diff --git a/sdk/ai/azure-ai-projects/_meta.json b/sdk/ai/azure-ai-projects/_meta.json new file mode 100644 index 000000000000..78a7562d1b0f --- /dev/null +++ b/sdk/ai/azure-ai-projects/_meta.json @@ -0,0 +1,6 @@ +{ + "commit": "f443736b9a9493d983e010fcf2b78dcc58ef5344", + "repository_url": "https://github.com/Azure/azure-rest-api-specs", + "typespec_src": "specification/ai/Azure.AI.Projects", + "@azure-tools/typespec-python": "0.38.4" +} \ No newline at end of file diff --git a/sdk/ai/azure-ai-projects/apiview-properties.json b/sdk/ai/azure-ai-projects/apiview-properties.json new file mode 100644 index 000000000000..dd9053deb39e --- /dev/null +++ b/sdk/ai/azure-ai-projects/apiview-properties.json @@ -0,0 +1,28 @@ +{ + "CrossLanguagePackageId": "Azure.AI.Projects", + "CrossLanguageDefinitionId": { + "azure.ai.projects.models.InputData": "Azure.AI.Projects.InputData", + "azure.ai.projects.models.ApplicationInsightsConfiguration": "Azure.AI.Projects.ApplicationInsightsConfiguration", + "azure.ai.projects.models.Trigger": "Azure.AI.Projects.Trigger", + "azure.ai.projects.models.CronTrigger": "Azure.AI.Projects.CronTrigger", + "azure.ai.projects.models.Dataset": "Azure.AI.Projects.Dataset", + "azure.ai.projects.models.Evaluation": "Azure.AI.Projects.Evaluation", + "azure.ai.projects.models.EvaluationSchedule": "Azure.AI.Projects.EvaluationSchedule", + "azure.ai.projects.models.EvaluatorConfiguration": "Azure.AI.Projects.EvaluatorConfiguration", + "azure.ai.projects.models.RecurrenceSchedule": "Azure.AI.Projects.RecurrenceSchedule", + "azure.ai.projects.models.RecurrenceTrigger": "Azure.AI.Projects.RecurrenceTrigger", + "azure.ai.projects.models.SystemData": "Azure.AI.Projects.SystemData", + "azure.ai.projects.models.AuthenticationType": "Azure.AI.Projects.AuthenticationType", + "azure.ai.projects.models.ConnectionType": "Azure.AI.Projects.ConnectionType", + "azure.ai.projects.models.Frequency": "Azure.AI.Projects.Frequency", + "azure.ai.projects.models.WeekDays": "Azure.AI.Projects.WeekDays", + "azure.ai.projects.AIProjectClient.evaluations.get": "Azure.AI.Projects.Evaluations.get", + "azure.ai.projects.AIProjectClient.evaluations.create": "Azure.AI.Projects.Evaluations.create", + "azure.ai.projects.AIProjectClient.evaluations.list": "Azure.AI.Projects.Evaluations.list", + "azure.ai.projects.AIProjectClient.evaluations.update": "Azure.AI.Projects.Evaluations.update", + "azure.ai.projects.AIProjectClient.evaluations.get_schedule": "Azure.AI.Projects.Evaluations.getSchedule", + "azure.ai.projects.AIProjectClient.evaluations.create_or_replace_schedule": "Azure.AI.Projects.Evaluations.createOrReplaceSchedule", + "azure.ai.projects.AIProjectClient.evaluations.list_schedule": "Azure.AI.Projects.Evaluations.listSchedule", + "azure.ai.projects.AIProjectClient.evaluations.disable_schedule": "Azure.AI.Projects.Evaluations.disableSchedule" + } +} \ No newline at end of file diff --git a/sdk/ai/azure-ai-projects/azure/ai/projects/_client.py b/sdk/ai/azure-ai-projects/azure/ai/projects/_client.py index b1fa737e1512..cc9f7c43940b 100644 --- a/sdk/ai/azure-ai-projects/azure/ai/projects/_client.py +++ b/sdk/ai/azure-ai-projects/azure/ai/projects/_client.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. @@ -16,7 +17,7 @@ from ._configuration import AIProjectClientConfiguration from ._serialization import Deserializer, Serializer -from .operations import AgentsOperations, ConnectionsOperations, EvaluationsOperations, TelemetryOperations +from .operations import ConnectionsOperations, EvaluationsOperations, TelemetryOperations if TYPE_CHECKING: from azure.core.credentials import TokenCredential @@ -25,8 +26,6 @@ class AIProjectClient: """AIProjectClient. - :ivar agents: AgentsOperations operations - :vartype agents: azure.ai.projects.operations.AgentsOperations :ivar connections: ConnectionsOperations operations :vartype connections: azure.ai.projects.operations.ConnectionsOperations :ivar telemetry: TelemetryOperations operations @@ -62,7 +61,7 @@ def __init__( credential: "TokenCredential", **kwargs: Any ) -> None: - _endpoint = "{endpoint}/agents/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{projectName}" # pylint: disable=line-too-long + _endpoint = "{endpoint}/agents/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{projectName}" self._config = AIProjectClientConfiguration( endpoint=endpoint, subscription_id=subscription_id, @@ -93,7 +92,6 @@ def __init__( self._serialize = Serializer() self._deserialize = Deserializer() self._serialize.client_side_validation = False - self.agents = AgentsOperations(self._client, self._config, self._serialize, self._deserialize) self.connections = ConnectionsOperations(self._client, self._config, self._serialize, self._deserialize) self.telemetry = TelemetryOperations(self._client, self._config, self._serialize, self._deserialize) self.evaluations = EvaluationsOperations(self._client, self._config, self._serialize, self._deserialize) diff --git a/sdk/ai/azure-ai-projects/azure/ai/projects/_patch.py b/sdk/ai/azure-ai-projects/azure/ai/projects/_patch.py index 9bc0729de4da..f7dd32510333 100644 --- a/sdk/ai/azure-ai-projects/azure/ai/projects/_patch.py +++ b/sdk/ai/azure-ai-projects/azure/ai/projects/_patch.py @@ -6,294 +6,9 @@ Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize """ -import uuid -from os import PathLike -from pathlib import Path -from typing import Any, Dict, List, Tuple, Union, Optional -from typing_extensions import Self +from typing import List -from azure.core import PipelineClient -from azure.core.credentials import TokenCredential -from azure.core.pipeline import policies - -from ._client import AIProjectClient as ClientGenerated -from ._configuration import AIProjectClientConfiguration -from ._serialization import Deserializer, Serializer -from .operations import AgentsOperations, ConnectionsOperations, EvaluationsOperations, TelemetryOperations -from .operations._patch import InferenceOperations - - -class AIProjectClient( - ClientGenerated -): # pylint: disable=client-accepts-api-version-keyword,too-many-instance-attributes - def __init__( # pylint: disable=super-init-not-called,too-many-statements - self, - endpoint: str, - subscription_id: str, - resource_group_name: str, - project_name: str, - credential: "TokenCredential", - **kwargs: Any, - ) -> None: - # TODO: Validate input formats with regex match (e.g. subscription ID) - if not endpoint: - raise ValueError("endpoint is required") - if not subscription_id: - raise ValueError("subscription_id ID is required") - if not resource_group_name: - raise ValueError("resource_group_name is required") - if not project_name: - raise ValueError("project_name is required") - if not credential: - raise ValueError("credential is required") - if "api_version" in kwargs: - raise ValueError("No support for overriding the API version") - if "credential_scopes" in kwargs: - raise ValueError("No support for overriding the credential scopes") - - kwargs0 = kwargs.copy() - kwargs1 = kwargs.copy() - kwargs2 = kwargs.copy() - kwargs3 = kwargs.copy() - - self._user_agent: Optional[str] = kwargs.get("user_agent", None) - - # For getting AppInsights connection string from the AppInsights resource. - # The AppInsights resource URL is not known at this point. We need to get it from the - # AzureML "Workspace - Get" REST API call. It will have the form: - # https://management.azure.com/subscriptions/{appinsights_subscription_id}/resourceGroups/{appinsights_resource_group_name}/providers/microsoft.insights/components/{appinsights_resource_name} - _endpoint0 = "https://management.azure.com" - self._config0: AIProjectClientConfiguration = AIProjectClientConfiguration( - endpoint=endpoint, - subscription_id=subscription_id, - resource_group_name=resource_group_name, - project_name=project_name, - credential=credential, - api_version="2020-02-02", - credential_scopes=["https://management.azure.com/.default"], - **kwargs0, - ) - - _policies0 = kwargs0.pop("policies", None) - if _policies0 is None: - _policies0 = [ - policies.RequestIdPolicy(**kwargs0), - self._config0.headers_policy, - self._config0.user_agent_policy, - self._config0.proxy_policy, - policies.ContentDecodePolicy(**kwargs0), - self._config0.redirect_policy, - self._config0.retry_policy, - self._config0.authentication_policy, - self._config0.custom_hook_policy, - self._config0.logging_policy, - policies.DistributedTracingPolicy(**kwargs0), - policies.SensitiveHeaderCleanupPolicy(**kwargs0) if self._config0.redirect_policy else None, - self._config0.http_logging_policy, - ] - self._client0: PipelineClient = PipelineClient(base_url=_endpoint0, policies=_policies0, **kwargs0) - - # For Endpoints operations (listing connections, getting connection properties, getting project properties) - _endpoint1 = ( - "https://management.azure.com/" - + f"subscriptions/{subscription_id}/" - + f"resourceGroups/{resource_group_name}/" - + "providers/Microsoft.MachineLearningServices/" - + f"workspaces/{project_name}" - ) - self._config1: AIProjectClientConfiguration = AIProjectClientConfiguration( - endpoint=endpoint, - subscription_id=subscription_id, - resource_group_name=resource_group_name, - project_name=project_name, - credential=credential, - api_version="2024-07-01-preview", - credential_scopes=["https://management.azure.com/.default"], - **kwargs1, - ) - _policies1 = kwargs1.pop("policies", None) - if _policies1 is None: - _policies1 = [ - policies.RequestIdPolicy(**kwargs1), - self._config1.headers_policy, - self._config1.user_agent_policy, - self._config1.proxy_policy, - policies.ContentDecodePolicy(**kwargs1), - self._config1.redirect_policy, - self._config1.retry_policy, - self._config1.authentication_policy, - self._config1.custom_hook_policy, - self._config1.logging_policy, - policies.DistributedTracingPolicy(**kwargs1), - policies.SensitiveHeaderCleanupPolicy(**kwargs1) if self._config1.redirect_policy else None, - self._config1.http_logging_policy, - ] - self._client1: PipelineClient = PipelineClient(base_url=_endpoint1, policies=_policies1, **kwargs1) - - # For Agents operations - _endpoint2 = f"{endpoint}/agents/v1.0/subscriptions/{subscription_id}/resourceGroups/{resource_group_name}/providers/Microsoft.MachineLearningServices/workspaces/{project_name}" # pylint: disable=line-too-long - self._config2 = AIProjectClientConfiguration( - endpoint=endpoint, - subscription_id=subscription_id, - resource_group_name=resource_group_name, - project_name=project_name, - credential=credential, - api_version="2024-12-01-preview", - credential_scopes=["https://ml.azure.com/.default"], - **kwargs2, - ) - _policies2 = kwargs2.pop("policies", None) - if _policies2 is None: - _policies2 = [ - policies.RequestIdPolicy(**kwargs2), - self._config2.headers_policy, - self._config2.user_agent_policy, - self._config2.proxy_policy, - policies.ContentDecodePolicy(**kwargs2), - self._config2.redirect_policy, - self._config2.retry_policy, - self._config2.authentication_policy, - self._config2.custom_hook_policy, - self._config2.logging_policy, - policies.DistributedTracingPolicy(**kwargs2), - policies.SensitiveHeaderCleanupPolicy(**kwargs2) if self._config2.redirect_policy else None, - self._config2.http_logging_policy, - ] - self._client2: PipelineClient = PipelineClient(base_url=_endpoint2, policies=_policies2, **kwargs2) - - # For Cloud Evaluations operations - # cSpell:disable-next-line - _endpoint3 = f"{endpoint}/raisvc/v1.0/subscriptions/{subscription_id}/resourceGroups/{resource_group_name}/providers/Microsoft.MachineLearningServices/workspaces/{project_name}" # pylint: disable=line-too-long - self._config3 = AIProjectClientConfiguration( - endpoint=endpoint, - subscription_id=subscription_id, - resource_group_name=resource_group_name, - project_name=project_name, - credential=credential, - api_version="2024-07-01-preview", # TODO: Update me - credential_scopes=["https://ml.azure.com/.default"], # TODO: Update once service changes are ready - **kwargs3, - ) - _policies3 = kwargs3.pop("policies", None) - if _policies3 is None: - _policies3 = [ - policies.RequestIdPolicy(**kwargs3), - self._config3.headers_policy, - self._config3.user_agent_policy, - self._config3.proxy_policy, - policies.ContentDecodePolicy(**kwargs3), - self._config3.redirect_policy, - self._config3.retry_policy, - self._config3.authentication_policy, - self._config3.custom_hook_policy, - self._config3.logging_policy, - policies.DistributedTracingPolicy(**kwargs3), - policies.SensitiveHeaderCleanupPolicy(**kwargs3) if self._config3.redirect_policy else None, - self._config3.http_logging_policy, - ] - self._client3: PipelineClient = PipelineClient(base_url=_endpoint3, policies=_policies3, **kwargs3) - - self._serialize = Serializer() - self._deserialize = Deserializer() - self._serialize.client_side_validation = False - - self.telemetry = TelemetryOperations( - self._client0, self._config0, self._serialize, self._deserialize, outer_instance=self - ) - self.connections = ConnectionsOperations(self._client1, self._config1, self._serialize, self._deserialize) - self.agents = AgentsOperations(self._client2, self._config2, self._serialize, self._deserialize) - self.evaluations = EvaluationsOperations(self._client3, self._config3, self._serialize, self._deserialize) - self.inference = InferenceOperations(self) - - def close(self) -> None: - self._client0.close() - self._client1.close() - self._client2.close() - self._client3.close() - - def __enter__(self) -> Self: - self._client0.__enter__() - self._client1.__enter__() - self._client2.__enter__() - self._client3.__enter__() - return self - - def __exit__(self, *exc_details: Any) -> None: - self._client0.__exit__(*exc_details) - self._client1.__exit__(*exc_details) - self._client2.__exit__(*exc_details) - self._client3.__exit__(*exc_details) - - @classmethod - def from_connection_string(cls, conn_str: str, credential: "TokenCredential", **kwargs) -> Self: - """ - Create an AIProjectClient from a connection string. - - :param str conn_str: The connection string, copied from your AI Foundry project. - :param TokenCredential credential: Credential used to authenticate requests to the service. - :return: An AIProjectClient instance. - :rtype: AIProjectClient - """ - if not conn_str: - raise ValueError("Connection string is required") - parts = conn_str.split(";") - if len(parts) != 4: - raise ValueError("Invalid connection string format") - endpoint = "https://" + parts[0] - subscription_id = parts[1] - resource_group_name = parts[2] - project_name = parts[3] - return cls(endpoint, subscription_id, resource_group_name, project_name, credential, **kwargs) - - def upload_file(self, file_path: Union[Path, str, PathLike]) -> Tuple[str, str]: - """Upload a file to the Azure AI Foundry project. - This method required *azure-ai-ml* to be installed. - - :param file_path: The path to the file to upload. - :type file_path: Union[str, Path, PathLike] - :return: The tuple, containing asset id and asset URI of uploaded file. - :rtype: Tuple[str] - """ - try: - from azure.ai.ml import MLClient # type: ignore - from azure.ai.ml.constants import AssetTypes # type: ignore - from azure.ai.ml.entities import Data # type: ignore - except ImportError as e: - raise ImportError( - "azure-ai-ml must be installed to use this function. Please install it using `pip install azure-ai-ml`" - ) from e - - data = Data( - path=str(file_path), - type=AssetTypes.URI_FILE, - name=str(uuid.uuid4()), # generating