed\", + and \"expired\".""" + required_action: Optional["_models.RequiredAction"] = rest_field() + """The details of the action required for the assistant thread run to continue.""" + last_error: "_models.RunError" = rest_field() + """The last error, if any, encountered by this assistant thread run. Required.""" + model: str = rest_field() + """The ID of the model to use. Required.""" + instructions: str = rest_field() + """The overridden system instructions used for this assistant thread run. Required.""" + tools: List["_models.ToolDefinition"] = rest_field() + """The overridden enabled tools used for this assistant thread run. Required.""" + created_at: datetime.datetime = rest_field(format="unix-timestamp") + """The Unix timestamp, in seconds, representing when this object was created. Required.""" + expires_at: datetime.datetime = rest_field(format="unix-timestamp") + """The Unix timestamp, in seconds, representing when this item expires. Required.""" + started_at: datetime.datetime = rest_field(format="unix-timestamp") + """The Unix timestamp, in seconds, representing when this item was started. Required.""" + completed_at: datetime.datetime = rest_field(format="unix-timestamp") + """The Unix timestamp, in seconds, representing when this completed. Required.""" + cancelled_at: datetime.datetime = rest_field(format="unix-timestamp") + """The Unix timestamp, in seconds, representing when this was cancelled. Required.""" + failed_at: datetime.datetime = rest_field(format="unix-timestamp") + """The Unix timestamp, in seconds, representing when this failed. Required.""" + incomplete_details: Union[str, "_models.IncompleteRunDetails"] = rest_field() + """Details on why the run is incomplete. Will be ``null`` if the run is not incomplete. Required. + Known values are: \"max_completion_tokens\" and \"max_prompt_tokens\".""" + usage: "_models.RunCompletionUsage" = rest_field() + """Usage statistics related to the run. This value will be ``null`` if the run is not in a + terminal state (i.e. ``in_progress``\ , ``queued``\ , etc.). Required.""" + temperature: Optional[float] = rest_field() + """The sampling temperature used for this run. If not set, defaults to 1.""" + top_p: Optional[float] = rest_field() + """The nucleus sampling value used for this run. If not set, defaults to 1.""" + max_prompt_tokens: int = rest_field() + """The maximum number of prompt tokens specified to have been used over the course of the run. + Required.""" + max_completion_tokens: int = rest_field() + """The maximum number of completion tokens specified to have been used over the course of the run. + Required.""" + truncation_strategy: "_models.TruncationObject" = rest_field() + """The strategy to use for dropping messages as the context windows moves forward. Required.""" + tool_choice: "_types.AssistantsApiToolChoiceOption" = rest_field() + """Controls whether or not and which tool is called by the model. Required. Is one of the + following types: str, Union[str, \"_models.AssistantsApiToolChoiceOptionMode\"], + AssistantsNamedToolChoice""" + response_format: "_types.AssistantsApiResponseFormatOption" = rest_field() + """The response format of the tool calls used in this run. Required. Is one of the following + types: str, Union[str, \"_models.AssistantsApiResponseFormatMode\"], + AssistantsApiResponseFormat""" + metadata: Dict[str, str] = rest_field() + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required.""" + + @overload + def __init__( # pylint: disable=too-many-locals + self, + *, + id: str, # pylint: disable=redefined-builtin + thread_id: str, + assistant_id: str, + status: Union[str, "_models.RunStatus"], + last_error: "_models.RunError", + model: str, + instructions: str, + tools: List["_models.ToolDefinition"], + created_at: datetime.datetime, + expires_at: datetime.datetime, + started_at: datetime.datetime, + completed_at: datetime.datetime, + cancelled_at: datetime.datetime, + failed_at: datetime.datetime, + incomplete_details: Union[str, "_models.IncompleteRunDetails"], + usage: "_models.RunCompletionUsage", + max_prompt_tokens: int, + max_completion_tokens: int, + truncation_strategy: "_models.TruncationObject", + tool_choice: "_types.AssistantsApiToolChoiceOption", + response_format: "_types.AssistantsApiResponseFormatOption", + metadata: Dict[str, str], + required_action: Optional["_models.RequiredAction"] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["thread.run"] = "thread.run" + + +class ToolOutput(_model_base.Model): + """The data provided during a tool outputs submission to resolve pending tool calls and allow the + model to continue. + + :ivar tool_call_id: The ID of the tool call being resolved, as provided in the tool calls of a + required action from a run. + :vartype tool_call_id: str + :ivar output: The output from the tool to be submitted. + :vartype output: str + """ + + tool_call_id: Optional[str] = rest_field() + """The ID of the tool call being resolved, as provided in the tool calls of a required action from + a run.""" + output: Optional[str] = rest_field() + """The output from the tool to be submitted.""" + + @overload + def __init__( + self, + *, + tool_call_id: Optional[str] = None, + output: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ToolResources(_model_base.Model): + """A set of resources that are used by the assistant's tools. The resources are specific to the + type of tool. For example, the ``code_interpreter`` tool requires a list of file IDs, while the + ``file_search`` tool requires a list of vector store IDs. + + :ivar code_interpreter: Resources to be used by the ``code_interpreter tool`` consisting of + file IDs. + :vartype code_interpreter: ~azure.ai.resources.autogen.models.CodeInterpreterToolResource + :ivar file_search: Resources to be used by the ``file_search`` tool consisting of vector store + IDs. + :vartype file_search: ~azure.ai.resources.autogen.models.FileSearchToolResource + """ + + code_interpreter: Optional["_models.CodeInterpreterToolResource"] = rest_field() + """Resources to be used by the ``code_interpreter tool`` consisting of file IDs.""" + file_search: Optional["_models.FileSearchToolResource"] = rest_field() + """Resources to be used by the ``file_search`` tool consisting of vector store IDs.""" + + @overload + def __init__( + self, + *, + code_interpreter: Optional["_models.CodeInterpreterToolResource"] = None, + file_search: Optional["_models.FileSearchToolResource"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class TruncationObject(_model_base.Model): + """Controls for how a thread will be truncated prior to the run. Use this to control the initial + context window of the run. + + + :ivar type: The truncation strategy to use for the thread. The default is ``auto``. If set to + ``last_messages``\\ , the thread will + be truncated to the ``lastMessages`` count most recent messages in the thread. When set to + ``auto``\\ , messages in the middle of the thread + will be dropped to fit the context length of the model, ``max_prompt_tokens``. Required. Known + values are: "auto" and "last_messages". + :vartype type: str or ~azure.ai.resources.autogen.models.TruncationStrategy + :ivar last_messages: The number of most recent messages from the thread when constructing the + context for the run. + :vartype last_messages: int + """ + + type: Union[str, "_models.TruncationStrategy"] = rest_field() + """The truncation strategy to use for the thread. The default is ``auto``. If set to + ``last_messages``\ , the thread will + be truncated to the ``lastMessages`` count most recent messages in the thread. When set to + ``auto``\ , messages in the middle of the thread + will be dropped to fit the context length of the model, ``max_prompt_tokens``. Required. Known + values are: \"auto\" and \"last_messages\".""" + last_messages: Optional[int] = rest_field() + """The number of most recent messages from the thread when constructing the context for the run.""" + + @overload + def __init__( + self, + *, + type: Union[str, "_models.TruncationStrategy"], + last_messages: Optional[int] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class UpdateAssistantOptions(_model_base.Model): + """The request details to use when modifying an existing assistant. + + :ivar model: The ID of the model to use. + :vartype model: str + :ivar name: The modified name for the assistant to use. + :vartype name: str + :ivar description: The modified description for the assistant to use. + :vartype description: str + :ivar instructions: The modified system instructions for the new assistant to use. + :vartype instructions: str + :ivar tools: The modified collection of tools to enable for the assistant. + :vartype tools: list[~azure.ai.resources.autogen.models.ToolDefinition] + :ivar tool_resources: A set of resources that are used by the assistant's tools. The resources + are specific to the type of tool. For example, the ``code_interpreter`` tool requires a list of + file IDs, while the ``file_search`` tool requires a list of vector store IDs. + :vartype tool_resources: ~azure.ai.resources.autogen.models.UpdateToolResourcesOptions + :ivar temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 + will make the output more random, while lower values like 0.2 will make it more focused and + deterministic. + :vartype temperature: float + :ivar top_p: An alternative to sampling with temperature, called nucleus sampling, where the + model considers the results of the tokens with top_p probability mass. So 0.1 means only the + tokens comprising the top 10% probability mass are considered. We generally recommend altering + this or temperature but not both. + :vartype top_p: float + :ivar response_format: The response format of the tool calls used by this assistant. Is one of + the following types: str, Union[str, "_models.AssistantsApiResponseFormatMode"], + AssistantsApiResponseFormat + :vartype response_format: str or str or + ~azure.ai.resources.autogen.models.AssistantsApiResponseFormatMode or + ~azure.ai.resources.autogen.models.AssistantsApiResponseFormat + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. + :vartype metadata: dict[str, str] + """ + + model: Optional[str] = rest_field() + """The ID of the model to use.""" + name: Optional[str] = rest_field() + """The modified name for the assistant to use.""" + description: Optional[str] = rest_field() + """The modified description for the assistant to use.""" + instructions: Optional[str] = rest_field() + """The modified system instructions for the new assistant to use.""" + tools: Optional[List["_models.ToolDefinition"]] = rest_field() + """The modified collection of tools to enable for the assistant.""" + tool_resources: Optional["_models.UpdateToolResourcesOptions"] = rest_field() + """A set of resources that are used by the assistant's tools. The resources are specific to the + type of tool. For example, the ``code_interpreter`` tool requires a list of file IDs, while the + ``file_search`` tool requires a list of vector store IDs.""" + temperature: Optional[float] = rest_field() + """What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output + more random, while lower values like 0.2 will make it more focused and deterministic.""" + top_p: Optional[float] = rest_field() + """An alternative to sampling with temperature, called nucleus sampling, where the model considers + the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising + the top 10% probability mass are considered. We generally recommend altering this or + temperature but not both.""" + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = rest_field() + """The response format of the tool calls used by this assistant. Is one of the following types: + str, Union[str, \"_models.AssistantsApiResponseFormatMode\"], AssistantsApiResponseFormat""" + metadata: Optional[Dict[str, str]] = rest_field() + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length.""" + + @overload + def __init__( + self, + *, + model: Optional[str] = None, + name: Optional[str] = None, + description: Optional[str] = None, + instructions: Optional[str] = None, + tools: Optional[List["_models.ToolDefinition"]] = None, + tool_resources: Optional["_models.UpdateToolResourcesOptions"] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + response_format: Optional["_types.AssistantsApiResponseFormatOption"] = None, + metadata: Optional[Dict[str, str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class UpdateAssistantThreadOptions(_model_base.Model): + """The details used to update an existing assistant thread. + + :ivar tool_resources: A set of resources that are made available to the assistant's tools in + this thread. The resources are specific to the type of tool. For example, the + ``code_interpreter`` tool requires a list of file IDs, while the ``file_search`` tool requires + a list of vector store IDs. + :vartype tool_resources: ~azure.ai.resources.autogen.models.UpdateToolResourcesOptions + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. + :vartype metadata: dict[str, str] + """ + + tool_resources: Optional["_models.UpdateToolResourcesOptions"] = rest_field() + """A set of resources that are made available to the assistant's tools in this thread. The + resources are specific to the type of tool. For example, the ``code_interpreter`` tool requires + a list of file IDs, while the ``file_search`` tool requires a list of vector store IDs.""" + metadata: Optional[Dict[str, str]] = rest_field() + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length.""" + + @overload + def __init__( + self, + *, + tool_resources: Optional["_models.UpdateToolResourcesOptions"] = None, + metadata: Optional[Dict[str, str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class UpdateCodeInterpreterToolResourceOptions(_model_base.Model): + """Request object to update ``code_interpreted`` tool resources. + + :ivar file_ids: A list of file IDs to override the current list of the assistant. + :vartype file_ids: list[str] + """ + + file_ids: Optional[List[str]] = rest_field() + """A list of file IDs to override the current list of the assistant.""" + + @overload + def __init__( + self, + *, + file_ids: Optional[List[str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class UpdateEvaluationRequest(_model_base.Model): + """Update Evaluation Request. + + All required parameters must be populated in order to send to server. + + :ivar tags: Tags to be updated. Required. + :vartype tags: dict[str, str] + :ivar display_name: Display Name. Required. + :vartype display_name: str + :ivar description: Description. Required. + :vartype description: str + """ + + tags: Dict[str, str] = rest_field() + """Tags to be updated. Required.""" + display_name: str = rest_field(name="displayName") + """Display Name. Required.""" + description: str = rest_field() + """Description. Required.""" + + @overload + def __init__( + self, + *, + tags: Dict[str, str], + display_name: str, + description: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class UpdateFileSearchToolResourceOptions(_model_base.Model): + """Request object to update ``file_search`` tool resources. + + :ivar vector_store_ids: A list of vector store IDs to override the current list of the + assistant. + :vartype vector_store_ids: list[str] + """ + + vector_store_ids: Optional[List[str]] = rest_field() + """A list of vector store IDs to override the current list of the assistant.""" + + @overload + def __init__( + self, + *, + vector_store_ids: Optional[List[str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class UpdateToolResourcesOptions(_model_base.Model): + """Request object. A set of resources that are used by the assistant's tools. The resources are + specific to the type of tool. For example, the ``code_interpreter`` tool requires a list of + file IDs, while the ``file_search`` tool requires a list of vector store IDs. + + :ivar code_interpreter: Overrides the list of file IDs made available to the + ``code_interpreter`` tool. There can be a maximum of 20 files associated with the tool. + :vartype code_interpreter: + ~azure.ai.resources.autogen.models.UpdateCodeInterpreterToolResourceOptions + :ivar file_search: Overrides the vector store attached to this assistant. There can be a + maximum of 1 vector store attached to the assistant. + :vartype file_search: ~azure.ai.resources.autogen.models.UpdateFileSearchToolResourceOptions + """ + + code_interpreter: Optional["_models.UpdateCodeInterpreterToolResourceOptions"] = rest_field() + """Overrides the list of file IDs made available to the ``code_interpreter`` tool. There can be a + maximum of 20 files associated with the tool.""" + file_search: Optional["_models.UpdateFileSearchToolResourceOptions"] = rest_field() + """Overrides the vector store attached to this assistant. There can be a maximum of 1 vector store + attached to the assistant.""" + + @overload + def __init__( + self, + *, + code_interpreter: Optional["_models.UpdateCodeInterpreterToolResourceOptions"] = None, + file_search: Optional["_models.UpdateFileSearchToolResourceOptions"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class UriFileDataVersion(DataVersionBase, discriminator="uri_file"): + """UriFileDataVersion Definition. + + + :ivar description: The asset description text. + :vartype description: str + :ivar properties: The asset property dictionary. + :vartype properties: dict[str, str] + :ivar tags: Tag dictionary. Tags can be added, removed, and updated. + :vartype tags: dict[str, str] + :ivar is_anonymous: If the name version are system generated (anonymous registration). + :vartype is_anonymous: bool + :ivar is_archived: Is the asset archived?. + :vartype is_archived: bool + :ivar data_uri: [Required] Uri of the data. Example: + https://go.microsoft.com/fwlink/?linkid=2202330. Required. + :vartype data_uri: str + :ivar data_type: [Required] Specifies the type of data. Required. URI file. + :vartype data_type: str or ~azure.ai.resources.autogen.models.URI_FILE + """ + + data_type: Literal[DataType.URI_FILE] = rest_discriminator(name="dataType") # type: ignore + """[Required] Specifies the type of data. Required. URI file.""" + + @overload + def __init__( + self, + *, + data_uri: str, + description: Optional[str] = None, + properties: Optional[Dict[str, str]] = None, + tags: Optional[Dict[str, str]] = None, + is_anonymous: Optional[bool] = None, + is_archived: Optional[bool] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, data_type=DataType.URI_FILE, **kwargs) + + +class UriFolderDataVersion(DataVersionBase, discriminator="uri_folder"): + """UriFolderDataVersion Definition. + + + :ivar description: The asset description text. + :vartype description: str + :ivar properties: The asset property dictionary. + :vartype properties: dict[str, str] + :ivar tags: Tag dictionary. Tags can be added, removed, and updated. + :vartype tags: dict[str, str] + :ivar is_anonymous: If the name version are system generated (anonymous registration). + :vartype is_anonymous: bool + :ivar is_archived: Is the asset archived?. + :vartype is_archived: bool + :ivar data_uri: [Required] Uri of the data. Example: + https://go.microsoft.com/fwlink/?linkid=2202330. Required. + :vartype data_uri: str + :ivar data_type: [Required] Specifies the type of data. Required. URI folder. + :vartype data_type: str or ~azure.ai.resources.autogen.models.URI_FOLDER + """ + + data_type: Literal[DataType.URI_FOLDER] = rest_discriminator(name="dataType") # type: ignore + """[Required] Specifies the type of data. Required. URI folder.""" + + @overload + def __init__( + self, + *, + data_uri: str, + description: Optional[str] = None, + properties: Optional[Dict[str, str]] = None, + tags: Optional[Dict[str, str]] = None, + is_anonymous: Optional[bool] = None, + is_archived: Optional[bool] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, data_type=DataType.URI_FOLDER, **kwargs) + + +class VectorStore(_model_base.Model): + """A vector store is a collection of processed files can be used by the ``file_search`` tool. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar id: The identifier, which can be referenced in API endpoints. Required. + :vartype id: str + :ivar object: The object type, which is always ``vector_store``. Required. Default value is + "vector_store". + :vartype object: str + :ivar created_at: The Unix timestamp (in seconds) for when the vector store was created. + Required. + :vartype created_at: ~datetime.datetime + :ivar name: The name of the vector store. Required. + :vartype name: str + :ivar usage_bytes: The total number of bytes used by the files in the vector store. Required. + :vartype usage_bytes: int + :ivar file_counts: Files count grouped by status processed or being processed by this vector + store. Required. + :vartype file_counts: ~azure.ai.resources.autogen.models.VectorStoreFileCount + :ivar status: The status of the vector store, which can be either ``expired``\\ , + ``in_progress``\\ , or ``completed``. A status of ``completed`` indicates that the vector store + is ready for use. Required. Known values are: "expired", "in_progress", and "completed". + :vartype status: str or ~azure.ai.resources.autogen.models.VectorStoreStatus + :ivar expires_after: Details on when this vector store expires. + :vartype expires_after: ~azure.ai.resources.autogen.models.VectorStoreExpirationPolicy + :ivar expires_at: The Unix timestamp (in seconds) for when the vector store will expire. + :vartype expires_at: ~datetime.datetime + :ivar last_active_at: The Unix timestamp (in seconds) for when the vector store was last + active. Required. + :vartype last_active_at: ~datetime.datetime + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required. + :vartype metadata: dict[str, str] + """ + + id: str = rest_field() + """The identifier, which can be referenced in API endpoints. Required.""" + object: Literal["vector_store"] = rest_field() + """The object type, which is always ``vector_store``. Required. Default value is \"vector_store\".""" + created_at: datetime.datetime = rest_field(format="unix-timestamp") + """The Unix timestamp (in seconds) for when the vector store was created. Required.""" + name: str = rest_field() + """The name of the vector store. Required.""" + usage_bytes: int = rest_field() + """The total number of bytes used by the files in the vector store. Required.""" + file_counts: "_models.VectorStoreFileCount" = rest_field() + """Files count grouped by status processed or being processed by this vector store. Required.""" + status: Union[str, "_models.VectorStoreStatus"] = rest_field() + """The status of the vector store, which can be either ``expired``\ , ``in_progress``\ , or + ``completed``. A status of ``completed`` indicates that the vector store is ready for use. + Required. Known values are: \"expired\", \"in_progress\", and \"completed\".""" + expires_after: Optional["_models.VectorStoreExpirationPolicy"] = rest_field() + """Details on when this vector store expires.""" + expires_at: Optional[datetime.datetime] = rest_field(format="unix-timestamp") + """The Unix timestamp (in seconds) for when the vector store will expire.""" + last_active_at: datetime.datetime = rest_field(format="unix-timestamp") + """The Unix timestamp (in seconds) for when the vector store was last active. Required.""" + metadata: Dict[str, str] = rest_field() + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + created_at: datetime.datetime, + name: str, + usage_bytes: int, + file_counts: "_models.VectorStoreFileCount", + status: Union[str, "_models.VectorStoreStatus"], + last_active_at: datetime.datetime, + metadata: Dict[str, str], + expires_after: Optional["_models.VectorStoreExpirationPolicy"] = None, + expires_at: Optional[datetime.datetime] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["vector_store"] = "vector_store" + + +class VectorStoreChunkingStrategyRequest(_model_base.Model): + """An abstract representation of a vector store chunking strategy configuration. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + VectorStoreAutoChunkingStrategyRequest, VectorStoreStaticChunkingStrategyRequest + + All required parameters must be populated in order to send to server. + + :ivar type: The object type. Required. Known values are: "auto" and "static". + :vartype type: str or ~azure.ai.resources.autogen.models.VectorStoreChunkingStrategyRequestType + """ + + __mapping__: Dict[str, _model_base.Model] = {} + type: str = rest_discriminator(name="type") + """The object type. Required. Known values are: \"auto\" and \"static\".""" + + @overload + def __init__( + self, + *, + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class VectorStoreAutoChunkingStrategyRequest(VectorStoreChunkingStrategyRequest, discriminator="auto"): + """The default strategy. This strategy currently uses a max_chunk_size_tokens of 800 and + chunk_overlap_tokens of 400. + + All required parameters must be populated in order to send to server. + + :ivar type: The object type, which is always 'auto'. Required. + :vartype type: str or ~azure.ai.resources.autogen.models.AUTO + """ + + type: Literal[VectorStoreChunkingStrategyRequestType.AUTO] = rest_discriminator(name="type") # type: ignore + """The object type, which is always 'auto'. Required.""" + + @overload + def __init__( + self, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type=VectorStoreChunkingStrategyRequestType.AUTO, **kwargs) + + +class VectorStoreChunkingStrategyResponse(_model_base.Model): + """An abstract representation of a vector store chunking strategy configuration. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + VectorStoreAutoChunkingStrategyResponse, VectorStoreStaticChunkingStrategyResponse + + + :ivar type: The object type. Required. Known values are: "other" and "static". + :vartype type: str or + ~azure.ai.resources.autogen.models.VectorStoreChunkingStrategyResponseType + """ + + __mapping__: Dict[str, _model_base.Model] = {} + type: str = rest_discriminator(name="type") + """The object type. Required. Known values are: \"other\" and \"static\".""" + + @overload + def __init__( + self, + *, + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class VectorStoreAutoChunkingStrategyResponse(VectorStoreChunkingStrategyResponse, discriminator="other"): + """This is returned when the chunking strategy is unknown. Typically, this is because the file was + indexed before the chunking_strategy concept was introduced in the API. + + + :ivar type: The object type, which is always 'other'. Required. + :vartype type: str or ~azure.ai.resources.autogen.models.OTHER + """ + + type: Literal[VectorStoreChunkingStrategyResponseType.OTHER] = rest_discriminator(name="type") # type: ignore + """The object type, which is always 'other'. Required.""" + + @overload + def __init__( + self, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type=VectorStoreChunkingStrategyResponseType.OTHER, **kwargs) + + +class VectorStoreDeletionStatus(_model_base.Model): + """Response object for deleting a vector store. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar id: The ID of the resource specified for deletion. Required. + :vartype id: str + :ivar deleted: A value indicating whether deletion was successful. Required. + :vartype deleted: bool + :ivar object: The object type, which is always 'vector_store.deleted'. Required. Default value + is "vector_store.deleted". + :vartype object: str + """ + + id: str = rest_field() + """The ID of the resource specified for deletion. Required.""" + deleted: bool = rest_field() + """A value indicating whether deletion was successful. Required.""" + object: Literal["vector_store.deleted"] = rest_field() + """The object type, which is always 'vector_store.deleted'. Required. Default value is + \"vector_store.deleted\".""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + deleted: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["vector_store.deleted"] = "vector_store.deleted" + + +class VectorStoreExpirationPolicy(_model_base.Model): + """The expiration policy for a vector store. + + + :ivar anchor: Anchor timestamp after which the expiration policy applies. Supported anchors: + ``last_active_at``. Required. "last_active_at" + :vartype anchor: str or ~azure.ai.resources.autogen.models.VectorStoreExpirationPolicyAnchor + :ivar days: The anchor timestamp after which the expiration policy applies. Required. + :vartype days: int + """ + + anchor: Union[str, "_models.VectorStoreExpirationPolicyAnchor"] = rest_field() + """Anchor timestamp after which the expiration policy applies. Supported anchors: + ``last_active_at``. Required. \"last_active_at\"""" + days: int = rest_field() + """The anchor timestamp after which the expiration policy applies. Required.""" + + @overload + def __init__( + self, + *, + anchor: Union[str, "_models.VectorStoreExpirationPolicyAnchor"], + days: int, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class VectorStoreFile(_model_base.Model): + """Description of a file attached to a vector store. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar id: The identifier, which can be referenced in API endpoints. Required. + :vartype id: str + :ivar object: The object type, which is always ``vector_store.file``. Required. Default value + is "vector_store.file". + :vartype object: str + :ivar usage_bytes: The total vector store usage in bytes. Note that this may be different from + the original file size. Required. + :vartype usage_bytes: int + :ivar created_at: The Unix timestamp (in seconds) for when the vector store file was created. + Required. + :vartype created_at: ~datetime.datetime + :ivar vector_store_id: The ID of the vector store that the file is attached to. Required. + :vartype vector_store_id: str + :ivar status: The status of the vector store file, which can be either ``in_progress``\\ , + ``completed``\\ , ``cancelled``\\ , or ``failed``. The status ``completed`` indicates that the + vector store file is ready for use. Required. Known values are: "in_progress", "completed", + "failed", and "cancelled". + :vartype status: str or ~azure.ai.resources.autogen.models.VectorStoreFileStatus + :ivar last_error: The last error associated with this vector store file. Will be ``null`` if + there are no errors. Required. + :vartype last_error: ~azure.ai.resources.autogen.models.VectorStoreFileError + :ivar chunking_strategy: The strategy used to chunk the file. Required. + :vartype chunking_strategy: + ~azure.ai.resources.autogen.models.VectorStoreChunkingStrategyResponse + """ + + id: str = rest_field() + """The identifier, which can be referenced in API endpoints. Required.""" + object: Literal["vector_store.file"] = rest_field() + """The object type, which is always ``vector_store.file``. Required. Default value is + \"vector_store.file\".""" + usage_bytes: int = rest_field() + """The total vector store usage in bytes. Note that this may be different from the original file + size. Required.""" + created_at: datetime.datetime = rest_field(format="unix-timestamp") + """The Unix timestamp (in seconds) for when the vector store file was created. Required.""" + vector_store_id: str = rest_field() + """The ID of the vector store that the file is attached to. Required.""" + status: Union[str, "_models.VectorStoreFileStatus"] = rest_field() + """The status of the vector store file, which can be either ``in_progress``\ , ``completed``\ , + ``cancelled``\ , or ``failed``. The status ``completed`` indicates that the vector store file + is ready for use. Required. Known values are: \"in_progress\", \"completed\", \"failed\", and + \"cancelled\".""" + last_error: "_models.VectorStoreFileError" = rest_field() + """The last error associated with this vector store file. Will be ``null`` if there are no errors. + Required.""" + chunking_strategy: "_models.VectorStoreChunkingStrategyResponse" = rest_field() + """The strategy used to chunk the file. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + usage_bytes: int, + created_at: datetime.datetime, + vector_store_id: str, + status: Union[str, "_models.VectorStoreFileStatus"], + last_error: "_models.VectorStoreFileError", + chunking_strategy: "_models.VectorStoreChunkingStrategyResponse", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["vector_store.file"] = "vector_store.file" + + +class VectorStoreFileBatch(_model_base.Model): + """A batch of files attached to a vector store. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar id: The identifier, which can be referenced in API endpoints. Required. + :vartype id: str + :ivar object: The object type, which is always ``vector_store.file_batch``. Required. Default + value is "vector_store.files_batch". + :vartype object: str + :ivar created_at: The Unix timestamp (in seconds) for when the vector store files batch was + created. Required. + :vartype created_at: ~datetime.datetime + :ivar vector_store_id: The ID of the vector store that the file is attached to. Required. + :vartype vector_store_id: str + :ivar status: The status of the vector store files batch, which can be either ``in_progress``\\ + , ``completed``\\ , ``cancelled`` or ``failed``. Required. Known values are: "in_progress", + "completed", "cancelled", and "failed". + :vartype status: str or ~azure.ai.resources.autogen.models.VectorStoreFileBatchStatus + :ivar file_counts: Files count grouped by status processed or being processed by this vector + store. Required. + :vartype file_counts: ~azure.ai.resources.autogen.models.VectorStoreFileCount + """ + + id: str = rest_field() + """The identifier, which can be referenced in API endpoints. Required.""" + object: Literal["vector_store.files_batch"] = rest_field() + """The object type, which is always ``vector_store.file_batch``. Required. Default value is + \"vector_store.files_batch\".""" + created_at: datetime.datetime = rest_field(format="unix-timestamp") + """The Unix timestamp (in seconds) for when the vector store files batch was created. Required.""" + vector_store_id: str = rest_field() + """The ID of the vector store that the file is attached to. Required.""" + status: Union[str, "_models.VectorStoreFileBatchStatus"] = rest_field() + """The status of the vector store files batch, which can be either ``in_progress``\ , + ``completed``\ , ``cancelled`` or ``failed``. Required. Known values are: \"in_progress\", + \"completed\", \"cancelled\", and \"failed\".""" + file_counts: "_models.VectorStoreFileCount" = rest_field() + """Files count grouped by status processed or being processed by this vector store. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + created_at: datetime.datetime, + vector_store_id: str, + status: Union[str, "_models.VectorStoreFileBatchStatus"], + file_counts: "_models.VectorStoreFileCount", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["vector_store.files_batch"] = "vector_store.files_batch" + + +class VectorStoreFileCount(_model_base.Model): + """Counts of files processed or being processed by this vector store grouped by status. + + + :ivar in_progress: The number of files that are currently being processed. Required. + :vartype in_progress: int + :ivar completed: The number of files that have been successfully processed. Required. + :vartype completed: int + :ivar failed: The number of files that have failed to process. Required. + :vartype failed: int + :ivar cancelled: The number of files that were cancelled. Required. + :vartype cancelled: int + :ivar total: The total number of files. Required. + :vartype total: int + """ + + in_progress: int = rest_field() + """The number of files that are currently being processed. Required.""" + completed: int = rest_field() + """The number of files that have been successfully processed. Required.""" + failed: int = rest_field() + """The number of files that have failed to process. Required.""" + cancelled: int = rest_field() + """The number of files that were cancelled. Required.""" + total: int = rest_field() + """The total number of files. Required.""" + + @overload + def __init__( + self, + *, + in_progress: int, + completed: int, + failed: int, + cancelled: int, + total: int, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class VectorStoreFileDeletionStatus(_model_base.Model): + """Response object for deleting a vector store file relationship. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + + :ivar id: The ID of the resource specified for deletion. Required. + :vartype id: str + :ivar deleted: A value indicating whether deletion was successful. Required. + :vartype deleted: bool + :ivar object: The object type, which is always 'vector_store.deleted'. Required. Default value + is "vector_store.file.deleted". + :vartype object: str + """ + + id: str = rest_field() + """The ID of the resource specified for deletion. Required.""" + deleted: bool = rest_field() + """A value indicating whether deletion was successful. Required.""" + object: Literal["vector_store.file.deleted"] = rest_field() + """The object type, which is always 'vector_store.deleted'. Required. Default value is + \"vector_store.file.deleted\".""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + deleted: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.object: Literal["vector_store.file.deleted"] = "vector_store.file.deleted" + + +class VectorStoreFileError(_model_base.Model): + """Details on the error that may have ocurred while processing a file for this vector store. + + + :ivar code: One of ``server_error`` or ``rate_limit_exceeded``. Required. Known values are: + "internal_error", "file_not_found", "parsing_error", and "unhandled_mime_type". + :vartype code: str or ~azure.ai.resources.autogen.models.VectorStoreFileErrorCode + :ivar message: A human-readable description of the error. Required. + :vartype message: str + """ + + code: Union[str, "_models.VectorStoreFileErrorCode"] = rest_field() + """One of ``server_error`` or ``rate_limit_exceeded``. Required. Known values are: + \"internal_error\", \"file_not_found\", \"parsing_error\", and \"unhandled_mime_type\".""" + message: str = rest_field() + """A human-readable description of the error. Required.""" + + @overload + def __init__( + self, + *, + code: Union[str, "_models.VectorStoreFileErrorCode"], + message: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class VectorStoreOptions(_model_base.Model): + """Request object for creating a vector store. + + :ivar file_ids: A list of file IDs that the vector store should use. Useful for tools like + ``file_search`` that can access files. + :vartype file_ids: list[str] + :ivar name: The name of the vector store. + :vartype name: str + :ivar expires_after: Details on when this vector store expires. + :vartype expires_after: ~azure.ai.resources.autogen.models.VectorStoreExpirationPolicy + :ivar chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will use + the auto strategy. Only applicable if file_ids is non-empty. + :vartype chunking_strategy: + ~azure.ai.resources.autogen.models.VectorStoreChunkingStrategyRequest + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. + :vartype metadata: dict[str, str] + """ + + file_ids: Optional[List[str]] = rest_field() + """A list of file IDs that the vector store should use. Useful for tools like ``file_search`` that + can access files.""" + name: Optional[str] = rest_field() + """The name of the vector store.""" + expires_after: Optional["_models.VectorStoreExpirationPolicy"] = rest_field() + """Details on when this vector store expires.""" + chunking_strategy: Optional["_models.VectorStoreChunkingStrategyRequest"] = rest_field() + """The chunking strategy used to chunk the file(s). If not set, will use the auto strategy. Only + applicable if file_ids is non-empty.""" + metadata: Optional[Dict[str, str]] = rest_field() + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length.""" + + @overload + def __init__( + self, + *, + file_ids: Optional[List[str]] = None, + name: Optional[str] = None, + expires_after: Optional["_models.VectorStoreExpirationPolicy"] = None, + chunking_strategy: Optional["_models.VectorStoreChunkingStrategyRequest"] = None, + metadata: Optional[Dict[str, str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class VectorStoreStaticChunkingStrategyOptions(_model_base.Model): + """Options to configure a vector store static chunking strategy. + + + :ivar max_chunk_size_tokens: The maximum number of tokens in each chunk. The default value is + 800. The minimum value is 100 and the maximum value is 4096. Required. + :vartype max_chunk_size_tokens: int + :ivar chunk_overlap_tokens: The number of tokens that overlap between chunks. The default value + is 400. Note that the overlap must not exceed half of max_chunk_size_tokens. Required. + :vartype chunk_overlap_tokens: int + """ + + max_chunk_size_tokens: int = rest_field() + """The maximum number of tokens in each chunk. The default value is 800. The minimum value is 100 + and the maximum value is 4096. Required.""" + chunk_overlap_tokens: int = rest_field() + """The number of tokens that overlap between chunks. The default value is 400. Note that the + overlap must not exceed half of max_chunk_size_tokens. Required.""" + + @overload + def __init__( + self, + *, + max_chunk_size_tokens: int, + chunk_overlap_tokens: int, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class VectorStoreStaticChunkingStrategyRequest(VectorStoreChunkingStrategyRequest, discriminator="static"): + """A statically configured chunking strategy. + + All required parameters must be populated in order to send to server. + + :ivar type: The object type, which is always 'static'. Required. + :vartype type: str or ~azure.ai.resources.autogen.models.STATIC + :ivar static: The options for the static chunking strategy. Required. + :vartype static: ~azure.ai.resources.autogen.models.VectorStoreStaticChunkingStrategyOptions + """ + + type: Literal[VectorStoreChunkingStrategyRequestType.STATIC] = rest_discriminator(name="type") # type: ignore + """The object type, which is always 'static'. Required.""" + static: "_models.VectorStoreStaticChunkingStrategyOptions" = rest_field() + """The options for the static chunking strategy. Required.""" + + @overload + def __init__( + self, + *, + static: "_models.VectorStoreStaticChunkingStrategyOptions", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type=VectorStoreChunkingStrategyRequestType.STATIC, **kwargs) + + +class VectorStoreStaticChunkingStrategyResponse( + VectorStoreChunkingStrategyResponse, discriminator="static" +): # pylint: disable=name-too-long + """A statically configured chunking strategy. + + + :ivar type: The object type, which is always 'static'. Required. + :vartype type: str or ~azure.ai.resources.autogen.models.STATIC + :ivar static: The options for the static chunking strategy. Required. + :vartype static: ~azure.ai.resources.autogen.models.VectorStoreStaticChunkingStrategyOptions + """ + + type: Literal[VectorStoreChunkingStrategyResponseType.STATIC] = rest_discriminator(name="type") # type: ignore + """The object type, which is always 'static'. Required.""" + static: "_models.VectorStoreStaticChunkingStrategyOptions" = rest_field() + """The options for the static chunking strategy. Required.""" + + @overload + def __init__( + self, + *, + static: "_models.VectorStoreStaticChunkingStrategyOptions", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type=VectorStoreChunkingStrategyResponseType.STATIC, **kwargs) + + +class VectorStoreUpdateOptions(_model_base.Model): + """Request object for updating a vector store. + + :ivar name: The name of the vector store. + :vartype name: str + :ivar expires_after: Details on when this vector store expires. + :vartype expires_after: ~azure.ai.resources.autogen.models.VectorStoreExpirationPolicy + :ivar metadata: A set of up to 16 key/value pairs that can be attached to an object, used for + storing additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length. + :vartype metadata: dict[str, str] + """ + + name: Optional[str] = rest_field() + """The name of the vector store.""" + expires_after: Optional["_models.VectorStoreExpirationPolicy"] = rest_field() + """Details on when this vector store expires.""" + metadata: Optional[Dict[str, str]] = rest_field() + """A set of up to 16 key/value pairs that can be attached to an object, used for storing + additional information about that object in a structured format. Keys may be up to 64 + characters in length and values may be up to 512 characters in length.""" + + @overload + def __init__( + self, + *, + name: Optional[str] = None, + expires_after: Optional["_models.VectorStoreExpirationPolicy"] = None, + metadata: Optional[Dict[str, str]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class VersionInfo(_model_base.Model): + """Next version definition. + + + :ivar next_version: Next version as defined by the server. The server keeps track of all + versions that are string-representations of integers. If one exists, the nextVersion will be a + string representation of the highest integer value + 1. Otherwise, the nextVersion will default + to '1'. + :vartype next_version: int + :ivar latest_version: Current latest version of the resource. Required. + :vartype latest_version: str + """ + + next_version: Optional[int] = rest_field(name="nextVersion") + """Next version as defined by the server. The server keeps track of all versions that are + string-representations of integers. If one exists, the nextVersion will be a string + representation of the highest integer value + 1. Otherwise, the nextVersion will default to + '1'.""" + latest_version: str = rest_field(name="latestVersion") + """Current latest version of the resource. Required.""" + + @overload + def __init__( + self, + *, + latest_version: str, + next_version: Optional[int] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/models/_patch.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/models/_patch.py new file mode 100644 index 000000000000..f7dd32510333 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/models/_patch.py @@ -0,0 +1,20 @@ +# ------------------------------------ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. +# ------------------------------------ +"""Customize generated code here. + +Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize +""" +from typing import List + +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level + + +def patch_sdk(): + """Do not remove from this file. + + `patch_sdk` is a last resort escape hatch that allows you to do customizations + you can't accomplish using the techniques described in + https://aka.ms/azsdk/python/dpcodegen/python/customize + """ diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/operations/__init__.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/operations/__init__.py new file mode 100644 index 000000000000..6fda01b5f7fa --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/operations/__init__.py @@ -0,0 +1,39 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +# pylint: disable=wrong-import-position + +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from ._patch import * # pylint: disable=unused-wildcard-import + +from ._operations import ConnectionsOperations # type: ignore +from ._operations import DataOperations # type: ignore +from ._operations import DataVersionsBaseOperations # type: ignore +from ._operations import EvaluationsOperations # type: ignore +from ._operations import IndexesOperations # type: ignore +from ._operations import ModelContainersOperations # type: ignore +from ._operations import ModelVersionsOperations # type: ignore +from ._operations import MachineLearningServicesClientOperationsMixin # type: ignore + +from ._patch import __all__ as _patch_all +from ._patch import * +from ._patch import patch_sdk as _patch_sdk + +__all__ = [ + "ConnectionsOperations", + "DataOperations", + "DataVersionsBaseOperations", + "EvaluationsOperations", + "IndexesOperations", + "ModelContainersOperations", + "ModelVersionsOperations", + "MachineLearningServicesClientOperationsMixin", +] +__all__.extend([p for p in _patch_all if p not in __all__]) # pyright: ignore +_patch_sdk() diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/operations/_operations.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/operations/_operations.py new file mode 100644 index 000000000000..d506886ba8ae --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/operations/_operations.py @@ -0,0 +1,11318 @@ +# pylint: disable=too-many-lines +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +from io import IOBase +import json +import sys +from typing import Any, Callable, Dict, IO, Iterable, List, Optional, TypeVar, Union, overload +import urllib.parse + +from azure.core.exceptions import ( + ClientAuthenticationError, + HttpResponseError, + ResourceExistsError, + ResourceNotFoundError, + ResourceNotModifiedError, + StreamClosedError, + StreamConsumedError, + map_error, +) +from azure.core.paging import ItemPaged +from azure.core.pipeline import PipelineResponse +from azure.core.rest import HttpRequest, HttpResponse +from azure.core.tracing.decorator import distributed_trace +from azure.core.utils import case_insensitive_dict + +from .. import _model_base, models as _models +from .._model_base import SdkJSONEncoder, _deserialize +from .._serialization import Serializer +from .._vendor import FileType, MachineLearningServicesClientMixinABC, prepare_multipart_form_data + +if sys.version_info >= (3, 9): + from collections.abc import MutableMapping +else: + from typing import MutableMapping # type: ignore +T = TypeVar("T") +ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] +JSON = MutableMapping[str, Any] # pylint: disable=unsubscriptable-object +_Unset: Any = object() + +_SERIALIZER = Serializer() +_SERIALIZER.client_side_validation = False + + +def build_connections_get_request(name: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/connections/{name}" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_connections_post_request(name: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/connections/{name}/listsecrets" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_connections_list_request( + *, top: Optional[int] = None, skip: Optional[int] = None, maxpagesize: Optional[int] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/connections" + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + if top is not None: + _params["top"] = _SERIALIZER.query("top", top, "int") + if skip is not None: + _params["skip"] = _SERIALIZER.query("skip", skip, "int") + if maxpagesize is not None: + _params["maxpagesize"] = _SERIALIZER.query("maxpagesize", maxpagesize, "int") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_connections_list_with_credentials_request( # pylint: disable=name-too-long + *, top: Optional[int] = None, skip: Optional[int] = None, maxpagesize: Optional[int] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/connections/withsecrets" + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + if top is not None: + _params["top"] = _SERIALIZER.query("top", top, "int") + if skip is not None: + _params["skip"] = _SERIALIZER.query("skip", skip, "int") + if maxpagesize is not None: + _params["maxpagesize"] = _SERIALIZER.query("maxpagesize", maxpagesize, "int") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_data_list_request( + *, _skip: Optional[str] = None, list_view_type: Optional[Union[str, _models.ListViewType]] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/data" + + # Construct parameters + if _skip is not None: + _params["$skip"] = _SERIALIZER.query("skip", _skip, "str") + if list_view_type is not None: + _params["listViewType"] = _SERIALIZER.query("list_view_type", list_view_type, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_data_delete_request(workspace_name: str, name: str, **kwargs: Any) -> HttpRequest: + # Construct URL + _url = "/data/{name}/{workspaceName}" + path_format_arguments = { + "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, "str"), + "name": _SERIALIZER.url("name", name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + return HttpRequest(method="DELETE", url=_url, **kwargs) + + +def build_data_get_request(name: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/data/{name}" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_data_create_or_update_request(name: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/data/{name}" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PUT", url=_url, headers=_headers, **kwargs) + + +def build_data_versions_base_list_request( + name: str, + *, + _order_by: Optional[str] = None, + _top: Optional[int] = None, + _skip: Optional[str] = None, + _tags: Optional[str] = None, + list_view_type: Optional[Union[str, _models.ListViewType]] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/data/{name}/versions" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if _order_by is not None: + _params["$orderBy"] = _SERIALIZER.query("order_by", _order_by, "str") + if _top is not None: + _params["$top"] = _SERIALIZER.query("top", _top, "int") + if _skip is not None: + _params["$skip"] = _SERIALIZER.query("skip", _skip, "str") + if _tags is not None: + _params["$tags"] = _SERIALIZER.query("tags", _tags, "str") + if list_view_type is not None: + _params["listViewType"] = _SERIALIZER.query("list_view_type", list_view_type, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_data_versions_base_delete_request(name: str, version: str, **kwargs: Any) -> HttpRequest: + # Construct URL + _url = "/data/{name}/versions/{version}" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + "version": _SERIALIZER.url("version", version, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + return HttpRequest(method="DELETE", url=_url, **kwargs) + + +def build_data_versions_base_get_request(name: str, version: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/data/{name}/versions/{version}" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + "version": _SERIALIZER.url("version", version, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_data_versions_base_create_or_update_request( # pylint: disable=name-too-long + name: str, version: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/data/{name}/versions/{version}" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + "version": _SERIALIZER.url("version", version, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PUT", url=_url, headers=_headers, **kwargs) + + +def build_data_versions_base_publish_request(name: str, version: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + # Construct URL + _url = "/data/{name}/versions/{version}/publish" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + "version": _SERIALIZER.url("version", version, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_evaluations_get_request(id: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/evaluations/{id}" + path_format_arguments = { + "id": _SERIALIZER.url("id", id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_evaluations_create_request(**kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/evaluations/create" + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_evaluations_list_request( + *, top: Optional[int] = None, skip: Optional[int] = None, maxpagesize: Optional[int] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/evaluations" + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + if top is not None: + _params["top"] = _SERIALIZER.query("top", top, "int") + if skip is not None: + _params["skip"] = _SERIALIZER.query("skip", skip, "int") + if maxpagesize is not None: + _params["maxpagesize"] = _SERIALIZER.query("maxpagesize", maxpagesize, "int") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_evaluations_update_request(id: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/evaluations/{id}" + path_format_arguments = { + "id": _SERIALIZER.url("id", id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PATCH", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_indexes_get_request(name: str, version: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/indexes/{name}/versions/{version}" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + "version": _SERIALIZER.url("version", version, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_indexes_create_or_update_request(name: str, version: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/indexes/{name}/versions/{version}" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + "version": _SERIALIZER.url("version", version, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PUT", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_indexes_list_request( + name: str, + *, + list_view_type: str, + order_by: Optional[str] = None, + orderby: Optional[str] = None, + tags: Optional[str] = None, + top: Optional[int] = None, + skip: Optional[int] = None, + maxpagesize: Optional[int] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/indexes/{name}/versions" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + _params["listViewType"] = _SERIALIZER.query("list_view_type", list_view_type, "str") + if order_by is not None: + _params["orderBy"] = _SERIALIZER.query("order_by", order_by, "str") + if orderby is not None: + _params["orderby"] = _SERIALIZER.query("orderby", orderby, "str") + if tags is not None: + _params["tags"] = _SERIALIZER.query("tags", tags, "str") + if top is not None: + _params["top"] = _SERIALIZER.query("top", top, "int") + if skip is not None: + _params["skip"] = _SERIALIZER.query("skip", skip, "int") + if maxpagesize is not None: + _params["maxpagesize"] = _SERIALIZER.query("maxpagesize", maxpagesize, "int") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_indexes_get_latest_request(name: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/indexes/{name}" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_indexes_get_next_version_request(name: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/indexes/{name}:getNextVersion" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_indexes_list_latest_request( + *, top: Optional[int] = None, skip: Optional[int] = None, maxpagesize: Optional[int] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/indexes" + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + if top is not None: + _params["top"] = _SERIALIZER.query("top", top, "int") + if skip is not None: + _params["skip"] = _SERIALIZER.query("skip", skip, "int") + if maxpagesize is not None: + _params["maxpagesize"] = _SERIALIZER.query("maxpagesize", maxpagesize, "int") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_model_containers_get_request(name: str, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/models/{name}" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_model_containers_create_or_update_request( # pylint: disable=name-too-long + name: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/models/{name}" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PUT", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_model_versions_list_request( + name: str, + *, + _skip: Optional[str] = None, + _order_by: Optional[str] = None, + _top: Optional[int] = None, + version: Optional[str] = None, + description: Optional[str] = None, + offset: Optional[int] = None, + tags: Optional[str] = None, + properties: Optional[str] = None, + feed: Optional[str] = None, + list_view_type: Optional[Union[str, _models.ListViewType]] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/models/{name}/versions" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + if _skip is not None: + _params["$skip"] = _SERIALIZER.query("skip", _skip, "str") + if _order_by is not None: + _params["$orderBy"] = _SERIALIZER.query("order_by", _order_by, "str") + if _top is not None: + _params["$top"] = _SERIALIZER.query("top", _top, "int") + if version is not None: + _params["version"] = _SERIALIZER.query("version", version, "str") + if description is not None: + _params["description"] = _SERIALIZER.query("description", description, "str") + if offset is not None: + _params["offset"] = _SERIALIZER.query("offset", offset, "int") + if tags is not None: + _params["tags"] = _SERIALIZER.query("tags", tags, "str") + if properties is not None: + _params["properties"] = _SERIALIZER.query("properties", properties, "str") + if feed is not None: + _params["feed"] = _SERIALIZER.query("feed", feed, "str") + if list_view_type is not None: + _params["listViewType"] = _SERIALIZER.query("list_view_type", list_view_type, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_create_assistant_request( # pylint: disable=name-too-long + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/assistants" + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_list_assistants_request( # pylint: disable=name-too-long + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/assistants" + + # Construct parameters + if limit is not None: + _params["limit"] = _SERIALIZER.query("limit", limit, "int") + if order is not None: + _params["order"] = _SERIALIZER.query("order", order, "str") + if after is not None: + _params["after"] = _SERIALIZER.query("after", after, "str") + if before is not None: + _params["before"] = _SERIALIZER.query("before", before, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_get_assistant_request( # pylint: disable=name-too-long + assistant_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/assistants/{assistantId}" + path_format_arguments = { + "assistantId": _SERIALIZER.url("assistant_id", assistant_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_update_assistant_request( # pylint: disable=name-too-long + assistant_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/assistants/{assistantId}" + path_format_arguments = { + "assistantId": _SERIALIZER.url("assistant_id", assistant_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_delete_assistant_request( # pylint: disable=name-too-long + assistant_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/assistants/{assistantId}" + path_format_arguments = { + "assistantId": _SERIALIZER.url("assistant_id", assistant_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_list_files_request( # pylint: disable=name-too-long + *, purpose: Optional[Union[str, _models.FilePurpose]] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/files" + + # Construct parameters + if purpose is not None: + _params["purpose"] = _SERIALIZER.query("purpose", purpose, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_upload_file_request(**kwargs: Any) -> HttpRequest: # pylint: disable=name-too-long + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/files" + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_delete_file_request( # pylint: disable=name-too-long + file_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/files/{fileId}" + path_format_arguments = { + "fileId": _SERIALIZER.url("file_id", file_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_get_file_request( # pylint: disable=name-too-long + file_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/files/{fileId}" + path_format_arguments = { + "fileId": _SERIALIZER.url("file_id", file_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_get_file_content_request( # pylint: disable=name-too-long + file_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/files/{fileId}/content" + path_format_arguments = { + "fileId": _SERIALIZER.url("file_id", file_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_get_online_endpoint_request( # pylint: disable=name-too-long + name: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/onlineEndpoints/{name}" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_delete_online_endpoint_request( # pylint: disable=name-too-long + name: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/onlineEndpoints/{name}" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_update_online_endpoint_request( # pylint: disable=name-too-long + name: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/onlineEndpoints/{name}" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PATCH", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_create_or_update_online_endpoint_request( # pylint: disable=name-too-long + name: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/onlineEndpoints/{name}" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PUT", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_list_online_endpoints_request( # pylint: disable=name-too-long + *, + name: Optional[str] = None, + count: Optional[int] = None, + compute_type: Optional[Union[str, _models.EndpointComputeType]] = None, + tags: Optional[str] = None, + properties: Optional[str] = None, + order_by: Optional[Union[str, _models.OrderString]] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/onlineEndpoints" + + # Construct parameters + if name is not None: + _params["name"] = _SERIALIZER.query("name", name, "str") + if count is not None: + _params["count"] = _SERIALIZER.query("count", count, "int") + if compute_type is not None: + _params["computeType"] = _SERIALIZER.query("compute_type", compute_type, "str") + if tags is not None: + _params["tags"] = _SERIALIZER.query("tags", tags, "str") + if properties is not None: + _params["properties"] = _SERIALIZER.query("properties", properties, "str") + if order_by is not None: + _params["orderBy"] = _SERIALIZER.query("order_by", order_by, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_list_keys_online_endpoint_request( # pylint: disable=name-too-long + name: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/onlineEndpoints/{name}/listKeys" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_regenerate_keys_online_endpoint_request( # pylint: disable=name-too-long + name: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/onlineEndpoints/{name}/regenerateKeys" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_get_token_online_endpoint_request( # pylint: disable=name-too-long + name: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/onlineEndpoints/{name}/token" + path_format_arguments = { + "name": _SERIALIZER.url("name", name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PUT", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_list_online_deployments_request( # pylint: disable=name-too-long + endpoint_name: str, *, _order_by: Optional[str] = None, _top: Optional[int] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/onlineEndpoints/{endpointName}/deployments" + path_format_arguments = { + "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if _order_by is not None: + _params["$orderBy"] = _SERIALIZER.query("order_by", _order_by, "str") + if _top is not None: + _params["$top"] = _SERIALIZER.query("top", _top, "int") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_delete_online_deployment_request( # pylint: disable=name-too-long + endpoint_name: str, deployment_name: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/onlineEndpoints/{endpointName}/deployments/{deploymentName}" + path_format_arguments = { + "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, "str"), + "deploymentName": _SERIALIZER.url("deployment_name", deployment_name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_get_online_deployment_request( # pylint: disable=name-too-long + endpoint_name: str, deployment_name: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/onlineEndpoints/{endpointName}/deployments/{deploymentName}" + path_format_arguments = { + "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, "str"), + "deploymentName": _SERIALIZER.url("deployment_name", deployment_name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_update_online_deployment_request( # pylint: disable=name-too-long + endpoint_name: str, deployment_name: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/onlineEndpoints/{endpointName}/deployments/{deploymentName}" + path_format_arguments = { + "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, "str"), + "deploymentName": _SERIALIZER.url("deployment_name", deployment_name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PATCH", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_create_or_update_online_deployment_request( # pylint: disable=name-too-long + endpoint_name: str, deployment_name: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/onlineEndpoints/{endpointName}/deployments/{deploymentName}" + path_format_arguments = { + "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, "str"), + "deploymentName": _SERIALIZER.url("deployment_name", deployment_name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PUT", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_poll_logs_online_deployment_request( # pylint: disable=name-too-long + endpoint_name: str, deployment_name: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/onlineEndpoints/{endpointName}/deployments/{deploymentName}/getLogs" + path_format_arguments = { + "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, "str"), + "deploymentName": _SERIALIZER.url("deployment_name", deployment_name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_get_skus_online_deployment_request( # pylint: disable=name-too-long + endpoint_name: str, deployment_name: str, *, count: int, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/onlineEndpoints/{endpointName}/deployments/{deploymentName}/skus" + path_format_arguments = { + "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, "str"), + "deploymentName": _SERIALIZER.url("deployment_name", deployment_name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + _params["count"] = _SERIALIZER.query("count", count, "int") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_get_batch_endpoint_request( # pylint: disable=name-too-long + endpoint_name: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/batchEndpoints/{endpointName}" + path_format_arguments = { + "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_delete_batch_endpoint_request( # pylint: disable=name-too-long + endpoint_name: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/batchEndpoints/{endpointName}" + path_format_arguments = { + "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_update_batch_endpoint_request( # pylint: disable=name-too-long + endpoint_name: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/batchEndpoints/{endpointName}" + path_format_arguments = { + "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PATCH", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_create_or_update_batch_endpoint_request( # pylint: disable=name-too-long + endpoint_name: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/batchEndpoints/{endpointName}" + path_format_arguments = { + "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PUT", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_list_batch_endpoints_request( # pylint: disable=name-too-long + *, count: Optional[int] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/batchEndpoints" + + # Construct parameters + if count is not None: + _params["count"] = _SERIALIZER.query("count", count, "int") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_list_batch_deployments_request( # pylint: disable=name-too-long + endpoint_name: str, + *, + _order_by: Optional[str] = None, + _top: Optional[int] = None, + _skip: Optional[str] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/batchEndpoints/{endpointName}/deployments" + path_format_arguments = { + "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if _order_by is not None: + _params["$orderBy"] = _SERIALIZER.query("order_by", _order_by, "str") + if _top is not None: + _params["$top"] = _SERIALIZER.query("top", _top, "int") + if _skip is not None: + _params["$skip"] = _SERIALIZER.query("skip", _skip, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_delete_batch_deployment_request( # pylint: disable=name-too-long + endpoint_name: str, deployment_name: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/batchEndpoints/{endpointName}/deployments/{deploymentName}" + path_format_arguments = { + "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, "str"), + "deploymentName": _SERIALIZER.url("deployment_name", deployment_name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_get_batch_deployment_request( # pylint: disable=name-too-long + endpoint_name: str, deployment_name: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/batchEndpoints/{endpointName}/deployments/{deploymentName}" + path_format_arguments = { + "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, "str"), + "deploymentName": _SERIALIZER.url("deployment_name", deployment_name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_update_batch_deployment_request( # pylint: disable=name-too-long + endpoint_name: str, deployment_name: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/batchEndpoints/{endpointName}/deployments/{deploymentName}" + path_format_arguments = { + "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, "str"), + "deploymentName": _SERIALIZER.url("deployment_name", deployment_name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PATCH", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_create_or_update_batch_deployment_request( # pylint: disable=name-too-long + endpoint_name: str, deployment_name: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-11-01-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/batchEndpoints/{endpointName}/deployments/{deploymentName}" + path_format_arguments = { + "endpointName": _SERIALIZER.url("endpoint_name", endpoint_name, "str"), + "deploymentName": _SERIALIZER.url("deployment_name", deployment_name, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PUT", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_create_message_request( # pylint: disable=name-too-long + thread_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/messages" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_list_messages_request( # pylint: disable=name-too-long + thread_id: str, + *, + run_id: Optional[str] = None, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/messages" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if run_id is not None: + _params["runId"] = _SERIALIZER.query("run_id", run_id, "str") + if limit is not None: + _params["limit"] = _SERIALIZER.query("limit", limit, "int") + if order is not None: + _params["order"] = _SERIALIZER.query("order", order, "str") + if after is not None: + _params["after"] = _SERIALIZER.query("after", after, "str") + if before is not None: + _params["before"] = _SERIALIZER.query("before", before, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_get_message_request( # pylint: disable=name-too-long + thread_id: str, message_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/messages/{messageId}" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + "messageId": _SERIALIZER.url("message_id", message_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_update_message_request( # pylint: disable=name-too-long + thread_id: str, message_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/messages/{messageId}" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + "messageId": _SERIALIZER.url("message_id", message_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_get_run_step_request( # pylint: disable=name-too-long + thread_id: str, run_id: str, step_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/runs/{runId}/steps/{stepId}" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + "runId": _SERIALIZER.url("run_id", run_id, "str"), + "stepId": _SERIALIZER.url("step_id", step_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_list_run_steps_request( # pylint: disable=name-too-long + thread_id: str, + run_id: str, + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/runs/{runId}/steps" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + "runId": _SERIALIZER.url("run_id", run_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if limit is not None: + _params["limit"] = _SERIALIZER.query("limit", limit, "int") + if order is not None: + _params["order"] = _SERIALIZER.query("order", order, "str") + if after is not None: + _params["after"] = _SERIALIZER.query("after", after, "str") + if before is not None: + _params["before"] = _SERIALIZER.query("before", before, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_create_run_request( # pylint: disable=name-too-long + thread_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/runs" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_list_runs_request( # pylint: disable=name-too-long + thread_id: str, + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/runs" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if limit is not None: + _params["limit"] = _SERIALIZER.query("limit", limit, "int") + if order is not None: + _params["order"] = _SERIALIZER.query("order", order, "str") + if after is not None: + _params["after"] = _SERIALIZER.query("after", after, "str") + if before is not None: + _params["before"] = _SERIALIZER.query("before", before, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_get_run_request( # pylint: disable=name-too-long + thread_id: str, run_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/runs/{runId}" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + "runId": _SERIALIZER.url("run_id", run_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_update_run_request( # pylint: disable=name-too-long + thread_id: str, run_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/runs/{runId}" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + "runId": _SERIALIZER.url("run_id", run_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_submit_tool_outputs_to_run_request( # pylint: disable=name-too-long + thread_id: str, run_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/runs/{runId}/submit_tool_outputs" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + "runId": _SERIALIZER.url("run_id", run_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_cancel_run_request( # pylint: disable=name-too-long + thread_id: str, run_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}/runs/{runId}/cancel" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + "runId": _SERIALIZER.url("run_id", run_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_create_thread_and_run_request( # pylint: disable=name-too-long + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/runs" + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_create_thread_request( # pylint: disable=name-too-long + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads" + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_get_thread_request( # pylint: disable=name-too-long + thread_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_update_thread_request( # pylint: disable=name-too-long + thread_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_delete_thread_request( # pylint: disable=name-too-long + thread_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/threads/{threadId}" + path_format_arguments = { + "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_list_vector_stores_request( # pylint: disable=name-too-long + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores" + + # Construct parameters + if limit is not None: + _params["limit"] = _SERIALIZER.query("limit", limit, "int") + if order is not None: + _params["order"] = _SERIALIZER.query("order", order, "str") + if after is not None: + _params["after"] = _SERIALIZER.query("after", after, "str") + if before is not None: + _params["before"] = _SERIALIZER.query("before", before, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_create_vector_store_request( # pylint: disable=name-too-long + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores" + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_get_vector_store_request( # pylint: disable=name-too-long + vector_store_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores/{vectorStoreId}" + path_format_arguments = { + "vectorStoreId": _SERIALIZER.url("vector_store_id", vector_store_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_modify_vector_store_request( # pylint: disable=name-too-long + vector_store_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores/{vectorStoreId}" + path_format_arguments = { + "vectorStoreId": _SERIALIZER.url("vector_store_id", vector_store_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_delete_vector_store_request( # pylint: disable=name-too-long + vector_store_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores/{vectorStoreId}" + path_format_arguments = { + "vectorStoreId": _SERIALIZER.url("vector_store_id", vector_store_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_list_vector_store_files_request( # pylint: disable=name-too-long + vector_store_id: str, + *, + filter: Optional[Union[str, _models.VectorStoreFileStatusFilter]] = None, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores/{vectorStoreId}/files" + path_format_arguments = { + "vectorStoreId": _SERIALIZER.url("vector_store_id", vector_store_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if filter is not None: + _params["filter"] = _SERIALIZER.query("filter", filter, "str") + if limit is not None: + _params["limit"] = _SERIALIZER.query("limit", limit, "int") + if order is not None: + _params["order"] = _SERIALIZER.query("order", order, "str") + if after is not None: + _params["after"] = _SERIALIZER.query("after", after, "str") + if before is not None: + _params["before"] = _SERIALIZER.query("before", before, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_machine_learning_services_create_vector_store_file_request( # pylint: disable=name-too-long + vector_store_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores/{vectorStoreId}/files" + path_format_arguments = { + "vectorStoreId": _SERIALIZER.url("vector_store_id", vector_store_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_get_vector_store_file_request( # pylint: disable=name-too-long + vector_store_id: str, file_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores/{vectorStoreId}/files/{fileId}" + path_format_arguments = { + "vectorStoreId": _SERIALIZER.url("vector_store_id", vector_store_id, "str"), + "fileId": _SERIALIZER.url("file_id", file_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_delete_vector_store_file_request( # pylint: disable=name-too-long + vector_store_id: str, file_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores/{vectorStoreId}/files/{fileId}" + path_format_arguments = { + "vectorStoreId": _SERIALIZER.url("vector_store_id", vector_store_id, "str"), + "fileId": _SERIALIZER.url("file_id", file_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_create_vector_store_file_batch_request( # pylint: disable=name-too-long + vector_store_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores/{vectorStoreId}/file_batches" + path_format_arguments = { + "vectorStoreId": _SERIALIZER.url("vector_store_id", vector_store_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_get_vector_store_file_batch_request( # pylint: disable=name-too-long + vector_store_id: str, batch_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores/{vectorStoreId}/file_batches/{batchId}" + path_format_arguments = { + "vectorStoreId": _SERIALIZER.url("vector_store_id", vector_store_id, "str"), + "batchId": _SERIALIZER.url("batch_id", batch_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_cancel_vector_store_file_batch_request( # pylint: disable=name-too-long + vector_store_id: str, batch_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores/{vectorStoreId}/file_batches/{batchId}/cancel" + path_format_arguments = { + "vectorStoreId": _SERIALIZER.url("vector_store_id", vector_store_id, "str"), + "batchId": _SERIALIZER.url("batch_id", batch_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_machine_learning_services_list_vector_store_file_batch_files_request( # pylint: disable=name-too-long + vector_store_id: str, + batch_id: str, + *, + filter: Optional[Union[str, _models.VectorStoreFileStatusFilter]] = None, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/vector_stores/{vectorStoreId}/file_batches/{batchId}/files" + path_format_arguments = { + "vectorStoreId": _SERIALIZER.url("vector_store_id", vector_store_id, "str"), + "batchId": _SERIALIZER.url("batch_id", batch_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if filter is not None: + _params["filter"] = _SERIALIZER.query("filter", filter, "str") + if limit is not None: + _params["limit"] = _SERIALIZER.query("limit", limit, "int") + if order is not None: + _params["order"] = _SERIALIZER.query("order", order, "str") + if after is not None: + _params["after"] = _SERIALIZER.query("after", after, "str") + if before is not None: + _params["before"] = _SERIALIZER.query("before", before, "str") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +class ConnectionsOperations: + """ + .. warning:: + **DO NOT** instantiate this class directly. + + Instead, you should access the following operations through + :class:`~azure.ai.resources.autogen.MachineLearningServicesClient`'s + :attr:`connections` attribute. + """ + + def __init__(self, *args, **kwargs): + input_args = list(args) + self._client = input_args.pop(0) if input_args else kwargs.pop("client") + self._config = input_args.pop(0) if input_args else kwargs.pop("config") + self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer") + self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer") + + @distributed_trace + def get(self, name: str, **kwargs: Any) -> _models.Connection: + """Get a connection by name. + + :param name: Name of the connection. Required. + :type name: str + :return: Connection. The Connection is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Connection + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.Connection] = kwargs.pop("cls", None) + + _request = build_connections_get_request( + name=name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Connection, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def post(self, name: str, **kwargs: Any) -> _models.Connection: + """Get a connection with credentials by name. + + :param name: Name of the connection. Required. + :type name: str + :return: Connection. The Connection is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Connection + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.Connection] = kwargs.pop("cls", None) + + _request = build_connections_post_request( + name=name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Connection, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list( + self, *, top: Optional[int] = None, skip: Optional[int] = None, **kwargs: Any + ) -> Iterable["_models.Connection"]: + """List all connections in the project. + + :keyword top: The number of result items to return. Default value is None. + :paramtype top: int + :keyword skip: The number of result items to skip. Default value is None. + :paramtype skip: int + :return: An iterator like instance of Connection + :rtype: ~azure.core.paging.ItemPaged[~azure.ai.resources.autogen.models.Connection] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + maxpagesize = kwargs.pop("maxpagesize", None) + cls: ClsType[List[_models.Connection]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_connections_list_request( + top=top, + skip=skip, + maxpagesize=maxpagesize, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.Connection], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, iter(list_of_elem) + + def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return ItemPaged(get_next, extract_data) + + @distributed_trace + def list_with_credentials( + self, *, top: Optional[int] = None, skip: Optional[int] = None, **kwargs: Any + ) -> Iterable["_models.Connection"]: + """List all connections with credentials in the project. + + :keyword top: The number of result items to return. Default value is None. + :paramtype top: int + :keyword skip: The number of result items to skip. Default value is None. + :paramtype skip: int + :return: An iterator like instance of Connection + :rtype: ~azure.core.paging.ItemPaged[~azure.ai.resources.autogen.models.Connection] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + maxpagesize = kwargs.pop("maxpagesize", None) + cls: ClsType[List[_models.Connection]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_connections_list_with_credentials_request( + top=top, + skip=skip, + maxpagesize=maxpagesize, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.Connection], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, iter(list_of_elem) + + def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return ItemPaged(get_next, extract_data) + + +class DataOperations: + """ + .. warning:: + **DO NOT** instantiate this class directly. + + Instead, you should access the following operations through + :class:`~azure.ai.resources.autogen.MachineLearningServicesClient`'s + :attr:`data` attribute. + """ + + def __init__(self, *args, **kwargs): + input_args = list(args) + self._client = input_args.pop(0) if input_args else kwargs.pop("client") + self._config = input_args.pop(0) if input_args else kwargs.pop("config") + self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer") + self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer") + + @distributed_trace + def list( + self, + *, + _skip: Optional[str] = None, + list_view_type: Optional[Union[str, _models.ListViewType]] = None, + **kwargs: Any, + ) -> Iterable["_models.DataContainer"]: + """List data containers. + + :keyword _skip: Continuation token for pagination. Default value is None. + :paramtype _skip: str + :keyword list_view_type: View type for including/excluding (for example) archived entities. + Known values are: "ActiveOnly", "ArchivedOnly", and "All". Default value is None. + :paramtype list_view_type: str or ~azure.ai.resources.autogen.models.ListViewType + :return: An iterator like instance of DataContainer + :rtype: ~azure.core.paging.ItemPaged[~azure.ai.resources.autogen.models.DataContainer] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.DataContainer]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_data_list_request( + _skip=_skip, + list_view_type=list_view_type, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.DataContainer], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, iter(list_of_elem) + + def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return ItemPaged(get_next, extract_data) + + @distributed_trace + def delete( # pylint: disable=inconsistent-return-statements + self, workspace_name: str, name: str, **kwargs: Any + ) -> None: + """Delete container. + + :param workspace_name: Name of Azure Machine Learning workspace. Required. + :type workspace_name: str + :param name: Container name. Required. + :type name: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_data_delete_request( + workspace_name=workspace_name, + name=name, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [204]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get(self, name: str, **kwargs: Any) -> _models.DataContainer: + """Get container. + + :param name: Container name. Required. + :type name: str + :return: DataContainer. The DataContainer is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DataContainer + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.DataContainer] = kwargs.pop("cls", None) + + _request = build_data_get_request( + name=name, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.DataContainer, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def create_or_update( + self, name: str, body: _models.DataContainer, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.DataContainer: + """Create or update container. + + :param name: Container name. Required. + :type name: str + :param body: Container entity to create or update. Required. + :type body: ~azure.ai.resources.autogen.models.DataContainer + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: DataContainer. The DataContainer is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DataContainer + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_or_update( + self, name: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.DataContainer: + """Create or update container. + + :param name: Container name. Required. + :type name: str + :param body: Container entity to create or update. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: DataContainer. The DataContainer is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DataContainer + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_or_update( + self, name: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.DataContainer: + """Create or update container. + + :param name: Container name. Required. + :type name: str + :param body: Container entity to create or update. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: DataContainer. The DataContainer is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DataContainer + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_or_update( + self, name: str, body: Union[_models.DataContainer, JSON, IO[bytes]], **kwargs: Any + ) -> _models.DataContainer: + """Create or update container. + + :param name: Container name. Required. + :type name: str + :param body: Container entity to create or update. Is one of the following types: + DataContainer, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.DataContainer or JSON or IO[bytes] + :return: DataContainer. The DataContainer is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DataContainer + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.DataContainer] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_data_create_or_update_request( + name=name, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.DataContainer, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + +class DataVersionsBaseOperations: + """ + .. warning:: + **DO NOT** instantiate this class directly. + + Instead, you should access the following operations through + :class:`~azure.ai.resources.autogen.MachineLearningServicesClient`'s + :attr:`data_versions_base` attribute. + """ + + def __init__(self, *args, **kwargs): + input_args = list(args) + self._client = input_args.pop(0) if input_args else kwargs.pop("client") + self._config = input_args.pop(0) if input_args else kwargs.pop("config") + self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer") + self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer") + + @distributed_trace + def list( + self, + name: str, + *, + _order_by: Optional[str] = None, + _top: Optional[int] = None, + _skip: Optional[str] = None, + _tags: Optional[str] = None, + list_view_type: Optional[Union[str, _models.ListViewType]] = None, + **kwargs: Any, + ) -> Iterable["_models.DataVersionBase"]: + """List data versions in the data container. + + :param name: Container name. Required. + :type name: str + :keyword _order_by: Please choose OrderBy value from ['createdtime', 'modifiedtime']. Default + value is None. + :paramtype _order_by: str + :keyword _top: Top count of results, top count cannot be greater than the page size. If + topCount > page size, results with be default page size count will be returned. Default value + is None. + :paramtype _top: int + :keyword _skip: Continuation token for pagination. Default value is None. + :paramtype _skip: str + :keyword _tags: Comma-separated list of tag names (and optionally values). Example: + tag1,tag2=value2. Default value is None. + :paramtype _tags: str + :keyword list_view_type: [ListViewType.ActiveOnly, ListViewType.ArchivedOnly, ListViewType.All] + View type for including/excluding (for example) archived entities. Known values are: + "ActiveOnly", "ArchivedOnly", and "All". Default value is None. + :paramtype list_view_type: str or ~azure.ai.resources.autogen.models.ListViewType + :return: An iterator like instance of DataVersionBase + :rtype: ~azure.core.paging.ItemPaged[~azure.ai.resources.autogen.models.DataVersionBase] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.DataVersionBase]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_data_versions_base_list_request( + name=name, + _order_by=_order_by, + _top=_top, + _skip=_skip, + _tags=_tags, + list_view_type=list_view_type, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.DataVersionBase], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, iter(list_of_elem) + + def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return ItemPaged(get_next, extract_data) + + @distributed_trace + def delete(self, name: str, version: str, **kwargs: Any) -> None: # pylint: disable=inconsistent-return-statements + """Delete version. + + :param name: Container name. Required. + :type name: str + :param version: Version identifier. Required. + :type version: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_data_versions_base_delete_request( + name=name, + version=version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [204]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get(self, name: str, version: str, **kwargs: Any) -> _models.DataVersionBase: + """Get version. + + :param name: Container name. Required. + :type name: str + :param version: Version identifier. Required. + :type version: str + :return: DataVersionBase. The DataVersionBase is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DataVersionBase + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.DataVersionBase] = kwargs.pop("cls", None) + + _request = build_data_versions_base_get_request( + name=name, + version=version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.DataVersionBase, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def create_or_update( + self, + name: str, + version: str, + body: _models.DataVersionBase, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.DataVersionBase: + """Create or update version. + + :param name: Container name. Required. + :type name: str + :param version: Version identifier. Required. + :type version: str + :param body: Version entity to create or update. Required. + :type body: ~azure.ai.resources.autogen.models.DataVersionBase + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: DataVersionBase. The DataVersionBase is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DataVersionBase + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_or_update( + self, name: str, version: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.DataVersionBase: + """Create or update version. + + :param name: Container name. Required. + :type name: str + :param version: Version identifier. Required. + :type version: str + :param body: Version entity to create or update. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: DataVersionBase. The DataVersionBase is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DataVersionBase + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_or_update( + self, name: str, version: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.DataVersionBase: + """Create or update version. + + :param name: Container name. Required. + :type name: str + :param version: Version identifier. Required. + :type version: str + :param body: Version entity to create or update. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: DataVersionBase. The DataVersionBase is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DataVersionBase + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_or_update( + self, name: str, version: str, body: Union[_models.DataVersionBase, JSON, IO[bytes]], **kwargs: Any + ) -> _models.DataVersionBase: + """Create or update version. + + :param name: Container name. Required. + :type name: str + :param version: Version identifier. Required. + :type version: str + :param body: Version entity to create or update. Is one of the following types: + DataVersionBase, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.DataVersionBase or JSON or IO[bytes] + :return: DataVersionBase. The DataVersionBase is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DataVersionBase + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.DataVersionBase] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_data_versions_base_create_or_update_request( + name=name, + version=version, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.DataVersionBase, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def publish( + self, + name: str, + version: str, + body: _models.DestinationAsset, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> None: + """Publish version asset into registry. + + :param name: Container name. Required. + :type name: str + :param version: Version identifier. Required. + :type version: str + :param body: Destination registry info. Required. + :type body: ~azure.ai.resources.autogen.models.DestinationAsset + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def publish( + self, name: str, version: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Publish version asset into registry. + + :param name: Container name. Required. + :type name: str + :param version: Version identifier. Required. + :type version: str + :param body: Destination registry info. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def publish( + self, name: str, version: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Publish version asset into registry. + + :param name: Container name. Required. + :type name: str + :param version: Version identifier. Required. + :type version: str + :param body: Destination registry info. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def publish( # pylint: disable=inconsistent-return-statements + self, name: str, version: str, body: Union[_models.DestinationAsset, JSON, IO[bytes]], **kwargs: Any + ) -> None: + """Publish version asset into registry. + + :param name: Container name. Required. + :type name: str + :param version: Version identifier. Required. + :type version: str + :param body: Destination registry info. Is one of the following types: DestinationAsset, JSON, + IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.DestinationAsset or JSON or IO[bytes] + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_data_versions_base_publish_request( + name=name, + version=version, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [204]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + +class EvaluationsOperations: + """ + .. warning:: + **DO NOT** instantiate this class directly. + + Instead, you should access the following operations through + :class:`~azure.ai.resources.autogen.MachineLearningServicesClient`'s + :attr:`evaluations` attribute. + """ + + def __init__(self, *args, **kwargs): + input_args = list(args) + self._client = input_args.pop(0) if input_args else kwargs.pop("client") + self._config = input_args.pop(0) if input_args else kwargs.pop("config") + self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer") + self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer") + + @distributed_trace + def get(self, id: str, **kwargs: Any) -> _models.Evaluation: + """Get an evaluation. + + :param id: Identifier of the evaluation. Required. + :type id: str + :return: Evaluation. The Evaluation is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Evaluation + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.Evaluation] = kwargs.pop("cls", None) + + _request = build_evaluations_get_request( + id=id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Evaluation, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def create( + self, body: _models.Evaluation, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Evaluation: + """Creates an evaluation. + + :param body: Properties of Evaluation. Required. + :type body: ~azure.ai.resources.autogen.models.Evaluation + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Evaluation. The Evaluation is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Evaluation + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create(self, body: JSON, *, content_type: str = "application/json", **kwargs: Any) -> _models.Evaluation: + """Creates an evaluation. + + :param body: Properties of Evaluation. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Evaluation. The Evaluation is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Evaluation + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create(self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any) -> _models.Evaluation: + """Creates an evaluation. + + :param body: Properties of Evaluation. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: Evaluation. The Evaluation is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Evaluation + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create(self, body: Union[_models.Evaluation, JSON, IO[bytes]], **kwargs: Any) -> _models.Evaluation: + """Creates an evaluation. + + :param body: Properties of Evaluation. Is one of the following types: Evaluation, JSON, + IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.Evaluation or JSON or IO[bytes] + :return: Evaluation. The Evaluation is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Evaluation + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.Evaluation] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_evaluations_create_request( + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [201]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Evaluation, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list( + self, *, top: Optional[int] = None, skip: Optional[int] = None, **kwargs: Any + ) -> Iterable["_models.Evaluation"]: + """List evaluations. + + :keyword top: The number of result items to return. Default value is None. + :paramtype top: int + :keyword skip: The number of result items to skip. Default value is None. + :paramtype skip: int + :return: An iterator like instance of Evaluation + :rtype: ~azure.core.paging.ItemPaged[~azure.ai.resources.autogen.models.Evaluation] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + maxpagesize = kwargs.pop("maxpagesize", None) + cls: ClsType[List[_models.Evaluation]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_evaluations_list_request( + top=top, + skip=skip, + maxpagesize=maxpagesize, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.Evaluation], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, iter(list_of_elem) + + def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return ItemPaged(get_next, extract_data) + + @overload + def update( + self, id: str, body: _models.UpdateEvaluationRequest, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Evaluation: + """Update an evaluation. + + :param id: Identifier of the evaluation. Required. + :type id: str + :param body: Update evaluation request. Required. + :type body: ~azure.ai.resources.autogen.models.UpdateEvaluationRequest + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Evaluation. The Evaluation is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Evaluation + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update( + self, id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Evaluation: + """Update an evaluation. + + :param id: Identifier of the evaluation. Required. + :type id: str + :param body: Update evaluation request. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Evaluation. The Evaluation is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Evaluation + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update( + self, id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Evaluation: + """Update an evaluation. + + :param id: Identifier of the evaluation. Required. + :type id: str + :param body: Update evaluation request. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: Evaluation. The Evaluation is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Evaluation + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update( + self, id: str, body: Union[_models.UpdateEvaluationRequest, JSON, IO[bytes]], **kwargs: Any + ) -> _models.Evaluation: + """Update an evaluation. + + :param id: Identifier of the evaluation. Required. + :type id: str + :param body: Update evaluation request. Is one of the following types: UpdateEvaluationRequest, + JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.UpdateEvaluationRequest or JSON or IO[bytes] + :return: Evaluation. The Evaluation is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Evaluation + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.Evaluation] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_evaluations_update_request( + id=id, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Evaluation, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + +class IndexesOperations: + """ + .. warning:: + **DO NOT** instantiate this class directly. + + Instead, you should access the following operations through + :class:`~azure.ai.resources.autogen.MachineLearningServicesClient`'s + :attr:`indexes` attribute. + """ + + def __init__(self, *args, **kwargs): + input_args = list(args) + self._client = input_args.pop(0) if input_args else kwargs.pop("client") + self._config = input_args.pop(0) if input_args else kwargs.pop("config") + self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer") + self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer") + + @distributed_trace + def get(self, name: str, version: str, **kwargs: Any) -> _models.Index: + """Get a specific version of an Index. + + :param name: Name of the index. Required. + :type name: str + :param version: Version of the index. Required. + :type version: str + :return: Index. The Index is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Index + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.Index] = kwargs.pop("cls", None) + + _request = build_indexes_get_request( + name=name, + version=version, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Index, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def create_or_update( + self, name: str, version: str, body: _models.Index, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Index: + """Creates or updates a IndexVersion. + + :param name: Name of the index. Required. + :type name: str + :param version: Version of the index. Required. + :type version: str + :param body: Properties of an Index Version. Required. + :type body: ~azure.ai.resources.autogen.models.Index + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Index. The Index is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Index + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_or_update( + self, name: str, version: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Index: + """Creates or updates a IndexVersion. + + :param name: Name of the index. Required. + :type name: str + :param version: Version of the index. Required. + :type version: str + :param body: Properties of an Index Version. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Index. The Index is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Index + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_or_update( + self, name: str, version: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Index: + """Creates or updates a IndexVersion. + + :param name: Name of the index. Required. + :type name: str + :param version: Version of the index. Required. + :type version: str + :param body: Properties of an Index Version. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: Index. The Index is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Index + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_or_update( + self, name: str, version: str, body: Union[_models.Index, JSON, IO[bytes]], **kwargs: Any + ) -> _models.Index: + """Creates or updates a IndexVersion. + + :param name: Name of the index. Required. + :type name: str + :param version: Version of the index. Required. + :type version: str + :param body: Properties of an Index Version. Is one of the following types: Index, JSON, + IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.Index or JSON or IO[bytes] + :return: Index. The Index is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Index + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.Index] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_indexes_create_or_update_request( + name=name, + version=version, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200, 201]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Index, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list( + self, + name: str, + *, + list_view_type: str, + order_by: Optional[str] = None, + orderby: Optional[str] = None, + tags: Optional[str] = None, + top: Optional[int] = None, + skip: Optional[int] = None, + **kwargs: Any, + ) -> Iterable["_models.Index"]: + """List the versions of an Index given the name. + + :param name: Name of the index. Required. + :type name: str + :keyword list_view_type: View type for including/excluding (for example) archived entities. + Required. + :paramtype list_view_type: str + :keyword order_by: Ordering of list: Please choose orderby value from ['createdAt', + 'lastModifiedAt']. Default value is None. + :paramtype order_by: str + :keyword orderby: Ordering of list: Please choose orderby value from ['createdAt', + 'lastModifiedAt']. Default value is None. + :paramtype orderby: str + :keyword tags: Comma-separated list of tag names (and optionally values). Example: + tag1,tag2=value2. Default value is None. + :paramtype tags: str + :keyword top: The number of result items to return. Default value is None. + :paramtype top: int + :keyword skip: The number of result items to skip. Default value is None. + :paramtype skip: int + :return: An iterator like instance of Index + :rtype: ~azure.core.paging.ItemPaged[~azure.ai.resources.autogen.models.Index] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + maxpagesize = kwargs.pop("maxpagesize", None) + cls: ClsType[List[_models.Index]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_indexes_list_request( + name=name, + list_view_type=list_view_type, + order_by=order_by, + orderby=orderby, + tags=tags, + top=top, + skip=skip, + maxpagesize=maxpagesize, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.Index], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, iter(list_of_elem) + + def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return ItemPaged(get_next, extract_data) + + @distributed_trace + def get_latest(self, name: str, **kwargs: Any) -> _models.Index: + """Get latest version of the Index. Latest is defined by most recent created by date. + + :param name: Name of the index. Required. + :type name: str + :return: Index. The Index is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Index + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.Index] = kwargs.pop("cls", None) + + _request = build_indexes_get_latest_request( + name=name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Index, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_next_version(self, name: str, **kwargs: Any) -> _models.VersionInfo: + """Get next Index version as defined by the server. The server keeps track of all versions that + are string-representations of integers. If one exists, the nextVersion will be a string + representation of the highest integer value + 1. Otherwise, the nextVersion will default to + '1'. + + :param name: Name of the index. Required. + :type name: str + :return: VersionInfo. The VersionInfo is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VersionInfo + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VersionInfo] = kwargs.pop("cls", None) + + _request = build_indexes_get_next_version_request( + name=name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VersionInfo, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_latest( + self, *, top: Optional[int] = None, skip: Optional[int] = None, **kwargs: Any + ) -> Iterable["_models.Index"]: + """List the latest version of each index. Latest is defined by most recent created by date. + + :keyword top: The number of result items to return. Default value is None. + :paramtype top: int + :keyword skip: The number of result items to skip. Default value is None. + :paramtype skip: int + :return: An iterator like instance of Index + :rtype: ~azure.core.paging.ItemPaged[~azure.ai.resources.autogen.models.Index] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + maxpagesize = kwargs.pop("maxpagesize", None) + cls: ClsType[List[_models.Index]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_indexes_list_latest_request( + top=top, + skip=skip, + maxpagesize=maxpagesize, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.Index], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, iter(list_of_elem) + + def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return ItemPaged(get_next, extract_data) + + +class ModelContainersOperations: + """ + .. warning:: + **DO NOT** instantiate this class directly. + + Instead, you should access the following operations through + :class:`~azure.ai.resources.autogen.MachineLearningServicesClient`'s + :attr:`model_containers` attribute. + """ + + def __init__(self, *args, **kwargs): + input_args = list(args) + self._client = input_args.pop(0) if input_args else kwargs.pop("client") + self._config = input_args.pop(0) if input_args else kwargs.pop("config") + self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer") + self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer") + + @distributed_trace + def get(self, name: str, **kwargs: Any) -> _models.ModelContainer: + """Get a model container. + + :param name: Name of the model container. Required. + :type name: str + :return: ModelContainer. The ModelContainer is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ModelContainer + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ModelContainer] = kwargs.pop("cls", None) + + _request = build_model_containers_get_request( + name=name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ModelContainer, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def create_or_update(self, name: str, **kwargs: Any) -> _models.ModelContainer: + """Creates or updates a model container. + + :param name: Name of the model container. Required. + :type name: str + :return: ModelContainer. The ModelContainer is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ModelContainer + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ModelContainer] = kwargs.pop("cls", None) + + _request = build_model_containers_create_or_update_request( + name=name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200, 201]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ModelContainer, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + +class ModelVersionsOperations: + """ + .. warning:: + **DO NOT** instantiate this class directly. + + Instead, you should access the following operations through + :class:`~azure.ai.resources.autogen.MachineLearningServicesClient`'s + :attr:`model_versions` attribute. + """ + + def __init__(self, *args, **kwargs): + input_args = list(args) + self._client = input_args.pop(0) if input_args else kwargs.pop("client") + self._config = input_args.pop(0) if input_args else kwargs.pop("config") + self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer") + self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer") + + @distributed_trace + def list( + self, + name: str, + *, + _skip: Optional[str] = None, + _order_by: Optional[str] = None, + _top: Optional[int] = None, + version: Optional[str] = None, + description: Optional[str] = None, + offset: Optional[int] = None, + tags: Optional[str] = None, + properties: Optional[str] = None, + feed: Optional[str] = None, + list_view_type: Optional[Union[str, _models.ListViewType]] = None, + **kwargs: Any, + ) -> Iterable["_models.ModelVersion"]: + """List model versions. + + :param name: Name of the model container. Required. + :type name: str + :keyword _skip: $skip. Default value is None. + :paramtype _skip: str + :keyword _order_by: $orderBy. Default value is None. + :paramtype _order_by: str + :keyword _top: $top. Default value is None. + :paramtype _top: int + :keyword version: Model version. Default value is None. + :paramtype version: str + :keyword description: Model description. Default value is None. + :paramtype description: str + :keyword offset: Number of initial results to skip. Default value is None. + :paramtype offset: int + :keyword tags: Comma-separated list of tag names (and optionally values). Example: + tag1,tag2=value2. Default value is None. + :paramtype tags: str + :keyword properties: Comma-separated list of property names (and optionally values). Example: + prop1,prop2=value2. Default value is None. + :paramtype properties: str + :keyword feed: Name of the feed. Default value is None. + :paramtype feed: str + :keyword list_view_type: View type for including/excluding (for example) archived entities. + Known values are: "ActiveOnly", "ArchivedOnly", and "All". Default value is None. + :paramtype list_view_type: str or ~azure.ai.resources.autogen.models.ListViewType + :return: An iterator like instance of ModelVersion + :rtype: ~azure.core.paging.ItemPaged[~azure.ai.resources.autogen.models.ModelVersion] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.ModelVersion]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_model_versions_list_request( + name=name, + _skip=_skip, + _order_by=_order_by, + _top=_top, + version=version, + description=description, + offset=offset, + tags=tags, + properties=properties, + feed=feed, + list_view_type=list_view_type, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.ModelVersion], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, iter(list_of_elem) + + def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return ItemPaged(get_next, extract_data) + + +class MachineLearningServicesClientOperationsMixin( # pylint: disable=too-many-public-methods,name-too-long + MachineLearningServicesClientMixinABC +): + + @overload + def create_assistant( + self, body: _models.AssistantCreationOptions, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Assistant: + """Creates a new assistant. + + :param body: The request details to use when creating a new assistant. Required. + :type body: ~azure.ai.resources.autogen.models.AssistantCreationOptions + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_assistant( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Assistant: + """Creates a new assistant. + + :param body: The request details to use when creating a new assistant. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_assistant( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Assistant: + """Creates a new assistant. + + :param body: The request details to use when creating a new assistant. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_assistant( + self, body: Union[_models.AssistantCreationOptions, JSON, IO[bytes]], **kwargs: Any + ) -> _models.Assistant: + """Creates a new assistant. + + :param body: The request details to use when creating a new assistant. Is one of the following + types: AssistantCreationOptions, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.AssistantCreationOptions or JSON or IO[bytes] + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.Assistant] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_assistant_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Assistant, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_assistants( + self, + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any, + ) -> _models.OpenAIPageableListOfAssistant: + """Gets a list of assistants that were previously created. + + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.resources.autogen.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfAssistant. The OpenAIPageableListOfAssistant is compatible with + MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OpenAIPageableListOfAssistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfAssistant] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_list_assistants_request( + limit=limit, + order=order, + after=after, + before=before, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfAssistant, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_assistant(self, assistant_id: str, **kwargs: Any) -> _models.Assistant: + """Retrieves an existing assistant. + + :param assistant_id: The ID of the assistant to retrieve. Required. + :type assistant_id: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.Assistant] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_assistant_request( + assistant_id=assistant_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Assistant, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def update_assistant( + self, + assistant_id: str, + body: _models.UpdateAssistantOptions, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.Assistant: + """Modifies an existing assistant. + + :param assistant_id: The ID of the assistant to modify. Required. + :type assistant_id: str + :param body: The request details to use when modifying an existing assistant. Required. + :type body: ~azure.ai.resources.autogen.models.UpdateAssistantOptions + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_assistant( + self, assistant_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Assistant: + """Modifies an existing assistant. + + :param assistant_id: The ID of the assistant to modify. Required. + :type assistant_id: str + :param body: The request details to use when modifying an existing assistant. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_assistant( + self, assistant_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.Assistant: + """Modifies an existing assistant. + + :param assistant_id: The ID of the assistant to modify. Required. + :type assistant_id: str + :param body: The request details to use when modifying an existing assistant. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_assistant( + self, assistant_id: str, body: Union[_models.UpdateAssistantOptions, JSON, IO[bytes]], **kwargs: Any + ) -> _models.Assistant: + """Modifies an existing assistant. + + :param assistant_id: The ID of the assistant to modify. Required. + :type assistant_id: str + :param body: The request details to use when modifying an existing assistant. Is one of the + following types: UpdateAssistantOptions, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.UpdateAssistantOptions or JSON or IO[bytes] + :return: Assistant. The Assistant is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.Assistant + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.Assistant] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_update_assistant_request( + assistant_id=assistant_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.Assistant, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def delete_assistant(self, assistant_id: str, **kwargs: Any) -> _models.AssistantDeletionStatus: + """Deletes an assistant. + + :param assistant_id: The ID of the assistant to delete. Required. + :type assistant_id: str + :return: AssistantDeletionStatus. The AssistantDeletionStatus is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.AssistantDeletionStatus + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.AssistantDeletionStatus] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_delete_assistant_request( + assistant_id=assistant_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AssistantDeletionStatus, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_files( + self, *, purpose: Optional[Union[str, _models.FilePurpose]] = None, **kwargs: Any + ) -> _models.FileListResponse: + """Gets a list of previously uploaded files. + + :keyword purpose: A value that, when provided, limits list results to files matching the + corresponding purpose. Known values are: "fine-tune", "fine-tune-results", "assistants", + "assistants_output", "batch", "batch_output", and "vision". Default value is None. + :paramtype purpose: str or ~azure.ai.resources.autogen.models.FilePurpose + :return: FileListResponse. The FileListResponse is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.FileListResponse + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.FileListResponse] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_list_files_request( + purpose=purpose, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FileListResponse, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def upload_file(self, body: JSON, **kwargs: Any) -> _models.OpenAIFile: + """Uploads a file for use by other operations. + + :param body: Required. + :type body: JSON + :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OpenAIFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def upload_file( + self, *, file: FileType, purpose: Union[str, _models.FilePurpose], filename: Optional[str] = None, **kwargs: Any + ) -> _models.OpenAIFile: + """Uploads a file for use by other operations. + + :keyword file: The file data (not filename) to upload. Required. + :paramtype file: ~azure.ai.resources.autogen._vendor.FileType + :keyword purpose: The intended purpose of the file. Known values are: "fine-tune", + "fine-tune-results", "assistants", "assistants_output", "batch", "batch_output", and "vision". + Required. + :paramtype purpose: str or ~azure.ai.resources.autogen.models.FilePurpose + :keyword filename: A filename to associate with the uploaded data. Default value is None. + :paramtype filename: str + :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OpenAIFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def upload_file( + self, + body: JSON = _Unset, + *, + file: FileType = _Unset, + purpose: Union[str, _models.FilePurpose] = _Unset, + filename: Optional[str] = None, + **kwargs: Any, + ) -> _models.OpenAIFile: + """Uploads a file for use by other operations. + + :param body: Is one of the following types: JSON Required. + :type body: JSON + :keyword file: The file data (not filename) to upload. Required. + :paramtype file: ~azure.ai.resources.autogen._vendor.FileType + :keyword purpose: The intended purpose of the file. Known values are: "fine-tune", + "fine-tune-results", "assistants", "assistants_output", "batch", "batch_output", and "vision". + Required. + :paramtype purpose: str or ~azure.ai.resources.autogen.models.FilePurpose + :keyword filename: A filename to associate with the uploaded data. Default value is None. + :paramtype filename: str + :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OpenAIFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIFile] = kwargs.pop("cls", None) + + if body is _Unset: + if file is _Unset: + raise TypeError("missing required argument: file") + if purpose is _Unset: + raise TypeError("missing required argument: purpose") + body = {"file": file, "filename": filename, "purpose": purpose} + body = {k: v for k, v in body.items() if v is not None} + _body = body.as_dict() if isinstance(body, _model_base.Model) else body + _file_fields: List[str] = ["file"] + _data_fields: List[str] = ["purpose", "filename"] + _files, _data = prepare_multipart_form_data(_body, _file_fields, _data_fields) + + _request = build_machine_learning_services_upload_file_request( + files=_files, + data=_data, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def delete_file(self, file_id: str, **kwargs: Any) -> _models.FileDeletionStatus: + """Delete a previously uploaded file. + + :param file_id: The ID of the file to delete. Required. + :type file_id: str + :return: FileDeletionStatus. The FileDeletionStatus is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.FileDeletionStatus + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.FileDeletionStatus] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_delete_file_request( + file_id=file_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FileDeletionStatus, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_file(self, file_id: str, **kwargs: Any) -> _models.OpenAIFile: + """Returns information about a specific file. Does not retrieve file content. + + :param file_id: The ID of the file to retrieve. Required. + :type file_id: str + :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OpenAIFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIFile] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_file_request( + file_id=file_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_file_content(self, file_id: str, **kwargs: Any) -> bytes: + """Returns information about a specific file. Does not retrieve file content. + + :param file_id: The ID of the file to retrieve. Required. + :type file_id: str + :return: bytes + :rtype: bytes + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[bytes] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_file_content_request( + file_id=file_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(bytes, response.json(), format="base64") + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_online_endpoint(self, name: str, **kwargs: Any) -> _models.OnlineEndpoint: + """Get an Online Endpoint. + + :param name: Name of the endpoint. Required. + :type name: str + :return: OnlineEndpoint. The OnlineEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OnlineEndpoint] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_online_endpoint_request( + name=name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OnlineEndpoint, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def delete_online_endpoint( # pylint: disable=inconsistent-return-statements + self, name: str, **kwargs: Any + ) -> None: + """Delete an Online Endpoint. + + :param name: Name of the endpoint. Required. + :type name: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_delete_online_endpoint_request( + name=name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [204]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @overload + def update_online_endpoint( + self, + name: str, + body: _models.PartialMinimalTrackedResourceWithIdentity, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.OnlineEndpoint: + """Update an Online Endpoint. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint entity to apply during operation. Required. + :type body: ~azure.ai.resources.autogen.models.PartialMinimalTrackedResourceWithIdentity + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineEndpoint. The OnlineEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_online_endpoint( + self, name: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.OnlineEndpoint: + """Update an Online Endpoint. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint entity to apply during operation. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineEndpoint. The OnlineEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_online_endpoint( + self, name: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.OnlineEndpoint: + """Update an Online Endpoint. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint entity to apply during operation. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineEndpoint. The OnlineEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_online_endpoint( + self, name: str, body: Union[_models.PartialMinimalTrackedResourceWithIdentity, JSON, IO[bytes]], **kwargs: Any + ) -> _models.OnlineEndpoint: + """Update an Online Endpoint. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint entity to apply during operation. Is one of the following types: + PartialMinimalTrackedResourceWithIdentity, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.PartialMinimalTrackedResourceWithIdentity or + JSON or IO[bytes] + :return: OnlineEndpoint. The OnlineEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.OnlineEndpoint] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_update_online_endpoint_request( + name=name, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OnlineEndpoint, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def create_or_update_online_endpoint( + self, name: str, body: _models.OnlineEndpoint, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.OnlineEndpoint: + """Create or Update an Online Endpoint. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint. Required. + :type body: ~azure.ai.resources.autogen.models.OnlineEndpoint + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineEndpoint. The OnlineEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_or_update_online_endpoint( + self, name: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.OnlineEndpoint: + """Create or Update an Online Endpoint. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineEndpoint. The OnlineEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_or_update_online_endpoint( + self, name: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.OnlineEndpoint: + """Create or Update an Online Endpoint. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineEndpoint. The OnlineEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_or_update_online_endpoint( + self, name: str, body: Union[_models.OnlineEndpoint, JSON, IO[bytes]], **kwargs: Any + ) -> _models.OnlineEndpoint: + """Create or Update an Online Endpoint. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint. Is one of the following types: OnlineEndpoint, JSON, IO[bytes] + Required. + :type body: ~azure.ai.resources.autogen.models.OnlineEndpoint or JSON or IO[bytes] + :return: OnlineEndpoint. The OnlineEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.OnlineEndpoint] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_or_update_online_endpoint_request( + name=name, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OnlineEndpoint, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_online_endpoints( + self, + *, + name: Optional[str] = None, + count: Optional[int] = None, + compute_type: Optional[Union[str, _models.EndpointComputeType]] = None, + tags: Optional[str] = None, + properties: Optional[str] = None, + order_by: Optional[Union[str, _models.OrderString]] = None, + **kwargs: Any, + ) -> Iterable["_models.OnlineEndpoint"]: + """List Online Endpoints. + + :keyword name: Name of the endpoint. Default value is None. + :paramtype name: str + :keyword count: Number of endpoints to be retrieved in a page of results. Default value is + None. + :paramtype count: int + :keyword compute_type: EndpointComputeType to be filtered by. Known values are: "Managed", + "Kubernetes", and "AzureMLCompute". Default value is None. + :paramtype compute_type: str or ~azure.ai.resources.autogen.models.EndpointComputeType + :keyword tags: A set of tags with which to filter the returned models. It is a comma separated + string of tags key or tags key=value. Example: tagKey1,tagKey2,tagKey3=value3 . Default value + is None. + :paramtype tags: str + :keyword properties: A set of properties with which to filter the returned models. It is a + comma separated string of properties key and/or properties key=value Example: + propKey1,propKey2,propKey3=value3 . Default value is None. + :paramtype properties: str + :keyword order_by: The option to order the response. Known values are: "CreatedAtDesc", + "CreatedAtAsc", "UpdatedAtDesc", and "UpdatedAtAsc". Default value is None. + :paramtype order_by: str or ~azure.ai.resources.autogen.models.OrderString + :return: An iterator like instance of OnlineEndpoint + :rtype: ~azure.core.paging.ItemPaged[~azure.ai.resources.autogen.models.OnlineEndpoint] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.OnlineEndpoint]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_machine_learning_services_list_online_endpoints_request( + name=name, + count=count, + compute_type=compute_type, + tags=tags, + properties=properties, + order_by=order_by, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.OnlineEndpoint], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, iter(list_of_elem) + + def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return ItemPaged(get_next, extract_data) + + @overload + def list_keys_online_endpoint( + self, + name: str, + body: _models.RegenerateEndpointKeysRequest, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.EndpointAuthKeys: + """List EndpointAuthKeys for an Endpoint using Key-based authentication. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint. Required. + :type body: ~azure.ai.resources.autogen.models.RegenerateEndpointKeysRequest + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: EndpointAuthKeys. The EndpointAuthKeys is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.EndpointAuthKeys + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def list_keys_online_endpoint( + self, name: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.EndpointAuthKeys: + """List EndpointAuthKeys for an Endpoint using Key-based authentication. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: EndpointAuthKeys. The EndpointAuthKeys is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.EndpointAuthKeys + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def list_keys_online_endpoint( + self, name: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.EndpointAuthKeys: + """List EndpointAuthKeys for an Endpoint using Key-based authentication. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: EndpointAuthKeys. The EndpointAuthKeys is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.EndpointAuthKeys + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def list_keys_online_endpoint( + self, name: str, body: Union[_models.RegenerateEndpointKeysRequest, JSON, IO[bytes]], **kwargs: Any + ) -> _models.EndpointAuthKeys: + """List EndpointAuthKeys for an Endpoint using Key-based authentication. + + :param name: Name of the endpoint. Required. + :type name: str + :param body: Online Endpoint. Is one of the following types: RegenerateEndpointKeysRequest, + JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.RegenerateEndpointKeysRequest or JSON or + IO[bytes] + :return: EndpointAuthKeys. The EndpointAuthKeys is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.EndpointAuthKeys + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.EndpointAuthKeys] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_list_keys_online_endpoint_request( + name=name, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.EndpointAuthKeys, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def regenerate_keys_online_endpoint(self, name: str, **kwargs: Any) -> _models.EndpointAuthKeys: + """Regenerate EndpointAuthKeys for an Endpoint using Key-based authentication (asynchronous). + + :param name: Name of the endpoint. Required. + :type name: str + :return: EndpointAuthKeys. The EndpointAuthKeys is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.EndpointAuthKeys + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.EndpointAuthKeys] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_regenerate_keys_online_endpoint_request( + name=name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.EndpointAuthKeys, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_token_online_endpoint(self, name: str, **kwargs: Any) -> _models.EndpointAuthToken: + """Retrieve a valid AML token for an Endpoint using AMLToken-based authentication. + + :param name: Name of the endpoint. Required. + :type name: str + :return: EndpointAuthToken. The EndpointAuthToken is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.EndpointAuthToken + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.EndpointAuthToken] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_token_online_endpoint_request( + name=name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.EndpointAuthToken, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_online_deployments( + self, endpoint_name: str, *, _order_by: Optional[str] = None, _top: Optional[int] = None, **kwargs: Any + ) -> Iterable["_models.OnlineDeployment"]: + """List Online Deployments. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :keyword _order_by: Ordering of list. Default value is None. + :paramtype _order_by: str + :keyword _top: Top of list. Default value is None. + :paramtype _top: int + :return: An iterator like instance of OnlineDeployment + :rtype: ~azure.core.paging.ItemPaged[~azure.ai.resources.autogen.models.OnlineDeployment] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.OnlineDeployment]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_machine_learning_services_list_online_deployments_request( + endpoint_name=endpoint_name, + _order_by=_order_by, + _top=_top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.OnlineDeployment], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, iter(list_of_elem) + + def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return ItemPaged(get_next, extract_data) + + @distributed_trace + def delete_online_deployment( # pylint: disable=inconsistent-return-statements + self, endpoint_name: str, deployment_name: str, **kwargs: Any + ) -> None: + """Delete an Online Deployment. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_delete_online_deployment_request( + endpoint_name=endpoint_name, + deployment_name=deployment_name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [204]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get_online_deployment( + self, endpoint_name: str, deployment_name: str, **kwargs: Any + ) -> _models.OnlineDeployment: + """Gets an online inference deployment by id. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :return: OnlineDeployment. The OnlineDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OnlineDeployment] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_online_deployment_request( + endpoint_name=endpoint_name, + deployment_name=deployment_name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OnlineDeployment, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def update_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: _models.PartialMinimalTrackedResourceWithSku, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.OnlineDeployment: + """Update a Online Deployment. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: Online Endpoint entity to apply during operation. Required. + :type body: ~azure.ai.resources.autogen.models.PartialMinimalTrackedResourceWithSku + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineDeployment. The OnlineDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: JSON, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.OnlineDeployment: + """Update a Online Deployment. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: Online Endpoint entity to apply during operation. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineDeployment. The OnlineDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: IO[bytes], + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.OnlineDeployment: + """Update a Online Deployment. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: Online Endpoint entity to apply during operation. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineDeployment. The OnlineDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: Union[_models.PartialMinimalTrackedResourceWithSku, JSON, IO[bytes]], + **kwargs: Any, + ) -> _models.OnlineDeployment: + """Update a Online Deployment. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: Online Endpoint entity to apply during operation. Is one of the following types: + PartialMinimalTrackedResourceWithSku, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.PartialMinimalTrackedResourceWithSku or JSON or + IO[bytes] + :return: OnlineDeployment. The OnlineDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.OnlineDeployment] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_update_online_deployment_request( + endpoint_name=endpoint_name, + deployment_name=deployment_name, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OnlineDeployment, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def create_or_update_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: _models.OnlineDeployment, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.OnlineDeployment: + """Create or Update an Online Deployment. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: Inference Endpoint entity to apply during operation. Required. + :type body: ~azure.ai.resources.autogen.models.OnlineDeployment + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineDeployment. The OnlineDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_or_update_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: JSON, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.OnlineDeployment: + """Create or Update an Online Deployment. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: Inference Endpoint entity to apply during operation. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineDeployment. The OnlineDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_or_update_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: IO[bytes], + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.OnlineDeployment: + """Create or Update an Online Deployment. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: Inference Endpoint entity to apply during operation. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: OnlineDeployment. The OnlineDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_or_update_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: Union[_models.OnlineDeployment, JSON, IO[bytes]], + **kwargs: Any, + ) -> _models.OnlineDeployment: + """Create or Update an Online Deployment. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: Inference Endpoint entity to apply during operation. Is one of the following + types: OnlineDeployment, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.OnlineDeployment or JSON or IO[bytes] + :return: OnlineDeployment. The OnlineDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OnlineDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.OnlineDeployment] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_or_update_online_deployment_request( + endpoint_name=endpoint_name, + deployment_name=deployment_name, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OnlineDeployment, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def poll_logs_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: _models.DeploymentLogsRequest, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.DeploymentLogs: + """Polls an Endpoint operation. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: The request containing parameters for retrieving logs. Required. + :type body: ~azure.ai.resources.autogen.models.DeploymentLogsRequest + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: DeploymentLogs. The DeploymentLogs is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DeploymentLogs + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def poll_logs_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: JSON, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.DeploymentLogs: + """Polls an Endpoint operation. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: The request containing parameters for retrieving logs. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: DeploymentLogs. The DeploymentLogs is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DeploymentLogs + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def poll_logs_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: IO[bytes], + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.DeploymentLogs: + """Polls an Endpoint operation. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: The request containing parameters for retrieving logs. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: DeploymentLogs. The DeploymentLogs is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DeploymentLogs + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def poll_logs_online_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: Union[_models.DeploymentLogsRequest, JSON, IO[bytes]], + **kwargs: Any, + ) -> _models.DeploymentLogs: + """Polls an Endpoint operation. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :param body: The request containing parameters for retrieving logs. Is one of the following + types: DeploymentLogsRequest, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.DeploymentLogsRequest or JSON or IO[bytes] + :return: DeploymentLogs. The DeploymentLogs is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.DeploymentLogs + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.DeploymentLogs] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_poll_logs_online_deployment_request( + endpoint_name=endpoint_name, + deployment_name=deployment_name, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.DeploymentLogs, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_skus_online_deployment( + self, endpoint_name: str, deployment_name: str, *, count: int, **kwargs: Any + ) -> Iterable["_models.SkuResource"]: + """List Inference Endpoint Deployment Skus. + + :param endpoint_name: Inference endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference Endpoint Deployment name. Required. + :type deployment_name: str + :keyword count: Number of Skus to be retrieved in a page of results. Required. + :paramtype count: int + :return: An iterator like instance of SkuResource + :rtype: ~azure.core.paging.ItemPaged[~azure.ai.resources.autogen.models.SkuResource] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.SkuResource]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_machine_learning_services_get_skus_online_deployment_request( + endpoint_name=endpoint_name, + deployment_name=deployment_name, + count=count, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.SkuResource], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, iter(list_of_elem) + + def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return ItemPaged(get_next, extract_data) + + @distributed_trace + def get_batch_endpoint(self, endpoint_name: str, **kwargs: Any) -> _models.BatchEndpoint: + """Get a Batch Endpoint. + + :param endpoint_name: Name for the Batch Endpoint. Required. + :type endpoint_name: str + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.BatchEndpoint] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_batch_endpoint_request( + endpoint_name=endpoint_name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.BatchEndpoint, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def delete_batch_endpoint( # pylint: disable=inconsistent-return-statements + self, endpoint_name: str, **kwargs: Any + ) -> None: + """Delete an Batch Endpoint. + + :param endpoint_name: Inference Endpoint name. Required. + :type endpoint_name: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_delete_batch_endpoint_request( + endpoint_name=endpoint_name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [204]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @overload + def update_batch_endpoint( + self, + endpoint_name: str, + body: _models.PartialMinimalTrackedResourceWithIdentity, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.BatchEndpoint: + """Update a Batch Endpoint. + + :param endpoint_name: Name for the Batch inference endpoint. Required. + :type endpoint_name: str + :param body: Mutable batch inference endpoint definition object. Required. + :type body: ~azure.ai.resources.autogen.models.PartialMinimalTrackedResourceWithIdentity + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_batch_endpoint( + self, endpoint_name: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.BatchEndpoint: + """Update a Batch Endpoint. + + :param endpoint_name: Name for the Batch inference endpoint. Required. + :type endpoint_name: str + :param body: Mutable batch inference endpoint definition object. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_batch_endpoint( + self, endpoint_name: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.BatchEndpoint: + """Update a Batch Endpoint. + + :param endpoint_name: Name for the Batch inference endpoint. Required. + :type endpoint_name: str + :param body: Mutable batch inference endpoint definition object. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_batch_endpoint( + self, + endpoint_name: str, + body: Union[_models.PartialMinimalTrackedResourceWithIdentity, JSON, IO[bytes]], + **kwargs: Any, + ) -> _models.BatchEndpoint: + """Update a Batch Endpoint. + + :param endpoint_name: Name for the Batch inference endpoint. Required. + :type endpoint_name: str + :param body: Mutable batch inference endpoint definition object. Is one of the following types: + PartialMinimalTrackedResourceWithIdentity, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.PartialMinimalTrackedResourceWithIdentity or + JSON or IO[bytes] + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.BatchEndpoint] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_update_batch_endpoint_request( + endpoint_name=endpoint_name, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.BatchEndpoint, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def create_or_update_batch_endpoint( + self, endpoint_name: str, body: _models.BatchEndpoint, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.BatchEndpoint: + """Create or Update a Batch Endpoint. + + :param endpoint_name: Name for the Batch inference endpoint. Required. + :type endpoint_name: str + :param body: Batch inference endpoint definition object. Required. + :type body: ~azure.ai.resources.autogen.models.BatchEndpoint + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_or_update_batch_endpoint( + self, endpoint_name: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.BatchEndpoint: + """Create or Update a Batch Endpoint. + + :param endpoint_name: Name for the Batch inference endpoint. Required. + :type endpoint_name: str + :param body: Batch inference endpoint definition object. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_or_update_batch_endpoint( + self, endpoint_name: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.BatchEndpoint: + """Create or Update a Batch Endpoint. + + :param endpoint_name: Name for the Batch inference endpoint. Required. + :type endpoint_name: str + :param body: Batch inference endpoint definition object. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_or_update_batch_endpoint( + self, endpoint_name: str, body: Union[_models.BatchEndpoint, JSON, IO[bytes]], **kwargs: Any + ) -> _models.BatchEndpoint: + """Create or Update a Batch Endpoint. + + :param endpoint_name: Name for the Batch inference endpoint. Required. + :type endpoint_name: str + :param body: Batch inference endpoint definition object. Is one of the following types: + BatchEndpoint, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.BatchEndpoint or JSON or IO[bytes] + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.BatchEndpoint] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_or_update_batch_endpoint_request( + endpoint_name=endpoint_name, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.BatchEndpoint, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_batch_endpoints(self, *, count: Optional[int] = None, **kwargs: Any) -> Iterable["_models.BatchEndpoint"]: + """List Batch Endpoints. + + :keyword count: Number of endpoints to be retrieved in a page of results. Default value is + None. + :paramtype count: int + :return: An iterator like instance of BatchEndpoint + :rtype: ~azure.core.paging.ItemPaged[~azure.ai.resources.autogen.models.BatchEndpoint] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.BatchEndpoint]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_machine_learning_services_list_batch_endpoints_request( + count=count, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.BatchEndpoint], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, iter(list_of_elem) + + def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return ItemPaged(get_next, extract_data) + + @distributed_trace + def list_batch_deployments( + self, + endpoint_name: str, + *, + _order_by: Optional[str] = None, + _top: Optional[int] = None, + _skip: Optional[str] = None, + **kwargs: Any, + ) -> Iterable["_models.BatchDeployment"]: + """List Batch Deployments. + + :param endpoint_name: Endpoint name. Required. + :type endpoint_name: str + :keyword _order_by: Ordering of list. Default value is None. + :paramtype _order_by: str + :keyword _top: Top of list. Default value is None. + :paramtype _top: int + :keyword _skip: Continuation token for pagination. Default value is None. + :paramtype _skip: str + :return: An iterator like instance of BatchDeployment + :rtype: ~azure.core.paging.ItemPaged[~azure.ai.resources.autogen.models.BatchDeployment] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.BatchDeployment]] = kwargs.pop("cls", None) + + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + def prepare_request(next_link=None): + if not next_link: + + _request = build_machine_learning_services_list_batch_deployments_request( + endpoint_name=endpoint_name, + _order_by=_order_by, + _top=_top, + _skip=_skip, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + else: + # make call to next link with the client's api-version + _parsed_next_link = urllib.parse.urlparse(next_link) + _next_request_params = case_insensitive_dict( + { + key: [urllib.parse.quote(v) for v in value] + for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items() + } + ) + _next_request_params["api-version"] = self._config.api_version + _request = HttpRequest( + "GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params + ) + path_format_arguments = { + "endpoint": self._serialize.url( + "self._config.endpoint", self._config.endpoint, "str", skip_quote=True + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + return _request + + def extract_data(pipeline_response): + deserialized = pipeline_response.http_response.json() + list_of_elem = _deserialize(List[_models.BatchDeployment], deserialized["value"]) + if cls: + list_of_elem = cls(list_of_elem) # type: ignore + return deserialized.get("nextLink") or None, iter(list_of_elem) + + def get_next(next_link=None): + _request = prepare_request(next_link) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + response = pipeline_response.http_response + + if response.status_code not in [200]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + return pipeline_response + + return ItemPaged(get_next, extract_data) + + @distributed_trace + def delete_batch_deployment( # pylint: disable=inconsistent-return-statements + self, endpoint_name: str, deployment_name: str, **kwargs: Any + ) -> None: + """Delete an Batch Deployment. + + :param endpoint_name: Endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference deployment identifier. Required. + :type deployment_name: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_delete_batch_deployment_request( + endpoint_name=endpoint_name, + deployment_name=deployment_name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [204]: + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get_batch_deployment(self, endpoint_name: str, deployment_name: str, **kwargs: Any) -> _models.BatchDeployment: + """Gets a batch inference deployment by id. + + :param endpoint_name: Endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference deployment identifier. Required. + :type deployment_name: str + :return: BatchDeployment. The BatchDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.BatchDeployment] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_batch_deployment_request( + endpoint_name=endpoint_name, + deployment_name=deployment_name, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.BatchDeployment, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def update_batch_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: _models.PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.BatchDeployment: + """Update a Batch Deployment. + + :param endpoint_name: Endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference deployment identifier. Required. + :type deployment_name: str + :param body: Mutable batch inference endpoint definition object. Required. + :type body: + ~azure.ai.resources.autogen.models.PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchDeployment. The BatchDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_batch_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: JSON, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.BatchDeployment: + """Update a Batch Deployment. + + :param endpoint_name: Endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference deployment identifier. Required. + :type deployment_name: str + :param body: Mutable batch inference endpoint definition object. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchDeployment. The BatchDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_batch_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: IO[bytes], + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.BatchDeployment: + """Update a Batch Deployment. + + :param endpoint_name: Endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference deployment identifier. Required. + :type deployment_name: str + :param body: Mutable batch inference endpoint definition object. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchDeployment. The BatchDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_batch_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: Union[_models.PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties, JSON, IO[bytes]], + **kwargs: Any, + ) -> _models.BatchDeployment: + """Update a Batch Deployment. + + :param endpoint_name: Endpoint name. Required. + :type endpoint_name: str + :param deployment_name: Inference deployment identifier. Required. + :type deployment_name: str + :param body: Mutable batch inference endpoint definition object. Is one of the following types: + PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties, JSON, IO[bytes] Required. + :type body: + ~azure.ai.resources.autogen.models.PartialBatchDeploymentPartialMinimalTrackedResourceWithProperties + or JSON or IO[bytes] + :return: BatchDeployment. The BatchDeployment is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchDeployment + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.BatchDeployment] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_update_batch_deployment_request( + endpoint_name=endpoint_name, + deployment_name=deployment_name, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.BatchDeployment, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def create_or_update_batch_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: _models.BatchDeployment, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.BatchEndpoint: + """Create or Update a Batch Deployment. + + :param endpoint_name: Endpoint name. Required. + :type endpoint_name: str + :param deployment_name: The identifier for the Batch inference deployment. Required. + :type deployment_name: str + :param body: Batch inference deployment definition object. Required. + :type body: ~azure.ai.resources.autogen.models.BatchDeployment + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_or_update_batch_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: JSON, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.BatchEndpoint: + """Create or Update a Batch Deployment. + + :param endpoint_name: Endpoint name. Required. + :type endpoint_name: str + :param deployment_name: The identifier for the Batch inference deployment. Required. + :type deployment_name: str + :param body: Batch inference deployment definition object. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_or_update_batch_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: IO[bytes], + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.BatchEndpoint: + """Create or Update a Batch Deployment. + + :param endpoint_name: Endpoint name. Required. + :type endpoint_name: str + :param deployment_name: The identifier for the Batch inference deployment. Required. + :type deployment_name: str + :param body: Batch inference deployment definition object. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_or_update_batch_deployment( + self, + endpoint_name: str, + deployment_name: str, + body: Union[_models.BatchDeployment, JSON, IO[bytes]], + **kwargs: Any, + ) -> _models.BatchEndpoint: + """Create or Update a Batch Deployment. + + :param endpoint_name: Endpoint name. Required. + :type endpoint_name: str + :param deployment_name: The identifier for the Batch inference deployment. Required. + :type deployment_name: str + :param body: Batch inference deployment definition object. Is one of the following types: + BatchDeployment, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.BatchDeployment or JSON or IO[bytes] + :return: BatchEndpoint. The BatchEndpoint is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.BatchEndpoint + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.BatchEndpoint] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_or_update_batch_deployment_request( + endpoint_name=endpoint_name, + deployment_name=deployment_name, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.BatchEndpoint, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def create_message( + self, + thread_id: str, + body: _models.ThreadMessageOptions, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.ThreadMessage: + """Creates a new message on a specified thread. + + :param thread_id: The ID of the thread to create the new message on. Required. + :type thread_id: str + :param body: A single message within an assistant thread, as provided during that thread's + creation for its initial state. Required. + :type body: ~azure.ai.resources.autogen.models.ThreadMessageOptions + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_message( + self, thread_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadMessage: + """Creates a new message on a specified thread. + + :param thread_id: The ID of the thread to create the new message on. Required. + :type thread_id: str + :param body: A single message within an assistant thread, as provided during that thread's + creation for its initial state. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_message( + self, thread_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadMessage: + """Creates a new message on a specified thread. + + :param thread_id: The ID of the thread to create the new message on. Required. + :type thread_id: str + :param body: A single message within an assistant thread, as provided during that thread's + creation for its initial state. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_message( + self, thread_id: str, body: Union[_models.ThreadMessageOptions, JSON, IO[bytes]], **kwargs: Any + ) -> _models.ThreadMessage: + """Creates a new message on a specified thread. + + :param thread_id: The ID of the thread to create the new message on. Required. + :type thread_id: str + :param body: A single message within an assistant thread, as provided during that thread's + creation for its initial state. Is one of the following types: ThreadMessageOptions, JSON, + IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.ThreadMessageOptions or JSON or IO[bytes] + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadMessage] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_message_request( + thread_id=thread_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadMessage, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_messages( + self, + thread_id: str, + *, + run_id: Optional[str] = None, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any, + ) -> _models.OpenAIPageableListOfThreadMessage: + """Gets a list of messages that exist on a thread. + + :param thread_id: The ID of the thread to list messages from. Required. + :type thread_id: str + :keyword run_id: Filter messages by the run ID that generated them. Default value is None. + :paramtype run_id: str + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.resources.autogen.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfThreadMessage. The OpenAIPageableListOfThreadMessage is compatible + with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OpenAIPageableListOfThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfThreadMessage] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_list_messages_request( + thread_id=thread_id, + run_id=run_id, + limit=limit, + order=order, + after=after, + before=before, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfThreadMessage, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_message(self, thread_id: str, message_id: str, **kwargs: Any) -> _models.ThreadMessage: + """Gets an existing message from an existing thread. + + :param thread_id: The ID of the thread to retrieve the specified message from. Required. + :type thread_id: str + :param message_id: The ID of the message to retrieve from the specified thread. Required. + :type message_id: str + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ThreadMessage] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_message_request( + thread_id=thread_id, + message_id=message_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadMessage, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def update_message( + self, thread_id: str, message_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadMessage: + """Modifies an existing message on an existing thread. + + :param thread_id: The ID of the thread containing the specified message to modify. Required. + :type thread_id: str + :param message_id: The ID of the message to modify on the specified thread. Required. + :type message_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_message( + self, + thread_id: str, + message_id: str, + *, + content_type: str = "application/json", + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any, + ) -> _models.ThreadMessage: + """Modifies an existing message on an existing thread. + + :param thread_id: The ID of the thread containing the specified message to modify. Required. + :type thread_id: str + :param message_id: The ID of the message to modify on the specified thread. Required. + :type message_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_message( + self, thread_id: str, message_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadMessage: + """Modifies an existing message on an existing thread. + + :param thread_id: The ID of the thread containing the specified message to modify. Required. + :type thread_id: str + :param message_id: The ID of the message to modify on the specified thread. Required. + :type message_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_message( + self, + thread_id: str, + message_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any, + ) -> _models.ThreadMessage: + """Modifies an existing message on an existing thread. + + :param thread_id: The ID of the thread containing the specified message to modify. Required. + :type thread_id: str + :param message_id: The ID of the message to modify on the specified thread. Required. + :type message_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadMessage + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadMessage] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"metadata": metadata} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_update_message_request( + thread_id=thread_id, + message_id=message_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadMessage, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_run_step(self, thread_id: str, run_id: str, step_id: str, **kwargs: Any) -> _models.RunStep: + """Gets a single run step from a thread run. + + :param thread_id: The ID of the thread that was run. Required. + :type thread_id: str + :param run_id: The ID of the specific run to retrieve the step from. Required. + :type run_id: str + :param step_id: The ID of the step to retrieve information about. Required. + :type step_id: str + :return: RunStep. The RunStep is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.RunStep + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.RunStep] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_run_step_request( + thread_id=thread_id, + run_id=run_id, + step_id=step_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.RunStep, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_run_steps( + self, + thread_id: str, + run_id: str, + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any, + ) -> _models.OpenAIPageableListOfRunStep: + """Gets a list of run steps from a thread run. + + :param thread_id: The ID of the thread that was run. Required. + :type thread_id: str + :param run_id: The ID of the run to list steps from. Required. + :type run_id: str + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.resources.autogen.