random name - is_anonymous=True, - version="1", - ) - - ml_client = MLClient( - self._config3.credential, - self._config3.subscription_id, - self._config3.resource_group_name, - self._config3.project_name, - ) - - data_asset = ml_client.data.create_or_update(data) - - return data_asset.id, data_asset.path - - @property - def scope(self) -> Dict[str, str]: - return { - "subscription_id": self._config3.subscription_id, - "resource_group_name": self._config3.resource_group_name, - "project_name": self._config3.project_name, - } - - -__all__: List[str] = [ - "AIProjectClient", -] # Add all objects you want publicly available to users at this package level +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level def patch_sdk(): diff --git a/sdk/ai/azure-ai-projects/azure/ai/projects/_serialization.py b/sdk/ai/azure-ai-projects/azure/ai/projects/_serialization.py index a066e16a64dd..e2a20b1d534c 100644 --- a/sdk/ai/azure-ai-projects/azure/ai/projects/_serialization.py +++ b/sdk/ai/azure-ai-projects/azure/ai/projects/_serialization.py @@ -1,4 +1,4 @@ -# pylint: disable=too-many-lines +# pylint: disable=line-too-long,useless-suppression,too-many-lines # -------------------------------------------------------------------------- # # Copyright (c) Microsoft Corporation. All rights reserved. @@ -1361,7 +1361,7 @@ def xml_key_extractor(attr, attr_desc, data): # pylint: disable=unused-argument # 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 + "Tried to deserialize an array not wrapped, and found several nodes '{}'. Maybe you should declare this array as wrapped?".format( xml_name ) ) diff --git a/sdk/ai/azure-ai-projects/azure/ai/projects/_types.py b/sdk/ai/azure-ai-projects/azure/ai/projects/_types.py deleted file mode 100644 index 1c059e5809cc..000000000000 --- a/sdk/ai/azure-ai-projects/azure/ai/projects/_types.py +++ /dev/null @@ -1,21 +0,0 @@ -# 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 TYPE_CHECKING, Union - -if TYPE_CHECKING: - from . import models as _models -AgentsApiResponseFormatOption = Union[ - str, - str, - "_models.AgentsApiResponseFormatMode", - "_models.AgentsApiResponseFormat", - "_models.ResponseFormatJsonSchemaType", -] -MessageAttachmentToolDefinition = Union["_models.CodeInterpreterToolDefinition", "_models.FileSearchToolDefinition"] -AgentsApiToolChoiceOption = Union[str, str, "_models.AgentsApiToolChoiceOptionMode", "_models.AgentsNamedToolChoice"] diff --git a/sdk/ai/azure-ai-projects/azure/ai/projects/_vendor.py b/sdk/ai/azure-ai-projects/azure/ai/projects/_vendor.py deleted file mode 100644 index e6f010934827..000000000000 --- a/sdk/ai/azure-ai-projects/azure/ai/projects/_vendor.py +++ /dev/null @@ -1,50 +0,0 @@ -# -------------------------------------------------------------------------- -# 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 json -from typing import Any, Dict, IO, List, Mapping, Optional, Tuple, Union - -from ._model_base import Model, SdkJSONEncoder - - -# 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-projects/azure/ai/projects/_version.py b/sdk/ai/azure-ai-projects/azure/ai/projects/_version.py index d17ec8abfb6f..be71c81bd282 100644 --- a/sdk/ai/azure-ai-projects/azure/ai/projects/_version.py +++ b/sdk/ai/azure-ai-projects/azure/ai/projects/_version.py @@ -6,4 +6,4 @@ # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- -VERSION = "1.0.0b6" +VERSION = "1.0.0b1" diff --git a/sdk/ai/azure-ai-projects/azure/ai/projects/aio/_client.py b/sdk/ai/azure-ai-projects/azure/ai/projects/aio/_client.py index a02ebf54047f..bac7834508f2 100644 --- a/sdk/ai/azure-ai-projects/azure/ai/projects/aio/_client.py +++ b/sdk/ai/azure-ai-projects/azure/ai/projects/aio/_client.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. @@ -16,7 +17,7 @@ from .._serialization import Deserializer, Serializer from ._configuration import AIProjectClientConfiguration -from .operations import AgentsOperations, ConnectionsOperations, EvaluationsOperations, TelemetryOperations +from .operations import ConnectionsOperations, EvaluationsOperations, TelemetryOperations if TYPE_CHECKING: from azure.core.credentials_async import AsyncTokenCredential @@ -25,8 +26,6 @@ class AIProjectClient: """AIProjectClient. - :ivar agents: AgentsOperations operations - :vartype agents: azure.ai.projects.aio.operations.AgentsOperations :ivar connections: ConnectionsOperations operations :vartype connections: azure.ai.projects.aio.operations.ConnectionsOperations :ivar telemetry: TelemetryOperations operations @@ -62,7 +61,7 @@ def __init__( credential: "AsyncTokenCredential", **kwargs: Any ) -> None: - _endpoint = "{endpoint}/agents/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{projectName}" # pylint: disable=line-too-long + _endpoint = "{endpoint}/agents/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{projectName}" self._config = AIProjectClientConfiguration( endpoint=endpoint, subscription_id=subscription_id, @@ -93,7 +92,6 @@ def __init__( self._serialize = Serializer() self._deserialize = Deserializer() self._serialize.client_side_validation = False - self.agents = AgentsOperations(self._client, self._config, self._serialize, self._deserialize) self.connections = ConnectionsOperations(self._client, self._config, self._serialize, self._deserialize) self.telemetry = TelemetryOperations(self._client, self._config, self._serialize, self._deserialize) self.evaluations = EvaluationsOperations(self._client, self._config, self._serialize, self._deserialize) diff --git a/sdk/ai/azure-ai-projects/azure/ai/projects/aio/_patch.py b/sdk/ai/azure-ai-projects/azure/ai/projects/aio/_patch.py index 61b035c5bec7..f7dd32510333 100644 --- a/sdk/ai/azure-ai-projects/azure/ai/projects/aio/_patch.py +++ b/sdk/ai/azure-ai-projects/azure/ai/projects/aio/_patch.py @@ -6,309 +6,9 @@ Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize """ -import uuid -from os import PathLike -from pathlib import Path -from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union -from typing_extensions import Self +from typing import List -from azure.core import AsyncPipelineClient -from azure.core.pipeline import policies - -from .._serialization import Deserializer, Serializer -from ._client import AIProjectClient as ClientGenerated -from ._configuration import AIProjectClientConfiguration -from .operations import ( - AgentsOperations, - ConnectionsOperations, - EvaluationsOperations, - TelemetryOperations, -) -from .operations._patch import _SyncCredentialWrapper, InferenceOperations - -if TYPE_CHECKING: - from azure.core.credentials import AccessToken - from azure.core.credentials_async import AsyncTokenCredential - - -class AIProjectClient( - ClientGenerated -): # pylint: disable=client-accepts-api-version-keyword,too-many-instance-attributes - def __init__( # pylint: disable=super-init-not-called,too-many-statements - self, - endpoint: str, - subscription_id: str, - resource_group_name: str, - project_name: str, - credential: "AsyncTokenCredential", - **kwargs: Any, - ) -> None: - # TODO: Validate input formats with regex match (e.g. subscription ID) - if not endpoint: - raise ValueError("endpoint is required") - if not subscription_id: - raise ValueError("subscription_id ID is required") - if not resource_group_name: - raise ValueError("resource_group_name is required") - if not project_name: - raise ValueError("project_name is required") - if not credential: - raise ValueError("credential is required") - if "api_version" in kwargs: - raise ValueError("No support for overriding the API version") - if "credential_scopes" in kwargs: - raise ValueError("No support for overriding the credential scopes") - - kwargs0 = kwargs.copy() - kwargs1 = kwargs.copy() - kwargs2 = kwargs.copy() - kwargs3 = kwargs.copy() - - self._user_agent: Optional[str] = kwargs.get("user_agent", None) - - # For getting AppInsights connection string from the AppInsights resource. - # The AppInsights resource URL is not known at this point. We need to get it from the - # AzureML "Workspace - Get" REST API call. It will have the form: - # https://management.azure.com/subscriptions/{appinsights_subscription_id}/resourceGroups/{appinsights_resource_group_name}/providers/microsoft.insights/components/{appinsights_resource_name} # pylint: disable=line-too-long - _endpoint0 = "https://management.azure.com" # pylint: disable=line-too-long - self._config0: AIProjectClientConfiguration = AIProjectClientConfiguration( - endpoint=endpoint, - subscription_id=subscription_id, - resource_group_name=resource_group_name, - project_name=project_name, - credential=credential, - api_version="2020-02-02", - credential_scopes=["https://management.azure.com/.default"], - **kwargs0, - ) - - _policies0 = kwargs0.pop("policies", None) - if _policies0 is None: - _policies0 = [ - policies.RequestIdPolicy(**kwargs0), - self._config0.headers_policy, - self._config0.user_agent_policy, - self._config0.proxy_policy, - policies.ContentDecodePolicy(**kwargs0), - self._config0.redirect_policy, - self._config0.retry_policy, - self._config0.authentication_policy, - self._config0.custom_hook_policy, - self._config0.logging_policy, - policies.DistributedTracingPolicy(**kwargs0), - (policies.SensitiveHeaderCleanupPolicy(**kwargs0) if self._config0.redirect_policy else None), - self._config0.http_logging_policy, - ] - self._client0: AsyncPipelineClient = AsyncPipelineClient(base_url=_endpoint0, policies=_policies0, **kwargs0) - - # For Endpoints operations (enumerating connections, getting SAS tokens) - _endpoint1 = f"https://management.azure.com/subscriptions/{subscription_id}/resourceGroups/{resource_group_name}/providers/Microsoft.MachineLearningServices/workspaces/{project_name}" - self._config1: AIProjectClientConfiguration = AIProjectClientConfiguration( - endpoint=endpoint, - subscription_id=subscription_id, - resource_group_name=resource_group_name, - project_name=project_name, - credential=credential, - api_version="2024-07-01-preview", - credential_scopes=["https://management.azure.com/.default"], - **kwargs1, - ) - _policies1 = kwargs1.pop("policies", None) - if _policies1 is None: - _policies1 = [ - policies.RequestIdPolicy(**kwargs1), - self._config1.headers_policy, - self._config1.user_agent_policy, - self._config1.proxy_policy, - policies.ContentDecodePolicy(**kwargs1), - self._config1.redirect_policy, - self._config1.retry_policy, - self._config1.authentication_policy, - self._config1.custom_hook_policy, - self._config1.logging_policy, - policies.DistributedTracingPolicy(**kwargs1), - (policies.SensitiveHeaderCleanupPolicy(**kwargs1) if self._config1.redirect_policy else None), - self._config1.http_logging_policy, - ] - self._client1: AsyncPipelineClient = AsyncPipelineClient(base_url=_endpoint1, policies=_policies1, **kwargs1) - - # For Agents operations - _endpoint2 = f"{endpoint}/agents/v1.0/subscriptions/{subscription_id}/resourceGroups/{resource_group_name}/providers/Microsoft.MachineLearningServices/workspaces/{project_name}" # pylint: disable=line-too-long - self._config2: AIProjectClientConfiguration = AIProjectClientConfiguration( - endpoint=endpoint, - subscription_id=subscription_id, - resource_group_name=resource_group_name, - project_name=project_name, - credential=credential, - api_version="2024-12-01-preview", - credential_scopes=["https://ml.azure.com/.default"], - **kwargs2, - ) - _policies2 = kwargs2.pop("policies", None) - if _policies2 is None: - _policies2 = [ - policies.RequestIdPolicy(**kwargs2), - self._config2.headers_policy, - self._config2.user_agent_policy, - self._config2.proxy_policy, - policies.ContentDecodePolicy(**kwargs2), - self._config2.redirect_policy, - self._config2.retry_policy, - self._config2.authentication_policy, - self._config2.custom_hook_policy, - self._config2.logging_policy, - policies.DistributedTracingPolicy(**kwargs2), - (policies.SensitiveHeaderCleanupPolicy(**kwargs2) if self._config2.redirect_policy else None), - self._config2.http_logging_policy, - ] - self._client2: AsyncPipelineClient = AsyncPipelineClient(base_url=_endpoint2, policies=_policies2, **kwargs2) - - # For Cloud Evaluations operations - # cSpell:disable-next-line - _endpoint3 = f"{endpoint}/raisvc/v1.0/subscriptions/{subscription_id}/resourceGroups/{resource_group_name}/providers/Microsoft.MachineLearningServices/workspaces/{project_name}" # pylint: disable=line-too-long - self._config3: AIProjectClientConfiguration = AIProjectClientConfiguration( - endpoint=endpoint, - subscription_id=subscription_id, - resource_group_name=resource_group_name, - project_name=project_name, - credential=credential, - api_version="2024-07-01-preview", # TODO: Update me - credential_scopes=["https://ml.azure.com/.default"], # TODO: Update once service changes are ready - **kwargs3, - ) - _policies3 = kwargs3.pop("policies", None) - if _policies3 is None: - _policies3 = [ - policies.RequestIdPolicy(**kwargs3), - self._config3.headers_policy, - self._config3.user_agent_policy, - self._config3.proxy_policy, - policies.ContentDecodePolicy(**kwargs3), - self._config3.redirect_policy, - self._config3.retry_policy, - self._config3.authentication_policy, - self._config3.custom_hook_policy, - self._config3.logging_policy, - policies.DistributedTracingPolicy(**kwargs3), - (policies.SensitiveHeaderCleanupPolicy(**kwargs3) if self._config3.redirect_policy else None), - self._config3.http_logging_policy, - ] - self._client3: AsyncPipelineClient = AsyncPipelineClient(base_url=_endpoint3, policies=_policies3, **kwargs3) - - self._serialize = Serializer() - self._deserialize = Deserializer() - self._serialize.client_side_validation = False - - self.telemetry = TelemetryOperations( - self._client0, - self._config0, - self._serialize, - self._deserialize, - outer_instance=self, - ) - self._credential = credential - self.connections = ConnectionsOperations(self._client1, self._config1, self._serialize, self._deserialize) - self.agents = AgentsOperations(self._client2, self._config2, self._serialize, self._deserialize) - self.evaluations = EvaluationsOperations(self._client3, self._config3, self._serialize, self._deserialize) - self.inference = InferenceOperations(self) - - async def close(self) -> None: - await self._client0.close() - await self._client1.close() - await self._client2.close() - await self._client3.close() - - async def __aenter__(self) -> Self: - await self._client0.__aenter__() - await self._client1.__aenter__() - await self._client2.__aenter__() - await self._client3.__aenter__() - return self - - async def __aexit__(self, *exc_details: Any) -> None: - await self._client0.__aexit__(*exc_details) - await self._client1.__aexit__(*exc_details) - await self._client2.__aexit__(*exc_details) - await self._client3.