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfRunStep. The OpenAIPageableListOfRunStep is compatible with + MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OpenAIPageableListOfRunStep + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfRunStep] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_list_run_steps_request( + thread_id=thread_id, + run_id=run_id, + limit=limit, + order=order, + after=after, + before=before, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfRunStep, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def create_run( + self, thread_id: str, body: _models.CreateRunOptions, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new run for an assistant thread. + + :param thread_id: The ID of the thread to run. Required. + :type thread_id: str + :param body: The details used when creating a new run of an assistant thread. Required. + :type body: ~azure.ai.resources.autogen.models.CreateRunOptions + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_run( + self, thread_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new run for an assistant thread. + + :param thread_id: The ID of the thread to run. Required. + :type thread_id: str + :param body: The details used when creating a new run of an assistant thread. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_run( + self, thread_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new run for an assistant thread. + + :param thread_id: The ID of the thread to run. Required. + :type thread_id: str + :param body: The details used when creating a new run of an assistant thread. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_run( + self, thread_id: str, body: Union[_models.CreateRunOptions, JSON, IO[bytes]], **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new run for an assistant thread. + + :param thread_id: The ID of the thread to run. Required. + :type thread_id: str + :param body: The details used when creating a new run of an assistant thread. Is one of the + following types: CreateRunOptions, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.CreateRunOptions or JSON or IO[bytes] + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_run_request( + thread_id=thread_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_runs( + self, + thread_id: str, + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any, + ) -> _models.OpenAIPageableListOfThreadRun: + """Gets a list of runs for a specified thread. + + :param thread_id: The ID of the thread to list runs from. Required. + :type thread_id: str + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.resources.autogen.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfThreadRun. The OpenAIPageableListOfThreadRun is compatible with + MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OpenAIPageableListOfThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfThreadRun] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_list_runs_request( + thread_id=thread_id, + limit=limit, + order=order, + after=after, + before=before, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_run(self, thread_id: str, run_id: str, **kwargs: Any) -> _models.ThreadRun: + """Gets an existing run from an existing thread. + + :param thread_id: The ID of the thread to retrieve run information from. Required. + :type thread_id: str + :param run_id: The ID of the thread to retrieve information about. Required. + :type run_id: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_run_request( + thread_id=thread_id, + run_id=run_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def update_run( + self, thread_id: str, run_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Modifies an existing thread run. + + :param thread_id: The ID of the thread associated with the specified run. Required. + :type thread_id: str + :param run_id: The ID of the run to modify. Required. + :type run_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_run( + self, + thread_id: str, + run_id: str, + *, + content_type: str = "application/json", + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any, + ) -> _models.ThreadRun: + """Modifies an existing thread run. + + :param thread_id: The ID of the thread associated with the specified run. Required. + :type thread_id: str + :param run_id: The ID of the run to modify. Required. + :type run_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_run( + self, thread_id: str, run_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Modifies an existing thread run. + + :param thread_id: The ID of the thread associated with the specified run. Required. + :type thread_id: str + :param run_id: The ID of the run to modify. Required. + :type run_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_run( + self, + thread_id: str, + run_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + metadata: Optional[Dict[str, str]] = None, + **kwargs: Any, + ) -> _models.ThreadRun: + """Modifies an existing thread run. + + :param thread_id: The ID of the thread associated with the specified run. Required. + :type thread_id: str + :param run_id: The ID of the run to modify. Required. + :type run_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword metadata: A set of up to 16 key/value pairs that can be attached to an object, used + for storing additional information about that object in a structured format. Keys may be up to + 64 characters in length and values may be up to 512 characters in length. Default value is + None. + :paramtype metadata: dict[str, str] + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"metadata": metadata} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_update_run_request( + thread_id=thread_id, + run_id=run_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def submit_tool_outputs_to_run( + self, thread_id: str, run_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Submits outputs from tools as requested by tool calls in a run. Runs that need submitted tool + outputs will have a status of 'requires_action' with a required_action.type of + 'submit_tool_outputs'. + + :param thread_id: The ID of the thread that was run. Required. + :type thread_id: str + :param run_id: The ID of the run that requires tool outputs. Required. + :type run_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def submit_tool_outputs_to_run( + self, + thread_id: str, + run_id: str, + *, + tool_outputs: List[_models.ToolOutput], + content_type: str = "application/json", + stream_parameter: Optional[bool] = None, + **kwargs: Any, + ) -> _models.ThreadRun: + """Submits outputs from tools as requested by tool calls in a run. Runs that need submitted tool + outputs will have a status of 'requires_action' with a required_action.type of + 'submit_tool_outputs'. + + :param thread_id: The ID of the thread that was run. Required. + :type thread_id: str + :param run_id: The ID of the run that requires tool outputs. Required. + :type run_id: str + :keyword tool_outputs: A list of tools for which the outputs are being submitted. Required. + :paramtype tool_outputs: list[~azure.ai.resources.autogen.models.ToolOutput] + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword stream_parameter: If ``true``\\ , returns a stream of events that happen during the + Run as server-sent events, terminating when the Run enters a terminal state with a ``data: + [DONE]`` message. Default value is None. + :paramtype stream_parameter: bool + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def submit_tool_outputs_to_run( + self, thread_id: str, run_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Submits outputs from tools as requested by tool calls in a run. Runs that need submitted tool + outputs will have a status of 'requires_action' with a required_action.type of + 'submit_tool_outputs'. + + :param thread_id: The ID of the thread that was run. Required. + :type thread_id: str + :param run_id: The ID of the run that requires tool outputs. Required. + :type run_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def submit_tool_outputs_to_run( + self, + thread_id: str, + run_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + tool_outputs: List[_models.ToolOutput] = _Unset, + stream_parameter: Optional[bool] = None, + **kwargs: Any, + ) -> _models.ThreadRun: + """Submits outputs from tools as requested by tool calls in a run. Runs that need submitted tool + outputs will have a status of 'requires_action' with a required_action.type of + 'submit_tool_outputs'. + + :param thread_id: The ID of the thread that was run. Required. + :type thread_id: str + :param run_id: The ID of the run that requires tool outputs. Required. + :type run_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword tool_outputs: A list of tools for which the outputs are being submitted. Required. + :paramtype tool_outputs: list[~azure.ai.resources.autogen.models.ToolOutput] + :keyword stream_parameter: If ``true``\\ , returns a stream of events that happen during the + Run as server-sent events, terminating when the Run enters a terminal state with a ``data: + [DONE]`` message. Default value is None. + :paramtype stream_parameter: bool + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + if body is _Unset: + if tool_outputs is _Unset: + raise TypeError("missing required argument: tool_outputs") + body = {"stream": stream_parameter, "tool_outputs": tool_outputs} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_submit_tool_outputs_to_run_request( + thread_id=thread_id, + run_id=run_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def cancel_run(self, thread_id: str, run_id: str, **kwargs: Any) -> _models.ThreadRun: + """Cancels a run of an in progress thread. + + :param thread_id: The ID of the thread being run. Required. + :type thread_id: str + :param run_id: The ID of the run to cancel. Required. + :type run_id: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_cancel_run_request( + thread_id=thread_id, + run_id=run_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def create_thread_and_run( + self, body: _models.CreateAndRunThreadOptions, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new assistant thread and immediately starts a run using that new thread. + + :param body: The details used when creating and immediately running a new assistant thread. + Required. + :type body: ~azure.ai.resources.autogen.models.CreateAndRunThreadOptions + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_thread_and_run( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new assistant thread and immediately starts a run using that new thread. + + :param body: The details used when creating and immediately running a new assistant thread. + Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_thread_and_run( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new assistant thread and immediately starts a run using that new thread. + + :param body: The details used when creating and immediately running a new assistant thread. + Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_thread_and_run( + self, body: Union[_models.CreateAndRunThreadOptions, JSON, IO[bytes]], **kwargs: Any + ) -> _models.ThreadRun: + """Creates a new assistant thread and immediately starts a run using that new thread. + + :param body: The details used when creating and immediately running a new assistant thread. Is + one of the following types: CreateAndRunThreadOptions, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.CreateAndRunThreadOptions or JSON or IO[bytes] + :return: ThreadRun. The ThreadRun is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadRun + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.ThreadRun] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_thread_and_run_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadRun, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def create_thread( + self, body: _models.AssistantThreadCreationOptions, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.AssistantThread: + """Creates a new thread. Threads contain messages and can be run by assistants. + + :param body: The details used to create a new assistant thread. Required. + :type body: ~azure.ai.resources.autogen.models.AssistantThreadCreationOptions + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_thread( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.AssistantThread: + """Creates a new thread. Threads contain messages and can be run by assistants. + + :param body: The details used to create a new assistant thread. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_thread( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.AssistantThread: + """Creates a new thread. Threads contain messages and can be run by assistants. + + :param body: The details used to create a new assistant thread. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_thread( + self, body: Union[_models.AssistantThreadCreationOptions, JSON, IO[bytes]], **kwargs: Any + ) -> _models.AssistantThread: + """Creates a new thread. Threads contain messages and can be run by assistants. + + :param body: The details used to create a new assistant thread. Is one of the following types: + AssistantThreadCreationOptions, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.AssistantThreadCreationOptions or JSON or + IO[bytes] + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.AssistantThread] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_thread_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AssistantThread, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_thread(self, thread_id: str, **kwargs: Any) -> _models.AssistantThread: + """Gets information about an existing thread. + + :param thread_id: The ID of the thread to retrieve information about. Required. + :type thread_id: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.AssistantThread] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_thread_request( + thread_id=thread_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AssistantThread, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def update_thread( + self, + thread_id: str, + body: _models.UpdateAssistantThreadOptions, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.AssistantThread: + """Modifies an existing thread. + + :param thread_id: The ID of the thread to modify. Required. + :type thread_id: str + :param body: The details used to update an existing assistant thread. Required. + :type body: ~azure.ai.resources.autogen.models.UpdateAssistantThreadOptions + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_thread( + self, thread_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.AssistantThread: + """Modifies an existing thread. + + :param thread_id: The ID of the thread to modify. Required. + :type thread_id: str + :param body: The details used to update an existing assistant thread. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_thread( + self, thread_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.AssistantThread: + """Modifies an existing thread. + + :param thread_id: The ID of the thread to modify. Required. + :type thread_id: str + :param body: The details used to update an existing assistant thread. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_thread( + self, thread_id: str, body: Union[_models.UpdateAssistantThreadOptions, JSON, IO[bytes]], **kwargs: Any + ) -> _models.AssistantThread: + """Modifies an existing thread. + + :param thread_id: The ID of the thread to modify. Required. + :type thread_id: str + :param body: The details used to update an existing assistant thread. Is one of the following + types: UpdateAssistantThreadOptions, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.UpdateAssistantThreadOptions or JSON or + IO[bytes] + :return: AssistantThread. The AssistantThread is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.AssistantThread + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.AssistantThread] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_update_thread_request( + thread_id=thread_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AssistantThread, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def delete_thread(self, thread_id: str, **kwargs: Any) -> _models.ThreadDeletionStatus: + """Deletes an existing thread. + + :param thread_id: The ID of the thread to delete. Required. + :type thread_id: str + :return: ThreadDeletionStatus. The ThreadDeletionStatus is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.ThreadDeletionStatus + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ThreadDeletionStatus] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_delete_thread_request( + thread_id=thread_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ThreadDeletionStatus, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_vector_stores( + self, + *, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any, + ) -> _models.OpenAIPageableListOfVectorStore: + """Returns a list of vector stores. + + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.resources.autogen.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfVectorStore. The OpenAIPageableListOfVectorStore is compatible + with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OpenAIPageableListOfVectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfVectorStore] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_list_vector_stores_request( + limit=limit, + order=order, + after=after, + before=before, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfVectorStore, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def create_vector_store( + self, body: _models.VectorStoreOptions, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStore: + """Creates a vector store. + + :param body: Request object for creating a vector store. Required. + :type body: ~azure.ai.resources.autogen.models.VectorStoreOptions + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_vector_store( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStore: + """Creates a vector store. + + :param body: Request object for creating a vector store. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_vector_store( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStore: + """Creates a vector store. + + :param body: Request object for creating a vector store. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_vector_store( + self, body: Union[_models.VectorStoreOptions, JSON, IO[bytes]], **kwargs: Any + ) -> _models.VectorStore: + """Creates a vector store. + + :param body: Request object for creating a vector store. Is one of the following types: + VectorStoreOptions, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.VectorStoreOptions or JSON or IO[bytes] + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.VectorStore] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_vector_store_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStore, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_vector_store(self, vector_store_id: str, **kwargs: Any) -> _models.VectorStore: + """Returns the vector store object matching the specified ID. + + :param vector_store_id: The ID of the vector store to retrieve. Required. + :type vector_store_id: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStore] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_vector_store_request( + vector_store_id=vector_store_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStore, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def modify_vector_store( + self, + vector_store_id: str, + body: _models.VectorStoreUpdateOptions, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.VectorStore: + """The ID of the vector store to modify. + + :param vector_store_id: The ID of the vector store to modify. Required. + :type vector_store_id: str + :param body: Request object for updating a vector store. Required. + :type body: ~azure.ai.resources.autogen.models.VectorStoreUpdateOptions + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def modify_vector_store( + self, vector_store_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStore: + """The ID of the vector store to modify. + + :param vector_store_id: The ID of the vector store to modify. Required. + :type vector_store_id: str + :param body: Request object for updating a vector store. Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def modify_vector_store( + self, vector_store_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStore: + """The ID of the vector store to modify. + + :param vector_store_id: The ID of the vector store to modify. Required. + :type vector_store_id: str + :param body: Request object for updating a vector store. Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def modify_vector_store( + self, vector_store_id: str, body: Union[_models.VectorStoreUpdateOptions, JSON, IO[bytes]], **kwargs: Any + ) -> _models.VectorStore: + """The ID of the vector store to modify. + + :param vector_store_id: The ID of the vector store to modify. Required. + :type vector_store_id: str + :param body: Request object for updating a vector store. Is one of the following types: + VectorStoreUpdateOptions, JSON, IO[bytes] Required. + :type body: ~azure.ai.resources.autogen.models.VectorStoreUpdateOptions or JSON or IO[bytes] + :return: VectorStore. The VectorStore is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStore + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.VectorStore] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_modify_vector_store_request( + vector_store_id=vector_store_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStore, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def delete_vector_store(self, vector_store_id: str, **kwargs: Any) -> _models.VectorStoreDeletionStatus: + """Deletes the vector store object matching the specified ID. + + :param vector_store_id: The ID of the vector store to delete. Required. + :type vector_store_id: str + :return: VectorStoreDeletionStatus. The VectorStoreDeletionStatus is compatible with + MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreDeletionStatus + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStoreDeletionStatus] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_delete_vector_store_request( + vector_store_id=vector_store_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreDeletionStatus, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_vector_store_files( + self, + vector_store_id: str, + *, + filter: Optional[Union[str, _models.VectorStoreFileStatusFilter]] = None, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any, + ) -> _models.OpenAIPageableListOfVectorStoreFile: + """Returns a list of vector store files. + + :param vector_store_id: The ID of the vector store that the files belong to. Required. + :type vector_store_id: str + :keyword filter: Filter by file status. Known values are: "in_progress", "completed", "failed", + and "cancelled". Default value is None. + :paramtype filter: str or ~azure.ai.resources.autogen.models.VectorStoreFileStatusFilter + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.resources.autogen.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfVectorStoreFile. The OpenAIPageableListOfVectorStoreFile is + compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OpenAIPageableListOfVectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfVectorStoreFile] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_list_vector_store_files_request( + vector_store_id=vector_store_id, + filter=filter, + limit=limit, + order=order, + after=after, + before=before, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfVectorStoreFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def create_vector_store_file( + self, vector_store_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStoreFile: + """Create a vector store file by attaching a file to a vector store. + + :param vector_store_id: The ID of the vector store for which to create a File. Required. + :type vector_store_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_vector_store_file( + self, + vector_store_id: str, + *, + file_id: str, + content_type: str = "application/json", + chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, + **kwargs: Any, + ) -> _models.VectorStoreFile: + """Create a vector store file by attaching a file to a vector store. + + :param vector_store_id: The ID of the vector store for which to create a File. Required. + :type vector_store_id: str + :keyword file_id: A File ID that the vector store should use. Useful for tools like + ``file_search`` that can access files. Required. + :paramtype file_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will + use the auto strategy. Default value is None. + :paramtype chunking_strategy: + ~azure.ai.resources.autogen.models.VectorStoreChunkingStrategyRequest + :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_vector_store_file( + self, vector_store_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStoreFile: + """Create a vector store file by attaching a file to a vector store. + + :param vector_store_id: The ID of the vector store for which to create a File. Required. + :type vector_store_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_vector_store_file( + self, + vector_store_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + file_id: str = _Unset, + chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, + **kwargs: Any, + ) -> _models.VectorStoreFile: + """Create a vector store file by attaching a file to a vector store. + + :param vector_store_id: The ID of the vector store for which to create a File. Required. + :type vector_store_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword file_id: A File ID that the vector store should use. Useful for tools like + ``file_search`` that can access files. Required. + :paramtype file_id: str + :keyword chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will + use the auto strategy. Default value is None. + :paramtype chunking_strategy: + ~azure.ai.resources.autogen.models.VectorStoreChunkingStrategyRequest + :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.VectorStoreFile] = kwargs.pop("cls", None) + + if body is _Unset: + if file_id is _Unset: + raise TypeError("missing required argument: file_id") + body = {"chunking_strategy": chunking_strategy, "file_id": file_id} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_vector_store_file_request( + vector_store_id=vector_store_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_vector_store_file(self, vector_store_id: str, file_id: str, **kwargs: Any) -> _models.VectorStoreFile: + """Retrieves a vector store file. + + :param vector_store_id: The ID of the vector store that the file belongs to. Required. + :type vector_store_id: str + :param file_id: The ID of the file being retrieved. Required. + :type file_id: str + :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStoreFile] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_vector_store_file_request( + vector_store_id=vector_store_id, + file_id=file_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def delete_vector_store_file( + self, vector_store_id: str, file_id: str, **kwargs: Any + ) -> _models.VectorStoreFileDeletionStatus: + """Delete a vector store file. This will remove the file from the vector store but the file itself + will not be deleted. To delete the file, use the delete file endpoint. + + :param vector_store_id: The ID of the vector store that the file belongs to. Required. + :type vector_store_id: str + :param file_id: The ID of the file to delete its relationship to the vector store. Required. + :type file_id: str + :return: VectorStoreFileDeletionStatus. The VectorStoreFileDeletionStatus is compatible with + MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFileDeletionStatus + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStoreFileDeletionStatus] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_delete_vector_store_file_request( + vector_store_id=vector_store_id, + file_id=file_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFileDeletionStatus, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def create_vector_store_file_batch( + self, vector_store_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStoreFileBatch: + """Create a vector store file batch. + + :param vector_store_id: The ID of the vector store for which to create a File Batch. Required. + :type vector_store_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_vector_store_file_batch( + self, + vector_store_id: str, + *, + file_ids: List[str], + content_type: str = "application/json", + chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, + **kwargs: Any, + ) -> _models.VectorStoreFileBatch: + """Create a vector store file batch. + + :param vector_store_id: The ID of the vector store for which to create a File Batch. Required. + :type vector_store_id: str + :keyword file_ids: A list of File IDs that the vector store should use. Useful for tools like + ``file_search`` that can access files. Required. + :paramtype file_ids: list[str] + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will + use the auto strategy. Default value is None. + :paramtype chunking_strategy: + ~azure.ai.resources.autogen.models.VectorStoreChunkingStrategyRequest + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_vector_store_file_batch( + self, vector_store_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.VectorStoreFileBatch: + """Create a vector store file batch. + + :param vector_store_id: The ID of the vector store for which to create a File Batch. Required. + :type vector_store_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_vector_store_file_batch( + self, + vector_store_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + file_ids: List[str] = _Unset, + chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, + **kwargs: Any, + ) -> _models.VectorStoreFileBatch: + """Create a vector store file batch. + + :param vector_store_id: The ID of the vector store for which to create a File Batch. Required. + :type vector_store_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword file_ids: A list of File IDs that the vector store should use. Useful for tools like + ``file_search`` that can access files. Required. + :paramtype file_ids: list[str] + :keyword chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will + use the auto strategy. Default value is None. + :paramtype chunking_strategy: + ~azure.ai.resources.autogen.models.VectorStoreChunkingStrategyRequest + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.VectorStoreFileBatch] = kwargs.pop("cls", None) + + if body is _Unset: + if file_ids is _Unset: + raise TypeError("missing required argument: file_ids") + body = {"chunking_strategy": chunking_strategy, "file_ids": file_ids} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_machine_learning_services_create_vector_store_file_batch_request( + vector_store_id=vector_store_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFileBatch, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_vector_store_file_batch( + self, vector_store_id: str, batch_id: str, **kwargs: Any + ) -> _models.VectorStoreFileBatch: + """Retrieve a vector store file batch. + + :param vector_store_id: The ID of the vector store that the file batch belongs to. Required. + :type vector_store_id: str + :param batch_id: The ID of the file batch being retrieved. Required. + :type batch_id: str + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStoreFileBatch] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_get_vector_store_file_batch_request( + vector_store_id=vector_store_id, + batch_id=batch_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFileBatch, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def cancel_vector_store_file_batch( + self, vector_store_id: str, batch_id: str, **kwargs: Any + ) -> _models.VectorStoreFileBatch: + """Cancel a vector store file batch. This attempts to cancel the processing of files in this batch + as soon as possible. + + :param vector_store_id: The ID of the vector store that the file batch belongs to. Required. + :type vector_store_id: str + :param batch_id: The ID of the file batch to cancel. Required. + :type batch_id: str + :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.VectorStoreFileBatch + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.VectorStoreFileBatch] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_cancel_vector_store_file_batch_request( + vector_store_id=vector_store_id, + batch_id=batch_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.VectorStoreFileBatch, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def list_vector_store_file_batch_files( + self, + vector_store_id: str, + batch_id: str, + *, + filter: Optional[Union[str, _models.VectorStoreFileStatusFilter]] = None, + limit: Optional[int] = None, + order: Optional[Union[str, _models.ListSortOrder]] = None, + after: Optional[str] = None, + before: Optional[str] = None, + **kwargs: Any, + ) -> _models.OpenAIPageableListOfVectorStoreFile: + """Returns a list of vector store files in a batch. + + :param vector_store_id: The ID of the vector store that the file batch belongs to. Required. + :type vector_store_id: str + :param batch_id: The ID of the file batch that the files belong to. Required. + :type batch_id: str + :keyword filter: Filter by file status. Known values are: "in_progress", "completed", "failed", + and "cancelled". Default value is None. + :paramtype filter: str or ~azure.ai.resources.autogen.models.VectorStoreFileStatusFilter + :keyword limit: A limit on the number of objects to be returned. Limit can range between 1 and + 100, and the default is 20. Default value is None. + :paramtype limit: int + :keyword order: Sort order by the created_at timestamp of the objects. asc for ascending order + and desc for descending order. Known values are: "asc" and "desc". Default value is None. + :paramtype order: str or ~azure.ai.resources.autogen.models.ListSortOrder + :keyword after: A cursor for use in pagination. after is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the + list. Default value is None. + :paramtype after: str + :keyword before: A cursor for use in pagination. before is an object ID that defines your place + in the list. For instance, if you make a list request and receive 100 objects, ending with + obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of + the list. Default value is None. + :paramtype before: str + :return: OpenAIPageableListOfVectorStoreFile. The OpenAIPageableListOfVectorStoreFile is + compatible with MutableMapping + :rtype: ~azure.ai.resources.autogen.models.OpenAIPageableListOfVectorStoreFile + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.OpenAIPageableListOfVectorStoreFile] = kwargs.pop("cls", None) + + _request = build_machine_learning_services_list_vector_store_file_batch_files_request( + vector_store_id=vector_store_id, + batch_id=batch_id, + filter=filter, + limit=limit, + order=order, + after=after, + before=before, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + raise HttpResponseError(response=response) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.OpenAIPageableListOfVectorStoreFile, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/operations/_patch.py b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/operations/_patch.py new file mode 100644 index 000000000000..f7dd32510333 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/operations/_patch.py @@ -0,0 +1,20 @@ +# ------------------------------------ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. +# ------------------------------------ +"""Customize generated code here. + +Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize +""" +from typing import List + +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level + + +def patch_sdk(): + """Do not remove from this file. + + `patch_sdk` is a last resort escape hatch that allows you to do customizations + you can't accomplish using the techniques described in + https://aka.ms/azsdk/python/dpcodegen/python/customize + """ diff --git a/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/py.typed b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/py.typed new file mode 100644 index 000000000000..e5aff4f83af8 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/azure/ai/resources/autogen/py.typed @@ -0,0 +1 @@ +# Marker file for PEP 561. \ No newline at end of file diff --git a/sdk/ai/azure-ai-resources-autogen/dev_requirements.txt b/sdk/ai/azure-ai-resources-autogen/dev_requirements.txt new file mode 100644 index 000000000000..c82827bb56f4 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/dev_requirements.txt @@ -0,0 +1,4 @@ +-e ../../../tools/azure-sdk-tools +../../core/azure-core +../../identity/azure-identity +aiohttp \ No newline at end of file diff --git a/sdk/ai/azure-ai-resources-autogen/generated_tests/conftest.py b/sdk/ai/azure-ai-resources-autogen/generated_tests/conftest.py new file mode 100644 index 000000000000..4e3c324a8369 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/generated_tests/conftest.py @@ -0,0 +1,47 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import os +import pytest +from dotenv import load_dotenv +from devtools_testutils import ( + test_proxy, + add_general_regex_sanitizer, + add_body_key_sanitizer, + add_header_regex_sanitizer, +) + +load_dotenv() + + +# For security, please avoid record sensitive identity information in recordings +@pytest.fixture(scope="session", autouse=True) +def add_sanitizers(test_proxy): + machinelearningservices_subscription_id = os.environ.get( + "MACHINELEARNINGSERVICES_SUBSCRIPTION_ID", "00000000-0000-0000-0000-000000000000" + ) + machinelearningservices_tenant_id = os.environ.get( + "MACHINELEARNINGSERVICES_TENANT_ID", "00000000-0000-0000-0000-000000000000" + ) + machinelearningservices_client_id = os.environ.get( + "MACHINELEARNINGSERVICES_CLIENT_ID", "00000000-0000-0000-0000-000000000000" + ) + machinelearningservices_client_secret = os.environ.get( + "MACHINELEARNINGSERVICES_CLIENT_SECRET", "00000000-0000-0000-0000-000000000000" + ) + add_general_regex_sanitizer( + regex=machinelearningservices_subscription_id, value="00000000-0000-0000-0000-000000000000" + ) + add_general_regex_sanitizer(regex=machinelearningservices_tenant_id, value="00000000-0000-0000-0000-000000000000") + add_general_regex_sanitizer(regex=machinelearningservices_client_id, value="00000000-0000-0000-0000-000000000000") + add_general_regex_sanitizer( + regex=machinelearningservices_client_secret, value="00000000-0000-0000-0000-000000000000" + ) + + add_header_regex_sanitizer(key="Set-Cookie", value="[set-cookie;]") + add_header_regex_sanitizer(key="Cookie", value="cookie;") + add_body_key_sanitizer(json_path="$..access_token", value="access_token") diff --git a/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services.py b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services.py new file mode 100644 index 000000000000..23130b512d5b --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services.py @@ -0,0 +1,965 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import pytest +from devtools_testutils import recorded_by_proxy +from testpreparer import MachineLearningServicesClientTestBase, MachineLearningServicesPreparer + + +@pytest.mark.skip("you may need to update the auto-generated test case before run it") +class TestMachineLearningServices(MachineLearningServicesClientTestBase): + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_create_assistant(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.create_assistant( + body={ + "model": "str", + "description": "str", + "instructions": "str", + "metadata": {"str": "str"}, + "name": "str", + "response_format": "str", + "temperature": 0.0, + "tool_resources": {"code_interpreter": {"file_ids": ["str"]}, "file_search": ["str"]}, + "tools": ["tool_definition"], + "top_p": 0.0, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_list_assistants(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.list_assistants() + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_get_assistant(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.get_assistant( + assistant_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_update_assistant(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.update_assistant( + assistant_id="str", + body={ + "description": "str", + "instructions": "str", + "metadata": {"str": "str"}, + "model": "str", + "name": "str", + "response_format": "str", + "temperature": 0.0, + "tool_resources": { + "code_interpreter": {"file_ids": ["str"]}, + "file_search": {"vector_store_ids": ["str"]}, + }, + "tools": ["tool_definition"], + "top_p": 0.0, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_delete_assistant(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.delete_assistant( + assistant_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_list_files(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.list_files() + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_upload_file(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.upload_file( + body={"file": "filetype", "purpose": "str", "filename": "str"}, + file="filetype", + purpose="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_delete_file(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.delete_file( + file_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_get_file(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.get_file( + file_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_get_file_content(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.get_file_content( + file_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_get_online_endpoint(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.get_online_endpoint( + name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_delete_online_endpoint(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.delete_online_endpoint( + name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_update_online_endpoint(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.update_online_endpoint( + name="str", + body={"identity": {"type": "str", "userAssignedIdentities": {"str": {"str": {}}}}, "tags": {"str": "str"}}, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_create_or_update_online_endpoint(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.create_or_update_online_endpoint( + name="str", + body={ + "authMode": "str", + "compute": "str", + "description": "str", + "keys": {"primaryKey": "str", "secondaryKey": "str"}, + "mirrorTraffic": {"str": 0}, + "properties": {"str": "str"}, + "provisioningState": "str", + "publicNetworkAccess": "str", + "scoringUri": "str", + "swaggerUri": "str", + "traffic": {"str": 0}, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_list_online_endpoints(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.list_online_endpoints() + result = [r for r in response] + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_list_keys_online_endpoint(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.list_keys_online_endpoint( + name="str", + body={"keyType": "str", "keyValue": "str"}, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_regenerate_keys_online_endpoint(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.regenerate_keys_online_endpoint( + name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_get_token_online_endpoint(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.get_token_online_endpoint( + name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_list_online_deployments(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.list_online_deployments( + endpoint_name="str", + ) + result = [r for r in response] + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_delete_online_deployment(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.delete_online_deployment( + endpoint_name="str", + deployment_name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_get_online_deployment(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.get_online_deployment( + endpoint_name="str", + deployment_name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_update_online_deployment(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.update_online_deployment( + endpoint_name="str", + deployment_name="str", + body={ + "sku": {"capacity": 0, "family": "str", "name": "str", "size": "str", "tier": "str"}, + "tags": {"str": "str"}, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_create_or_update_online_deployment(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.create_or_update_online_deployment( + endpoint_name="str", + deployment_name="str", + body={ + "appInsightsEnabled": bool, + "codeConfiguration": {"scoringScript": "str", "codeId": "str"}, + "dataCollector": { + "collections": { + "str": {"clientId": "str", "dataCollectionMode": "str", "dataId": "str", "samplingRate": 0.0} + }, + "requestLogging": {"captureHeaders": ["str"]}, + "rollingRate": "str", + }, + "description": "str", + "egressPublicNetworkAccess": "str", + "environmentId": "str", + "environmentVariables": {"str": "str"}, + "instanceType": "str", + "livenessProbe": { + "failureThreshold": 0, + "initialDelay": "1 day, 0:00:00", + "period": "1 day, 0:00:00", + "successThreshold": 0, + "timeout": "1 day, 0:00:00", + }, + "model": "str", + "modelMountPath": "str", + "properties": {"str": "str"}, + "provisioningState": "str", + "readinessProbe": { + "failureThreshold": 0, + "initialDelay": "1 day, 0:00:00", + "period": "1 day, 0:00:00", + "successThreshold": 0, + "timeout": "1 day, 0:00:00", + }, + "requestSettings": { + "maxConcurrentRequestsPerInstance": 0, + "maxQueueWait": "1 day, 0:00:00", + "requestTimeout": "1 day, 0:00:00", + }, + "scaleSettings": {}, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_poll_logs_online_deployment(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.poll_logs_online_deployment( + endpoint_name="str", + deployment_name="str", + body={"containerType": "str", "tail": 0}, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_get_skus_online_deployment(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.get_skus_online_deployment( + endpoint_name="str", + deployment_name="str", + count=0, + ) + result = [r for r in response] + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_get_batch_endpoint(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.get_batch_endpoint( + endpoint_name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_delete_batch_endpoint(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.delete_batch_endpoint( + endpoint_name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_update_batch_endpoint(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.update_batch_endpoint( + endpoint_name="str", + body={"identity": {"type": "str", "userAssignedIdentities": {"str": {"str": {}}}}, "tags": {"str": "str"}}, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_create_or_update_batch_endpoint(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.create_or_update_batch_endpoint( + endpoint_name="str", + body={ + "authMode": "str", + "defaults": {"deploymentName": "str"}, + "description": "str", + "keys": {"primaryKey": "str", "secondaryKey": "str"}, + "properties": {"str": "str"}, + "provisioningState": "str", + "scoringUri": "str", + "swaggerUri": "str", + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_list_batch_endpoints(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.list_batch_endpoints() + result = [r for r in response] + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_list_batch_deployments(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.list_batch_deployments( + endpoint_name="str", + ) + result = [r for r in response] + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_delete_batch_deployment(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.delete_batch_deployment( + endpoint_name="str", + deployment_name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_get_batch_deployment(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.get_batch_deployment( + endpoint_name="str", + deployment_name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_update_batch_deployment(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.update_batch_deployment( + endpoint_name="str", + deployment_name="str", + body={"properties": {"description": "str"}, "tags": {"str": "str"}}, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_create_or_update_batch_deployment(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.create_or_update_batch_deployment( + endpoint_name="str", + deployment_name="str", + body={ + "codeConfiguration": {"scoringScript": "str", "codeId": "str"}, + "compute": "str", + "deploymentConfiguration": {}, + "description": "str", + "environmentId": "str", + "environmentVariables": {"str": "str"}, + "errorThreshold": 0, + "loggingLevel": "str", + "maxConcurrencyPerInstance": 0, + "miniBatchSize": 0, + "model": "asset_reference_base", + "outputAction": "str", + "outputFileName": "str", + "properties": {"str": "str"}, + "provisioningState": "str", + "resources": {"instanceCount": 0, "instanceType": "str", "properties": {"str": {"str": {}}}}, + "retrySettings": {"maxRetries": 0, "timeout": "1 day, 0:00:00"}, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_create_message(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.create_message( + thread_id="str", + body={ + "content": "str", + "role": "str", + "attachments": [{"file_id": "str", "tools": [{"type": "code_interpreter"}]}], + "metadata": {"str": "str"}, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_list_messages(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.list_messages( + thread_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_get_message(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.get_message( + thread_id="str", + message_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_update_message(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.update_message( + thread_id="str", + message_id="str", + body={"metadata": {"str": "str"}}, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_get_run_step(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.get_run_step( + thread_id="str", + run_id="str", + step_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_list_run_steps(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.list_run_steps( + thread_id="str", + run_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_create_run(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.create_run( + thread_id="str", + body={ + "assistant_id": "str", + "additional_instructions": "str", + "additional_messages": [ + { + "assistant_id": "str", + "attachments": [{"file_id": "str", "tools": [{"type": "code_interpreter"}]}], + "completed_at": "2020-02-20 00:00:00", + "content": ["message_content"], + "created_at": "2020-02-20 00:00:00", + "id": "str", + "incomplete_at": "2020-02-20 00:00:00", + "incomplete_details": {"reason": "str"}, + "metadata": {"str": "str"}, + "object": "thread.message", + "role": "str", + "run_id": "str", + "status": "str", + "thread_id": "str", + } + ], + "instructions": "str", + "max_completion_tokens": 0, + "max_prompt_tokens": 0, + "metadata": {"str": "str"}, + "model": "str", + "response_format": "str", + "stream": bool, + "temperature": 0.0, + "tool_choice": "str", + "tools": ["tool_definition"], + "top_p": 0.0, + "truncation_strategy": {"type": "str", "last_messages": 0}, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_list_runs(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.list_runs( + thread_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_get_run(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.get_run( + thread_id="str", + run_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_update_run(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.update_run( + thread_id="str", + run_id="str", + body={"metadata": {"str": "str"}}, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_submit_tool_outputs_to_run(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.submit_tool_outputs_to_run( + thread_id="str", + run_id="str", + body={"tool_outputs": [{"output": "str", "tool_call_id": "str"}], "stream": bool}, + tool_outputs=[{"output": "str", "tool_call_id": "str"}], + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_cancel_run(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.cancel_run( + thread_id="str", + run_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_create_thread_and_run(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.create_thread_and_run( + body={ + "assistant_id": "str", + "instructions": "str", + "max_completion_tokens": 0, + "max_prompt_tokens": 0, + "metadata": {"str": "str"}, + "model": "str", + "response_format": "str", + "stream": bool, + "temperature": 0.0, + "thread": { + "messages": [ + { + "content": "str", + "role": "str", + "attachments": [{"file_id": "str", "tools": [{"type": "code_interpreter"}]}], + "metadata": {"str": "str"}, + } + ], + "metadata": {"str": "str"}, + "tool_resources": {"code_interpreter": {"file_ids": ["str"]}, "file_search": ["str"]}, + }, + "tool_choice": "str", + "tool_resources": { + "code_interpreter": {"file_ids": ["str"]}, + "file_search": {"vector_store_ids": ["str"]}, + }, + "tools": ["tool_definition"], + "top_p": 0.0, + "truncation_strategy": {"type": "str", "last_messages": 0}, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_create_thread(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.create_thread( + body={ + "messages": [ + { + "content": "str", + "role": "str", + "attachments": [{"file_id": "str", "tools": [{"type": "code_interpreter"}]}], + "metadata": {"str": "str"}, + } + ], + "metadata": {"str": "str"}, + "tool_resources": {"code_interpreter": {"file_ids": ["str"]}, "file_search": ["str"]}, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_get_thread(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.get_thread( + thread_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_update_thread(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.update_thread( + thread_id="str", + body={ + "metadata": {"str": "str"}, + "tool_resources": { + "code_interpreter": {"file_ids": ["str"]}, + "file_search": {"vector_store_ids": ["str"]}, + }, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_delete_thread(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.delete_thread( + thread_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_list_vector_stores(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.list_vector_stores() + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_create_vector_store(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.create_vector_store( + body={ + "chunking_strategy": "vector_store_chunking_strategy_request", + "expires_after": {"anchor": "str", "days": 0}, + "file_ids": ["str"], + "metadata": {"str": "str"}, + "name": "str", + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_get_vector_store(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.get_vector_store( + vector_store_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_modify_vector_store(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.modify_vector_store( + vector_store_id="str", + body={"expires_after": {"anchor": "str", "days": 0}, "metadata": {"str": "str"}, "name": "str"}, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_delete_vector_store(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.delete_vector_store( + vector_store_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_list_vector_store_files(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.list_vector_store_files( + vector_store_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_create_vector_store_file(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.create_vector_store_file( + vector_store_id="str", + body={"file_id": "str", "chunking_strategy": "vector_store_chunking_strategy_request"}, + file_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_get_vector_store_file(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.get_vector_store_file( + vector_store_id="str", + file_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_delete_vector_store_file(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.delete_vector_store_file( + vector_store_id="str", + file_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_create_vector_store_file_batch(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.create_vector_store_file_batch( + vector_store_id="str", + body={"file_ids": ["str"], "chunking_strategy": "vector_store_chunking_strategy_request"}, + file_ids=["str"], + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_get_vector_store_file_batch(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.get_vector_store_file_batch( + vector_store_id="str", + batch_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_cancel_vector_store_file_batch(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.cancel_vector_store_file_batch( + vector_store_id="str", + batch_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_list_vector_store_file_batch_files(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.list_vector_store_file_batch_files( + vector_store_id="str", + batch_id="str", + ) + + # please add some check logic here by yourself + # ... diff --git a/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_async.py b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_async.py new file mode 100644 index 000000000000..34b00e87f6a7 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_async.py @@ -0,0 +1,966 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import pytest +from devtools_testutils.aio import recorded_by_proxy_async +from testpreparer import MachineLearningServicesPreparer +from testpreparer_async import MachineLearningServicesClientTestBaseAsync + + +@pytest.mark.skip("you may need to update the auto-generated test case before run it") +class TestMachineLearningServicesAsync(MachineLearningServicesClientTestBaseAsync): + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_create_assistant(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.create_assistant( + body={ + "model": "str", + "description": "str", + "instructions": "str", + "metadata": {"str": "str"}, + "name": "str", + "response_format": "str", + "temperature": 0.0, + "tool_resources": {"code_interpreter": {"file_ids": ["str"]}, "file_search": ["str"]}, + "tools": ["tool_definition"], + "top_p": 0.0, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_list_assistants(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.list_assistants() + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_get_assistant(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.get_assistant( + assistant_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_update_assistant(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.update_assistant( + assistant_id="str", + body={ + "description": "str", + "instructions": "str", + "metadata": {"str": "str"}, + "model": "str", + "name": "str", + "response_format": "str", + "temperature": 0.0, + "tool_resources": { + "code_interpreter": {"file_ids": ["str"]}, + "file_search": {"vector_store_ids": ["str"]}, + }, + "tools": ["tool_definition"], + "top_p": 0.0, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_delete_assistant(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.delete_assistant( + assistant_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_list_files(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.list_files() + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_upload_file(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.upload_file( + body={"file": "filetype", "purpose": "str", "filename": "str"}, + file="filetype", + purpose="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_delete_file(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.delete_file( + file_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_get_file(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.get_file( + file_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_get_file_content(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.get_file_content( + file_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_get_online_endpoint(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.get_online_endpoint( + name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_delete_online_endpoint(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.delete_online_endpoint( + name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_update_online_endpoint(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.update_online_endpoint( + name="str", + body={"identity": {"type": "str", "userAssignedIdentities": {"str": {"str": {}}}}, "tags": {"str": "str"}}, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_create_or_update_online_endpoint(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.create_or_update_online_endpoint( + name="str", + body={ + "authMode": "str", + "compute": "str", + "description": "str", + "keys": {"primaryKey": "str", "secondaryKey": "str"}, + "mirrorTraffic": {"str": 0}, + "properties": {"str": "str"}, + "provisioningState": "str", + "publicNetworkAccess": "str", + "scoringUri": "str", + "swaggerUri": "str", + "traffic": {"str": 0}, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_list_online_endpoints(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = client.list_online_endpoints() + result = [r async for r in response] + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_list_keys_online_endpoint(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.list_keys_online_endpoint( + name="str", + body={"keyType": "str", "keyValue": "str"}, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_regenerate_keys_online_endpoint(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.regenerate_keys_online_endpoint( + name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_get_token_online_endpoint(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.get_token_online_endpoint( + name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_list_online_deployments(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = client.list_online_deployments( + endpoint_name="str", + ) + result = [r async for r in response] + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_delete_online_deployment(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.delete_online_deployment( + endpoint_name="str", + deployment_name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_get_online_deployment(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.get_online_deployment( + endpoint_name="str", + deployment_name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_update_online_deployment(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.update_online_deployment( + endpoint_name="str", + deployment_name="str", + body={ + "sku": {"capacity": 0, "family": "str", "name": "str", "size": "str", "tier": "str"}, + "tags": {"str": "str"}, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_create_or_update_online_deployment(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.create_or_update_online_deployment( + endpoint_name="str", + deployment_name="str", + body={ + "appInsightsEnabled": bool, + "codeConfiguration": {"scoringScript": "str", "codeId": "str"}, + "dataCollector": { + "collections": { + "str": {"clientId": "str", "dataCollectionMode": "str", "dataId": "str", "samplingRate": 0.0} + }, + "requestLogging": {"captureHeaders": ["str"]}, + "rollingRate": "str", + }, + "description": "str", + "egressPublicNetworkAccess": "str", + "environmentId": "str", + "environmentVariables": {"str": "str"}, + "instanceType": "str", + "livenessProbe": { + "failureThreshold": 0, + "initialDelay": "1 day, 0:00:00", + "period": "1 day, 0:00:00", + "successThreshold": 0, + "timeout": "1 day, 0:00:00", + }, + "model": "str", + "modelMountPath": "str", + "properties": {"str": "str"}, + "provisioningState": "str", + "readinessProbe": { + "failureThreshold": 0, + "initialDelay": "1 day, 0:00:00", + "period": "1 day, 0:00:00", + "successThreshold": 0, + "timeout": "1 day, 0:00:00", + }, + "requestSettings": { + "maxConcurrentRequestsPerInstance": 0, + "maxQueueWait": "1 day, 0:00:00", + "requestTimeout": "1 day, 0:00:00", + }, + "scaleSettings": {}, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_poll_logs_online_deployment(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.poll_logs_online_deployment( + endpoint_name="str", + deployment_name="str", + body={"containerType": "str", "tail": 0}, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_get_skus_online_deployment(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = client.get_skus_online_deployment( + endpoint_name="str", + deployment_name="str", + count=0, + ) + result = [r async for r in response] + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_get_batch_endpoint(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.get_batch_endpoint( + endpoint_name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_delete_batch_endpoint(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.delete_batch_endpoint( + endpoint_name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_update_batch_endpoint(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.update_batch_endpoint( + endpoint_name="str", + body={"identity": {"type": "str", "userAssignedIdentities": {"str": {"str": {}}}}, "tags": {"str": "str"}}, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_create_or_update_batch_endpoint(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.create_or_update_batch_endpoint( + endpoint_name="str", + body={ + "authMode": "str", + "defaults": {"deploymentName": "str"}, + "description": "str", + "keys": {"primaryKey": "str", "secondaryKey": "str"}, + "properties": {"str": "str"}, + "provisioningState": "str", + "scoringUri": "str", + "swaggerUri": "str", + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_list_batch_endpoints(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = client.list_batch_endpoints() + result = [r async for r in response] + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_list_batch_deployments(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = client.list_batch_deployments( + endpoint_name="str", + ) + result = [r async for r in response] + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_delete_batch_deployment(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.delete_batch_deployment( + endpoint_name="str", + deployment_name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_get_batch_deployment(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.get_batch_deployment( + endpoint_name="str", + deployment_name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_update_batch_deployment(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.update_batch_deployment( + endpoint_name="str", + deployment_name="str", + body={"properties": {"description": "str"}, "tags": {"str": "str"}}, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_create_or_update_batch_deployment(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.create_or_update_batch_deployment( + endpoint_name="str", + deployment_name="str", + body={ + "codeConfiguration": {"scoringScript": "str", "codeId": "str"}, + "compute": "str", + "deploymentConfiguration": {}, + "description": "str", + "environmentId": "str", + "environmentVariables": {"str": "str"}, + "errorThreshold": 0, + "loggingLevel": "str", + "maxConcurrencyPerInstance": 0, + "miniBatchSize": 0, + "model": "asset_reference_base", + "outputAction": "str", + "outputFileName": "str", + "properties": {"str": "str"}, + "provisioningState": "str", + "resources": {"instanceCount": 0, "instanceType": "str", "properties": {"str": {"str": {}}}}, + "retrySettings": {"maxRetries": 0, "timeout": "1 day, 0:00:00"}, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_create_message(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.create_message( + thread_id="str", + body={ + "content": "str", + "role": "str", + "attachments": [{"file_id": "str", "tools": [{"type": "code_interpreter"}]}], + "metadata": {"str": "str"}, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_list_messages(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.list_messages( + thread_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_get_message(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.get_message( + thread_id="str", + message_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_update_message(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.update_message( + thread_id="str", + message_id="str", + body={"metadata": {"str": "str"}}, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_get_run_step(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.get_run_step( + thread_id="str", + run_id="str", + step_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_list_run_steps(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.list_run_steps( + thread_id="str", + run_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_create_run(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.create_run( + thread_id="str", + body={ + "assistant_id": "str", + "additional_instructions": "str", + "additional_messages": [ + { + "assistant_id": "str", + "attachments": [{"file_id": "str", "tools": [{"type": "code_interpreter"}]}], + "completed_at": "2020-02-20 00:00:00", + "content": ["message_content"], + "created_at": "2020-02-20 00:00:00", + "id": "str", + "incomplete_at": "2020-02-20 00:00:00", + "incomplete_details": {"reason": "str"}, + "metadata": {"str": "str"}, + "object": "thread.message", + "role": "str", + "run_id": "str", + "status": "str", + "thread_id": "str", + } + ], + "instructions": "str", + "max_completion_tokens": 0, + "max_prompt_tokens": 0, + "metadata": {"str": "str"}, + "model": "str", + "response_format": "str", + "stream": bool, + "temperature": 0.0, + "tool_choice": "str", + "tools": ["tool_definition"], + "top_p": 0.0, + "truncation_strategy": {"type": "str", "last_messages": 0}, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_list_runs(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.list_runs( + thread_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_get_run(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.get_run( + thread_id="str", + run_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_update_run(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.update_run( + thread_id="str", + run_id="str", + body={"metadata": {"str": "str"}}, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_submit_tool_outputs_to_run(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.submit_tool_outputs_to_run( + thread_id="str", + run_id="str", + body={"tool_outputs": [{"output": "str", "tool_call_id": "str"}], "stream": bool}, + tool_outputs=[{"output": "str", "tool_call_id": "str"}], + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_cancel_run(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.cancel_run( + thread_id="str", + run_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_create_thread_and_run(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.create_thread_and_run( + body={ + "assistant_id": "str", + "instructions": "str", + "max_completion_tokens": 0, + "max_prompt_tokens": 0, + "metadata": {"str": "str"}, + "model": "str", + "response_format": "str", + "stream": bool, + "temperature": 0.0, + "thread": { + "messages": [ + { + "content": "str", + "role": "str", + "attachments": [{"file_id": "str", "tools": [{"type": "code_interpreter"}]}], + "metadata": {"str": "str"}, + } + ], + "metadata": {"str": "str"}, + "tool_resources": {"code_interpreter": {"file_ids": ["str"]}, "file_search": ["str"]}, + }, + "tool_choice": "str", + "tool_resources": { + "code_interpreter": {"file_ids": ["str"]}, + "file_search": {"vector_store_ids": ["str"]}, + }, + "tools": ["tool_definition"], + "top_p": 0.0, + "truncation_strategy": {"type": "str", "last_messages": 0}, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_create_thread(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.create_thread( + body={ + "messages": [ + { + "content": "str", + "role": "str", + "attachments": [{"file_id": "str", "tools": [{"type": "code_interpreter"}]}], + "metadata": {"str": "str"}, + } + ], + "metadata": {"str": "str"}, + "tool_resources": {"code_interpreter": {"file_ids": ["str"]}, "file_search": ["str"]}, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_get_thread(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.get_thread( + thread_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_update_thread(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.update_thread( + thread_id="str", + body={ + "metadata": {"str": "str"}, + "tool_resources": { + "code_interpreter": {"file_ids": ["str"]}, + "file_search": {"vector_store_ids": ["str"]}, + }, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_delete_thread(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.delete_thread( + thread_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_list_vector_stores(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.list_vector_stores() + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_create_vector_store(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.create_vector_store( + body={ + "chunking_strategy": "vector_store_chunking_strategy_request", + "expires_after": {"anchor": "str", "days": 0}, + "file_ids": ["str"], + "metadata": {"str": "str"}, + "name": "str", + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_get_vector_store(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.get_vector_store( + vector_store_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_modify_vector_store(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.modify_vector_store( + vector_store_id="str", + body={"expires_after": {"anchor": "str", "days": 0}, "metadata": {"str": "str"}, "name": "str"}, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_delete_vector_store(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.delete_vector_store( + vector_store_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_list_vector_store_files(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.list_vector_store_files( + vector_store_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_create_vector_store_file(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.create_vector_store_file( + vector_store_id="str", + body={"file_id": "str", "chunking_strategy": "vector_store_chunking_strategy_request"}, + file_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_get_vector_store_file(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.get_vector_store_file( + vector_store_id="str", + file_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_delete_vector_store_file(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.delete_vector_store_file( + vector_store_id="str", + file_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_create_vector_store_file_batch(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.create_vector_store_file_batch( + vector_store_id="str", + body={"file_ids": ["str"], "chunking_strategy": "vector_store_chunking_strategy_request"}, + file_ids=["str"], + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_get_vector_store_file_batch(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.get_vector_store_file_batch( + vector_store_id="str", + batch_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_cancel_vector_store_file_batch(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.cancel_vector_store_file_batch( + vector_store_id="str", + batch_id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_list_vector_store_file_batch_files(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.list_vector_store_file_batch_files( + vector_store_id="str", + batch_id="str", + ) + + # please add some check logic here by yourself + # ... diff --git a/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_connections_operations.py b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_connections_operations.py new file mode 100644 index 000000000000..1dc4d076bf83 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_connections_operations.py @@ -0,0 +1,53 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import pytest +from devtools_testutils import recorded_by_proxy +from testpreparer import MachineLearningServicesClientTestBase, MachineLearningServicesPreparer + + +@pytest.mark.skip("you may need to update the auto-generated test case before run it") +class TestMachineLearningServicesConnectionsOperations(MachineLearningServicesClientTestBase): + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_connections_get(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.connections.get( + name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_connections_post(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.connections.post( + name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_connections_list(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.connections.list() + result = [r for r in response] + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_connections_list_with_credentials(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.connections.list_with_credentials() + result = [r for r in response] + # please add some check logic here by yourself + # ... diff --git a/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_connections_operations_async.py b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_connections_operations_async.py new file mode 100644 index 000000000000..b3e96cfcebaa --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_connections_operations_async.py @@ -0,0 +1,54 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import pytest +from devtools_testutils.aio import recorded_by_proxy_async +from testpreparer import MachineLearningServicesPreparer +from testpreparer_async import MachineLearningServicesClientTestBaseAsync + + +@pytest.mark.skip("you may need to update the auto-generated test case before run it") +class TestMachineLearningServicesConnectionsOperationsAsync(MachineLearningServicesClientTestBaseAsync): + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_connections_get(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.connections.get( + name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_connections_post(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.connections.post( + name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_connections_list(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = client.connections.list() + result = [r async for r in response] + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_connections_list_with_credentials(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = client.connections.list_with_credentials() + result = [r async for r in response] + # please add some check logic here by yourself + # ... diff --git a/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_data_operations.py b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_data_operations.py new file mode 100644 index 000000000000..8012c67e1c12 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_data_operations.py @@ -0,0 +1,64 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import pytest +from devtools_testutils import recorded_by_proxy +from testpreparer import MachineLearningServicesClientTestBase, MachineLearningServicesPreparer + + +@pytest.mark.skip("you may need to update the auto-generated test case before run it") +class TestMachineLearningServicesDataOperations(MachineLearningServicesClientTestBase): + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_data_list(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.data.list() + result = [r for r in response] + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_data_delete(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.data.delete( + workspace_name="str", + name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_data_get(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.data.get( + name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_data_create_or_update(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.data.create_or_update( + name="str", + body={ + "description": "str", + "isArchived": bool, + "latestVersion": "str", + "nextVersion": "str", + "properties": {"str": "str"}, + "tags": {"str": "str"}, + }, + ) + + # please add some check logic here by yourself + # ... diff --git a/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_data_operations_async.py b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_data_operations_async.py new file mode 100644 index 000000000000..818e222d42a0 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_data_operations_async.py @@ -0,0 +1,65 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import pytest +from devtools_testutils.aio import recorded_by_proxy_async +from testpreparer import MachineLearningServicesPreparer +from testpreparer_async import MachineLearningServicesClientTestBaseAsync + + +@pytest.mark.skip("you may need to update the auto-generated test case before run it") +class TestMachineLearningServicesDataOperationsAsync(MachineLearningServicesClientTestBaseAsync): + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_data_list(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = client.data.list() + result = [r async for r in response] + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_data_delete(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.data.delete( + workspace_name="str", + name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_data_get(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.data.get( + name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_data_create_or_update(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.data.create_or_update( + name="str", + body={ + "description": "str", + "isArchived": bool, + "latestVersion": "str", + "nextVersion": "str", + "properties": {"str": "str"}, + "tags": {"str": "str"}, + }, + ) + + # please add some check logic here by yourself + # ... diff --git a/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_data_versions_base_operations.py b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_data_versions_base_operations.py new file mode 100644 index 000000000000..840ef3eb18e2 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_data_versions_base_operations.py @@ -0,0 +1,82 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import pytest +from devtools_testutils import recorded_by_proxy +from testpreparer import MachineLearningServicesClientTestBase, MachineLearningServicesPreparer + + +@pytest.mark.skip("you may need to update the auto-generated test case before run it") +class TestMachineLearningServicesDataVersionsBaseOperations(MachineLearningServicesClientTestBase): + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_data_versions_base_list(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.data_versions_base.list( + name="str", + ) + result = [r for r in response] + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_data_versions_base_delete(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.data_versions_base.delete( + name="str", + version="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_data_versions_base_get(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.data_versions_base.get( + name="str", + version="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_data_versions_base_create_or_update(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.data_versions_base.create_or_update( + name="str", + version="str", + body={ + "dataType": "uri_file", + "dataUri": "str", + "description": "str", + "isAnonymous": bool, + "isArchived": bool, + "properties": {"str": "str"}, + "tags": {"str": "str"}, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_data_versions_base_publish(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.data_versions_base.publish( + name="str", + version="str", + body={"destinationName": "str", "destinationVersion": "str", "registryName": "str"}, + ) + + # please add some check logic here by yourself + # ... diff --git a/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_data_versions_base_operations_async.py b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_data_versions_base_operations_async.py new file mode 100644 index 000000000000..65d27a771d9e --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_data_versions_base_operations_async.py @@ -0,0 +1,83 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import pytest +from devtools_testutils.aio import recorded_by_proxy_async +from testpreparer import MachineLearningServicesPreparer +from testpreparer_async import MachineLearningServicesClientTestBaseAsync + + +@pytest.mark.skip("you may need to update the auto-generated test case before run it") +class TestMachineLearningServicesDataVersionsBaseOperationsAsync(MachineLearningServicesClientTestBaseAsync): + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_data_versions_base_list(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = client.data_versions_base.list( + name="str", + ) + result = [r async for r in response] + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_data_versions_base_delete(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.data_versions_base.delete( + name="str", + version="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_data_versions_base_get(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.data_versions_base.get( + name="str", + version="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_data_versions_base_create_or_update(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.data_versions_base.create_or_update( + name="str", + version="str", + body={ + "dataType": "uri_file", + "dataUri": "str", + "description": "str", + "isAnonymous": bool, + "isArchived": bool, + "properties": {"str": "str"}, + "tags": {"str": "str"}, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_data_versions_base_publish(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.data_versions_base.publish( + name="str", + version="str", + body={"destinationName": "str", "destinationVersion": "str", "registryName": "str"}, + ) + + # please add some check logic here by yourself + # ... diff --git a/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_evaluations_operations.py b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_evaluations_operations.py new file mode 100644 index 000000000000..aae91ee9d4f8 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_evaluations_operations.py @@ -0,0 +1,72 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import pytest +from devtools_testutils import recorded_by_proxy +from testpreparer import MachineLearningServicesClientTestBase, MachineLearningServicesPreparer + + +@pytest.mark.skip("you may need to update the auto-generated test case before run it") +class TestMachineLearningServicesEvaluationsOperations(MachineLearningServicesClientTestBase): + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_evaluations_get(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.evaluations.get( + id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_evaluations_create(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.evaluations.create( + body={ + "data": "input_data", + "evaluators": {"str": {"id": "str", "dataMapping": {"str": "str"}, "initParams": {"str": "str"}}}, + "description": "str", + "displayName": "str", + "evaluationTarget": "evaluation_target", + "id": "str", + "properties": {"str": "str"}, + "status": "str", + "systemData": { + "createdAt": "2020-02-20 00:00:00", + "createdBy": "str", + "createdByType": "str", + "lastModifiedAt": "2020-02-20 00:00:00", + }, + "tags": {"str": "str"}, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_evaluations_list(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.evaluations.list() + result = [r for r in response] + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_evaluations_update(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.evaluations.update( + id="str", + body={"description": "str", "displayName": "str", "tags": {"str": "str"}}, + ) + + # please add some check logic here by yourself + # ... diff --git a/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_evaluations_operations_async.py b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_evaluations_operations_async.py new file mode 100644 index 000000000000..d6cda74d49e2 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_evaluations_operations_async.py @@ -0,0 +1,73 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import pytest +from devtools_testutils.aio import recorded_by_proxy_async +from testpreparer import MachineLearningServicesPreparer +from testpreparer_async import MachineLearningServicesClientTestBaseAsync + + +@pytest.mark.skip("you may need to update the auto-generated test case before run it") +class TestMachineLearningServicesEvaluationsOperationsAsync(MachineLearningServicesClientTestBaseAsync): + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_evaluations_get(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.evaluations.get( + id="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_evaluations_create(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.evaluations.create( + body={ + "data": "input_data", + "evaluators": {"str": {"id": "str", "dataMapping": {"str": "str"}, "initParams": {"str": "str"}}}, + "description": "str", + "displayName": "str", + "evaluationTarget": "evaluation_target", + "id": "str", + "properties": {"str": "str"}, + "status": "str", + "systemData": { + "createdAt": "2020-02-20 00:00:00", + "createdBy": "str", + "createdByType": "str", + "lastModifiedAt": "2020-02-20 00:00:00", + }, + "tags": {"str": "str"}, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_evaluations_list(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = client.evaluations.list() + result = [r async for r in response] + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_evaluations_update(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.evaluations.update( + id="str", + body={"description": "str", "displayName": "str", "tags": {"str": "str"}}, + ) + + # please add some check logic here by yourself + # ... diff --git a/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_indexes_operations.py b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_indexes_operations.py new file mode 100644 index 000000000000..44f4f900672b --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_indexes_operations.py @@ -0,0 +1,94 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import pytest +from devtools_testutils import recorded_by_proxy +from testpreparer import MachineLearningServicesClientTestBase, MachineLearningServicesPreparer + + +@pytest.mark.skip("you may need to update the auto-generated test case before run it") +class TestMachineLearningServicesIndexesOperations(MachineLearningServicesClientTestBase): + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_indexes_get(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.indexes.get( + name="str", + version="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_indexes_create_or_update(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.indexes.create_or_update( + name="str", + version="str", + body={ + "id": "str", + "storageUri": "str", + "description": "str", + "properties": {"str": "str"}, + "stage": "str", + "systemData": { + "createdAt": "2020-02-20 00:00:00", + "createdBy": "str", + "createdByType": "str", + "lastModifiedAt": "2020-02-20 00:00:00", + }, + "tags": {"str": "str"}, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_indexes_list(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.indexes.list( + name="str", + list_view_type="str", + ) + result = [r for r in response] + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_indexes_get_latest(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.indexes.get_latest( + name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_indexes_get_next_version(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.indexes.get_next_version( + name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_indexes_list_latest(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.indexes.list_latest() + result = [r for r in response] + # please add some check logic here by yourself + # ... diff --git a/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_indexes_operations_async.py b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_indexes_operations_async.py new file mode 100644 index 000000000000..f3d560f1a539 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_indexes_operations_async.py @@ -0,0 +1,95 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import pytest +from devtools_testutils.aio import recorded_by_proxy_async +from testpreparer import MachineLearningServicesPreparer +from testpreparer_async import MachineLearningServicesClientTestBaseAsync + + +@pytest.mark.skip("you may need to update the auto-generated test case before run it") +class TestMachineLearningServicesIndexesOperationsAsync(MachineLearningServicesClientTestBaseAsync): + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_indexes_get(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.indexes.get( + name="str", + version="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_indexes_create_or_update(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.indexes.create_or_update( + name="str", + version="str", + body={ + "id": "str", + "storageUri": "str", + "description": "str", + "properties": {"str": "str"}, + "stage": "str", + "systemData": { + "createdAt": "2020-02-20 00:00:00", + "createdBy": "str", + "createdByType": "str", + "lastModifiedAt": "2020-02-20 00:00:00", + }, + "tags": {"str": "str"}, + }, + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_indexes_list(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = client.indexes.list( + name="str", + list_view_type="str", + ) + result = [r async for r in response] + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_indexes_get_latest(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.indexes.get_latest( + name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_indexes_get_next_version(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.indexes.get_next_version( + name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_indexes_list_latest(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = client.indexes.list_latest() + result = [r async for r in response] + # please add some check logic here by yourself + # ... diff --git a/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_model_containers_operations.py b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_model_containers_operations.py new file mode 100644 index 000000000000..cba0e15e61f2 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_model_containers_operations.py @@ -0,0 +1,35 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import pytest +from devtools_testutils import recorded_by_proxy +from testpreparer import MachineLearningServicesClientTestBase, MachineLearningServicesPreparer + + +@pytest.mark.skip("you may need to update the auto-generated test case before run it") +class TestMachineLearningServicesModelContainersOperations(MachineLearningServicesClientTestBase): + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_model_containers_get(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.model_containers.get( + name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_model_containers_create_or_update(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.model_containers.create_or_update( + name="str", + ) + + # please add some check logic here by yourself + # ... diff --git a/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_model_containers_operations_async.py b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_model_containers_operations_async.py new file mode 100644 index 000000000000..2d4d25b2bacf --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_model_containers_operations_async.py @@ -0,0 +1,36 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import pytest +from devtools_testutils.aio import recorded_by_proxy_async +from testpreparer import MachineLearningServicesPreparer +from testpreparer_async import MachineLearningServicesClientTestBaseAsync + + +@pytest.mark.skip("you may need to update the auto-generated test case before run it") +class TestMachineLearningServicesModelContainersOperationsAsync(MachineLearningServicesClientTestBaseAsync): + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_model_containers_get(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.model_containers.get( + name="str", + ) + + # please add some check logic here by yourself + # ... + + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_model_containers_create_or_update(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = await client.model_containers.create_or_update( + name="str", + ) + + # please add some check logic here by yourself + # ... diff --git a/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_model_versions_operations.py b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_model_versions_operations.py new file mode 100644 index 000000000000..411a88ccb291 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_model_versions_operations.py @@ -0,0 +1,24 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import pytest +from devtools_testutils import recorded_by_proxy +from testpreparer import MachineLearningServicesClientTestBase, MachineLearningServicesPreparer + + +@pytest.mark.skip("you may need to update the auto-generated test case before run it") +class TestMachineLearningServicesModelVersionsOperations(MachineLearningServicesClientTestBase): + @MachineLearningServicesPreparer() + @recorded_by_proxy + def test_model_versions_list(self, machinelearningservices_endpoint): + client = self.create_client(endpoint=machinelearningservices_endpoint) + response = client.model_versions.list( + name="str", + ) + result = [r for r in response] + # please add some check logic here by yourself + # ... diff --git a/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_model_versions_operations_async.py b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_model_versions_operations_async.py new file mode 100644 index 000000000000..50a14abe4de0 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/generated_tests/test_machine_learning_services_model_versions_operations_async.py @@ -0,0 +1,25 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +import pytest +from devtools_testutils.aio import recorded_by_proxy_async +from testpreparer import MachineLearningServicesPreparer +from testpreparer_async import MachineLearningServicesClientTestBaseAsync + + +@pytest.mark.skip("you may need to update the auto-generated test case before run it") +class TestMachineLearningServicesModelVersionsOperationsAsync(MachineLearningServicesClientTestBaseAsync): + @MachineLearningServicesPreparer() + @recorded_by_proxy_async + async def test_model_versions_list(self, machinelearningservices_endpoint): + client = self.create_async_client(endpoint=machinelearningservices_endpoint) + response = client.model_versions.list( + name="str", + ) + result = [r async for r in response] + # please add some check logic here by yourself + # ... diff --git a/sdk/ai/azure-ai-resources-autogen/generated_tests/testpreparer.py b/sdk/ai/azure-ai-resources-autogen/generated_tests/testpreparer.py new file mode 100644 index 000000000000..7015943fc2b6 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/generated_tests/testpreparer.py @@ -0,0 +1,28 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +from azure.ai.resources.autogen import MachineLearningServicesClient +from devtools_testutils import AzureRecordedTestCase, PowerShellPreparer +import functools + + +class MachineLearningServicesClientTestBase(AzureRecordedTestCase): + + def create_client(self, endpoint): + credential = self.get_credential(MachineLearningServicesClient) + return self.create_client_from_credential( + MachineLearningServicesClient, + credential=credential, + endpoint=endpoint, + ) + + +MachineLearningServicesPreparer = functools.partial( + PowerShellPreparer, + "machinelearningservices", + machinelearningservices_endpoint="https://fake_machinelearningservices_endpoint.com", +) diff --git a/sdk/ai/azure-ai-resources-autogen/generated_tests/testpreparer_async.py b/sdk/ai/azure-ai-resources-autogen/generated_tests/testpreparer_async.py new file mode 100644 index 000000000000..06bb79cb62e5 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/generated_tests/testpreparer_async.py @@ -0,0 +1,20 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +from azure.ai.resources.autogen.aio import MachineLearningServicesClient +from devtools_testutils import AzureRecordedTestCase + + +class MachineLearningServicesClientTestBaseAsync(AzureRecordedTestCase): + + def create_async_client(self, endpoint): + credential = self.get_credential(MachineLearningServicesClient, is_async=True) + return self.create_client_from_credential( + MachineLearningServicesClient, + credential=credential, + endpoint=endpoint, + ) diff --git a/sdk/ai/azure-ai-resources-autogen/sdk_packaging.toml b/sdk/ai/azure-ai-resources-autogen/sdk_packaging.toml new file mode 100644 index 000000000000..e7687fdae93b --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/sdk_packaging.toml @@ -0,0 +1,2 @@ +[packaging] +auto_update = false \ No newline at end of file diff --git a/sdk/ai/azure-ai-resources-autogen/setup.py b/sdk/ai/azure-ai-resources-autogen/setup.py new file mode 100644 index 000000000000..5a58d1fef808 --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/setup.py @@ -0,0 +1,72 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +# coding: utf-8 + +import os +import re +from setuptools import setup, find_packages + + +PACKAGE_NAME = "azure-ai-resources-autogen" +PACKAGE_PPRINT_NAME = "Azure Ai Resources Autogen" + +# a-b-c => a/b/c +package_folder_path = PACKAGE_NAME.replace("-", "/") + +# Version extraction inspired from 'requests' +with open(os.path.join(package_folder_path, "_version.py"), "r") as fd: + version = re.search(r'^VERSION\s*=\s*[\'"]([^\'"]*)[\'"]', fd.read(), re.MULTILINE).group(1) + +if not version: + raise RuntimeError("Cannot find version information") + + +setup( + name=PACKAGE_NAME, + version=version, + description="Microsoft {} Client Library for Python".format(PACKAGE_PPRINT_NAME), + long_description=open("README.md", "r").read(), + long_description_content_type="text/markdown", + license="MIT License", + author="Microsoft Corporation", + author_email="azpysdkhelp@microsoft.com", + url="https://github.com/Azure/azure-sdk-for-python/tree/main/sdk", + keywords="azure, azure sdk", + classifiers=[ + "Development Status :: 4 - Beta", + "Programming Language :: Python", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "License :: OSI Approved :: MIT License", + ], + zip_safe=False, + packages=find_packages( + exclude=[ + "tests", + # Exclude packages that will be covered by PEP420 or nspkg + "azure", + "azure.ai", + "azure.ai.resources", + ] + ), + include_package_data=True, + package_data={ + "azure.ai.resources.autogen": ["py.typed"], + }, + install_requires=[ + "isodate>=0.6.1", + "azure-core>=1.30.0", + "typing-extensions>=4.6.0", + ], + python_requires=">=3.8", +) diff --git a/sdk/ai/azure-ai-resources-autogen/tsp-location.yaml b/sdk/ai/azure-ai-resources-autogen/tsp-location.yaml new file mode 100644 index 000000000000..d2f4a2b263ef --- /dev/null +++ b/sdk/ai/azure-ai-resources-autogen/tsp-location.yaml @@ -0,0 +1,4 @@ +directory: specification/machinelearningservices/AzureAI.Unified +commit: 5f1dcf05a1adc5595442b87eeb06b9c7a1831f63 +repo: Azure/azure-rest-api-specs +additionalDirectories: diff --git a/sdk/ai/ci.yml b/sdk/ai/ci.yml index 5b839a88424f..76eb5da740b1 100644 --- a/sdk/ai/ci.yml +++ b/sdk/ai/ci.yml @@ -59,3 +59,5 @@ extends: # These packages are deprecated: #- name: azure-ai-resources # safeName: azureairesources + - name: azure-ai-resources-autogen + safeName: azureairesourcesautogen