__aexit__(*exc_details) - - @classmethod - def from_connection_string(cls, conn_str: str, credential: "AsyncTokenCredential", **kwargs) -> Self: - """ - Create an asynchronous AIProjectClient from a connection string. - - :param str conn_str: The connection string, copied from your AI Foundry project. - :param AsyncTokenCredential credential: Credential used to authenticate requests to the service. - :return: An AIProjectClient instance. - :rtype: AIProjectClient - """ - if not conn_str: - raise ValueError("Connection string is required") - parts = conn_str.split(";") - if len(parts) != 4: - raise ValueError("Invalid connection string format") - endpoint = "https://" + parts[0] - subscription_id = parts[1] - resource_group_name = parts[2] - project_name = parts[3] - return cls( - endpoint, - subscription_id, - resource_group_name, - project_name, - credential, - **kwargs, - ) - - def upload_file(self, file_path: Union[Path, str, PathLike]) -> Tuple[str, str]: - """Upload a file to the Azure AI Foundry project. - This method required *azure-ai-ml* to be installed. - - :param file_path: The path to the file to upload. - :type file_path: Union[str, Path, PathLike] - :return: The tuple, containing asset id and asset URI of uploaded file. - :rtype: Tuple[str, str] - """ - try: - from azure.ai.ml import MLClient # type: ignore - from azure.ai.ml.constants import AssetTypes # type: ignore - from azure.ai.ml.entities import Data # type: ignore - except ImportError as e: - raise ImportError( - "azure-ai-ml must be installed to use this function. Please install it using `pip install azure-ai-ml`" - ) from e - - data = Data( - path=str(file_path), - type=AssetTypes.URI_FILE, - name=str(uuid.uuid4()), # generating random name - is_anonymous=True, - version="1", - ) - # We have to wrap async method get_token of - - ml_client = MLClient( - _SyncCredentialWrapper(self._config3.credential), - self._config3.subscription_id, - self._config3.resource_group_name, - self._config3.project_name, - ) - - data_asset = ml_client.data.create_or_update(data) - - return data_asset.id, data_asset.path - - @property - def scope(self) -> Dict[str, str]: - return { - "subscription_id": self._config3.subscription_id, - "resource_group_name": self._config3.resource_group_name, - "project_name": self._config3.project_name, - } - - -__all__: List[str] = [ - "AIProjectClient", -] # Add all objects you want publicly available to users at this package level +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level def patch_sdk(): diff --git a/sdk/ai/azure-ai-projects/azure/ai/projects/aio/operations/__init__.py b/sdk/ai/azure-ai-projects/azure/ai/projects/aio/operations/__init__.py index 64c4031e2bb6..f1eb231e4404 100644 --- a/sdk/ai/azure-ai-projects/azure/ai/projects/aio/operations/__init__.py +++ b/sdk/ai/azure-ai-projects/azure/ai/projects/aio/operations/__init__.py @@ -12,7 +12,6 @@ if TYPE_CHECKING: from ._patch import * # pylint: disable=unused-wildcard-import -from ._operations import AgentsOperations # type: ignore from ._operations import ConnectionsOperations # type: ignore from ._operations import TelemetryOperations # type: ignore from ._operations import EvaluationsOperations # type: ignore @@ -22,7 +21,6 @@ from ._patch import patch_sdk as _patch_sdk __all__ = [ - "AgentsOperations", "ConnectionsOperations", "TelemetryOperations", "EvaluationsOperations", diff --git a/sdk/ai/azure-ai-projects/azure/ai/projects/aio/operations/_operations.py b/sdk/ai/azure-ai-projects/azure/ai/projects/aio/operations/_operations.py index 1e990007a1f0..feb9829e07a5 100644 --- a/sdk/ai/azure-ai-projects/azure/ai/projects/aio/operations/_operations.py +++ b/sdk/ai/azure-ai-projects/azure/ai/projects/aio/operations/_operations.py @@ -1,4 +1,4 @@ -# pylint: disable=too-many-lines +# pylint: disable=line-too-long,useless-suppression,too-many-lines # coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. @@ -9,7 +9,7 @@ from io import IOBase import json import sys -from typing import Any, AsyncIterable, Callable, Dict, IO, List, Optional, TYPE_CHECKING, TypeVar, Union, overload +from typing import Any, AsyncIterable, Callable, Dict, IO, List, Optional, TypeVar, Union, overload import urllib.parse from azure.core import AsyncPipelineClient @@ -30,51 +30,10 @@ 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 ... import models as _models from ..._model_base import SdkJSONEncoder, _deserialize from ..._serialization import Deserializer, Serializer -from ..._vendor import FileType, prepare_multipart_form_data from ...operations._operations import ( - build_agents_cancel_run_request, - build_agents_cancel_vector_store_file_batch_request, - build_agents_create_agent_request, - build_agents_create_message_request, - build_agents_create_run_request, - build_agents_create_thread_and_run_request, - build_agents_create_thread_request, - build_agents_create_vector_store_file_batch_request, - build_agents_create_vector_store_file_request, - build_agents_create_vector_store_request, - build_agents_delete_agent_request, - build_agents_delete_file_request, - build_agents_delete_thread_request, - build_agents_delete_vector_store_file_request, - build_agents_delete_vector_store_request, - build_agents_get_agent_request, - build_agents_get_file_content_request, - build_agents_get_file_request, - build_agents_get_message_request, - build_agents_get_run_request, - build_agents_get_run_step_request, - build_agents_get_thread_request, - build_agents_get_vector_store_file_batch_request, - build_agents_get_vector_store_file_request, - build_agents_get_vector_store_request, - build_agents_list_agents_request, - build_agents_list_files_request, - build_agents_list_messages_request, - build_agents_list_run_steps_request, - build_agents_list_runs_request, - build_agents_list_vector_store_file_batch_files_request, - build_agents_list_vector_store_files_request, - build_agents_list_vector_stores_request, - build_agents_modify_vector_store_request, - build_agents_submit_tool_outputs_to_run_request, - build_agents_update_agent_request, - build_agents_update_message_request, - build_agents_update_run_request, - build_agents_update_thread_request, - build_agents_upload_file_request, build_connections_get_connection_request, build_connections_get_connection_with_secrets_request, build_connections_get_workspace_request, @@ -95,4960 +54,10 @@ from collections.abc import MutableMapping else: from typing import MutableMapping # type: ignore - -if TYPE_CHECKING: - from ... import _types -JSON = MutableMapping[str, Any] # pylint: disable=unsubscriptable-object -_Unset: Any = object() -T = TypeVar("T") -ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] - - -class AgentsOperations: # pylint: disable=too-many-public-methods - """ - .. warning:: - **DO NOT** instantiate this class directly. - - Instead, you should access the following operations through - :class:`~azure.ai.projects.aio.AIProjectClient`'s - :attr:`agents` attribute. - """ - - def __init__(self, *args, **kwargs) -> None: - input_args = list(args) - self._client: AsyncPipelineClient = input_args.pop(0) if input_args else kwargs.pop("client") - self._config: AIProjectClientConfiguration = input_args.pop(0) if input_args else kwargs.pop("config") - self._serialize: Serializer = input_args.pop(0) if input_args else kwargs.pop("serializer") - self._deserialize: Deserializer = input_args.pop(0) if input_args else kwargs.pop("deserializer") - - @overload - async def create_agent( - self, - *, - model: str, - content_type: str = "application/json", - name: Optional[str] = None, - description: Optional[str] = None, - instructions: Optional[str] = None, - tools: Optional[List[_models.ToolDefinition]] = None, - tool_resources: Optional[_models.ToolResources] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.Agent: - """Creates a new agent. - - :keyword model: The ID of the model to use. Required. - :paramtype model: str - :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. - Default value is "application/json". - :paramtype content_type: str - :keyword name: The name of the new agent. Default value is None. - :paramtype name: str - :keyword description: The description of the new agent. Default value is None. - :paramtype description: str - :keyword instructions: The system instructions for the new agent to use. Default value is None. - :paramtype instructions: str - :keyword tools: The collection of tools to enable for the new agent. Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword tool_resources: A set of resources that are used by the agent'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. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.ToolResources - :keyword 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. Default value is - None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword response_format: The response format of the tool calls used by this agent. Is one of - the following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat or - ~azure.ai.projects.models.ResponseFormatJsonSchemaType - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def create_agent(self, body: JSON, *, content_type: str = "application/json", **kwargs: Any) -> _models.Agent: - """Creates a new agent. - - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def create_agent( - self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any - ) -> _models.Agent: - """Creates a new agent. - - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace_async - async def create_agent( - self, - body: Union[JSON, IO[bytes]] = _Unset, - *, - model: str = _Unset, - name: Optional[str] = None, - description: Optional[str] = None, - instructions: Optional[str] = None, - tools: Optional[List[_models.ToolDefinition]] = None, - tool_resources: Optional[_models.ToolResources] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.Agent: - """Creates a new agent. - - :param body: Is either a JSON type or a IO[bytes] type. Required. - :type body: JSON or IO[bytes] - :keyword model: The ID of the model to use. Required. - :paramtype model: str - :keyword name: The name of the new agent. Default value is None. - :paramtype name: str - :keyword description: The description of the new agent. Default value is None. - :paramtype description: str - :keyword instructions: The system instructions for the new agent to use. Default value is None. - :paramtype instructions: str - :keyword tools: The collection of tools to enable for the new agent. Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword tool_resources: A set of resources that are used by the agent'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. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.ToolResources - :keyword 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. Default value is - None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword response_format: The response format of the tool calls used by this agent. Is one of - the following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat or - ~azure.ai.projects.models.ResponseFormatJsonSchemaType - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :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.Agent] = kwargs.pop("cls", None) - - if body is _Unset: - if model is _Unset: - raise TypeError("missing required argument: model") - body = { - "description": description, - "instructions": instructions, - "metadata": metadata, - "model": model, - "name": name, - "response_format": response_format, - "temperature": temperature, - "tool_resources": tool_resources, - "tools": tools, - "top_p": top_p, - } - 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_agents_create_agent_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.Agent, response.json()) - - if cls: - return cls(pipeline_response, deserialized, {}) # type: ignore - - return deserialized # type: ignore - - @distributed_trace_async - async def list_agents( - self, - *, - limit: Optional[int] = None, - order: Optional[Union[str, _models.ListSortOrder]] = None, - after: Optional[str] = None, - before: Optional[str] = None, - **kwargs: Any - ) -> _models.OpenAIPageableListOfAgent: - """Gets a list of agents 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.projects.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: OpenAIPageableListOfAgent. The OpenAIPageableListOfAgent is compatible with - MutableMapping - :rtype: ~azure.ai.projects.models.OpenAIPageableListOfAgent - :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.OpenAIPageableListOfAgent] = kwargs.pop("cls", None) - - _request = build_agents_list_agents_request( - limit=limit, - order=order, - after=after, - before=before, - api_version=self._config.api_version, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.OpenAIPageableListOfAgent, response.json()) - - if cls: - return cls(pipeline_response, deserialized, {}) # type: ignore - - return deserialized # type: ignore - - @distributed_trace_async - async def get_agent(self, assistant_id: str, **kwargs: Any) -> _models.Agent: - """Retrieves an existing agent. - - :param assistant_id: Identifier of the agent. Required. - :type assistant_id: str - :return: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :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.Agent] = kwargs.pop("cls", None) - - _request = build_agents_get_agent_request( - assistant_id=assistant_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.Agent, response.json()) - - if cls: - return cls(pipeline_response, deserialized, {}) # type: ignore - - return deserialized # type: ignore - - @overload - async def update_agent( - self, - assistant_id: str, - *, - content_type: str = "application/json", - 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.ToolResources] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.Agent: - """Modifies an existing agent. - - :param assistant_id: The ID of the agent to modify. Required. - :type assistant_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 model: The ID of the model to use. Default value is None. - :paramtype model: str - :keyword name: The modified name for the agent to use. Default value is None. - :paramtype name: str - :keyword description: The modified description for the agent to use. Default value is None. - :paramtype description: str - :keyword instructions: The modified system instructions for the new agent to use. Default value - is None. - :paramtype instructions: str - :keyword tools: The modified collection of tools to enable for the agent. Default value is - None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword tool_resources: A set of resources that are used by the agent'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. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.ToolResources - :keyword 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. Default value is - None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword response_format: The response format of the tool calls used by this agent. Is one of - the following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat or - ~azure.ai.projects.models.ResponseFormatJsonSchemaType - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def update_agent( - self, assistant_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any - ) -> _models.Agent: - """Modifies an existing agent. - - :param assistant_id: The ID of the agent to modify. Required. - :type assistant_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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def update_agent( - self, assistant_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any - ) -> _models.Agent: - """Modifies an existing agent. - - :param assistant_id: The ID of the agent to modify. Required. - :type assistant_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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace_async - async def update_agent( - self, - assistant_id: str, - body: Union[JSON, IO[bytes]] = _Unset, - *, - 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.ToolResources] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.Agent: - """Modifies an existing agent. - - :param assistant_id: The ID of the agent to modify. Required. - :type assistant_id: str - :param body: Is either a JSON type or a IO[bytes] type. Required. - :type body: JSON or IO[bytes] - :keyword model: The ID of the model to use. Default value is None. - :paramtype model: str - :keyword name: The modified name for the agent to use. Default value is None. - :paramtype name: str - :keyword description: The modified description for the agent to use. Default value is None. - :paramtype description: str - :keyword instructions: The modified system instructions for the new agent to use. Default value - is None. - :paramtype instructions: str - :keyword tools: The modified collection of tools to enable for the agent. Default value is - None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword tool_resources: A set of resources that are used by the agent'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. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.ToolResources - :keyword 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. Default value is - None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword response_format: The response format of the tool calls used by this agent. Is one of - the following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat or - ~azure.ai.projects.models.ResponseFormatJsonSchemaType - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :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.Agent] = kwargs.pop("cls", None) - - if body is _Unset: - body = { - "description": description, - "instructions": instructions, - "metadata": metadata, - "model": model, - "name": name, - "response_format": response_format, - "temperature": temperature, - "tool_resources": tool_resources, - "tools": tools, - "top_p": top_p, - } - 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_agents_update_agent_request( - assistant_id=assistant_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.Agent, response.json()) - - if cls: - return cls(pipeline_response, deserialized, {}) # type: ignore - - return deserialized # type: ignore - - @distributed_trace_async - async def delete_agent(self, assistant_id: str, **kwargs: Any) -> _models.AgentDeletionStatus: - """Deletes an agent. - - :param assistant_id: Identifier of the agent. Required. - :type assistant_id: str - :return: AgentDeletionStatus. The AgentDeletionStatus is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.AgentDeletionStatus - :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.AgentDeletionStatus] = kwargs.pop("cls", None) - - _request = build_agents_delete_agent_request( - assistant_id=assistant_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.AgentDeletionStatus, response.json()) - - if cls: - return cls(pipeline_response, deserialized, {}) # type: ignore - - return deserialized # type: ignore - - @overload - async def create_thread( - self, - *, - content_type: str = "application/json", - messages: Optional[List[_models.ThreadMessageOptions]] = None, - tool_resources: Optional[_models.ToolResources] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.AgentThread: - """Creates a new thread. Threads contain messages and can be run by agents. - - :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. - Default value is "application/json". - :paramtype content_type: str - :keyword messages: The initial messages to associate with the new thread. Default value is - None. - :paramtype messages: list[~azure.ai.projects.models.ThreadMessageOptions] - :keyword tool_resources: A set of resources that are made available to the agent'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. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.ToolResources - :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: AgentThread. The AgentThread is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.AgentThread - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def create_thread( - self, body: JSON, *, content_type: str = "application/json", **kwargs: Any - ) -> _models.AgentThread: - """Creates a new thread. Threads contain messages and can be run by agents. - - :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: AgentThread. The AgentThread is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.AgentThread - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def create_thread( - self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any - ) -> _models.AgentThread: - """Creates a new thread. Threads contain messages and can be run by agents. - - :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: AgentThread. The AgentThread is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.AgentThread - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace_async - async def create_thread( - self, - body: Union[JSON, IO[bytes]] = _Unset, - *, - messages: Optional[List[_models.ThreadMessageOptions]] = None, - tool_resources: Optional[_models.ToolResources] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.AgentThread: - """Creates a new thread. Threads contain messages and can be run by agents. - - :param body: Is either a JSON type or a IO[bytes] type. Required. - :type body: JSON or IO[bytes] - :keyword messages: The initial messages to associate with the new thread. Default value is - None. - :paramtype messages: list[~azure.ai.projects.models.ThreadMessageOptions] - :keyword tool_resources: A set of resources that are made available to the agent'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. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.ToolResources - :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: AgentThread. The AgentThread is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.AgentThread - :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.AgentThread] = kwargs.pop("cls", None) - - if body is _Unset: - body = {"messages": messages, "metadata": metadata, "tool_resources": tool_resources} - 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_agents_create_thread_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.AgentThread, 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.AgentThread: - """Gets information about an existing thread. - - :param thread_id: Identifier of the thread. Required. - :type thread_id: str - :return: AgentThread. The AgentThread is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.AgentThread - :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.AgentThread] = kwargs.pop("cls", None) - - _request = build_agents_get_thread_request( - thread_id=thread_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.AgentThread, response.json()) - - if cls: - return cls(pipeline_response, deserialized, {}) # type: ignore - - return deserialized # type: ignore - - @overload - async def update_thread( - self, - thread_id: str, - *, - content_type: str = "application/json", - tool_resources: Optional[_models.ToolResources] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.AgentThread: - """Modifies an existing thread. - - :param thread_id: The ID of the thread to modify. Required. - :type thread_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 tool_resources: A set of resources that are made available to the agent'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. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.ToolResources - :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: AgentThread. The AgentThread is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.AgentThread - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def update_thread( - self, thread_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any - ) -> _models.AgentThread: - """Modifies an existing thread. - - :param thread_id: The ID of the thread to modify. Required. - :type thread_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: AgentThread. The AgentThread is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.AgentThread - :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.AgentThread: - """Modifies an existing thread. - - :param thread_id: The ID of the thread to modify. Required. - :type thread_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: AgentThread. The AgentThread is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.AgentThread - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace_async - async def update_thread( - self, - thread_id: str, - body: Union[JSON, IO[bytes]] = _Unset, - *, - tool_resources: Optional[_models.ToolResources] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.AgentThread: - """Modifies an existing thread. - - :param thread_id: The ID of the thread to modify. Required. - :type thread_id: str - :param body: Is either a JSON type or a IO[bytes] type. Required. - :type body: JSON or IO[bytes] - :keyword tool_resources: A set of resources that are made available to the agent'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. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.ToolResources - :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: AgentThread. The AgentThread is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.AgentThread - :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.AgentThread] = kwargs.pop("cls", None) - - if body is _Unset: - body = {"metadata": metadata, "tool_resources": tool_resources} - 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_agents_update_thread_request( - thread_id=thread_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.AgentThread, 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: Identifier of the thread. Required. - :type thread_id: str - :return: ThreadDeletionStatus. The ThreadDeletionStatus is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_delete_thread_request( - thread_id=thread_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.ThreadDeletionStatus, response.json()) - - if cls: - return cls(pipeline_response, deserialized, {}) # type: ignore - - return deserialized # type: ignore - - @overload - async def create_message( - self, - thread_id: str, - *, - role: Union[str, _models.MessageRole], - content: str, - content_type: str = "application/json", - attachments: Optional[List[_models.MessageAttachment]] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.ThreadMessage: - """Creates a new message on a specified thread. - - :param thread_id: Identifier of the thread. Required. - :type thread_id: str - :keyword 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 agent. Use this value to insert - messages from the agent into the - conversation. Known values are: "user" and "assistant". Required. - :paramtype role: str or ~azure.ai.projects.models.MessageRole - :keyword 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. - :paramtype content: str - :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. - Default value is "application/json". - :paramtype content_type: str - :keyword attachments: A list of files attached to the message, and the tools they should be - added to. Default value is None. - :paramtype attachments: list[~azure.ai.projects.models.MessageAttachment] - :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.projects.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: Identifier of the thread. Required. - :type thread_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.projects.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: Identifier of the thread. Required. - :type thread_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.projects.models.ThreadMessage - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace_async - async def create_message( - self, - thread_id: str, - body: Union[JSON, IO[bytes]] = _Unset, - *, - role: Union[str, _models.MessageRole] = _Unset, - content: str = _Unset, - attachments: Optional[List[_models.MessageAttachment]] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.ThreadMessage: - """Creates a new message on a specified thread. - - :param thread_id: Identifier of the thread. Required. - :type thread_id: str - :param body: Is either a JSON type or a IO[bytes] type. Required. - :type body: JSON or IO[bytes] - :keyword 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 agent. Use this value to insert - messages from the agent into the - conversation. Known values are: "user" and "assistant". Required. - :paramtype role: str or ~azure.ai.projects.models.MessageRole - :keyword 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. - :paramtype content: str - :keyword attachments: A list of files attached to the message, and the tools they should be - added to. Default value is None. - :paramtype attachments: list[~azure.ai.projects.models.MessageAttachment] - :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.projects.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: - if role is _Unset: - raise TypeError("missing required argument: role") - if content is _Unset: - raise TypeError("missing required argument: content") - body = {"attachments": attachments, "content": content, "metadata": metadata, "role": role} - 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_agents_create_message_request( - thread_id=thread_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.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: Identifier of the thread. 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.projects.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.projects.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_agents_list_messages_request( - thread_id=thread_id, - run_id=run_id, - limit=limit, - order=order, - after=after, - before=before, - api_version=self._config.api_version, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.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: Identifier of the thread. Required. - :type thread_id: str - :param message_id: Identifier of the message. Required. - :type message_id: str - :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_get_message_request( - thread_id=thread_id, - message_id=message_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.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, - *, - 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: Identifier of the thread. Required. - :type thread_id: str - :param message_id: Identifier of the message. 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.projects.models.ThreadMessage - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @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: Identifier of the thread. Required. - :type thread_id: str - :param message_id: Identifier of the message. 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.projects.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: Identifier of the thread. Required. - :type thread_id: str - :param message_id: Identifier of the message. 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.projects.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: Identifier of the thread. Required. - :type thread_id: str - :param message_id: Identifier of the message. 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.projects.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_agents_update_message_request( - thread_id=thread_id, - message_id=message_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.ThreadMessage, response.json()) - - if cls: - return cls(pipeline_response, deserialized, {}) # type: ignore - - return deserialized # type: ignore - - @overload - async def create_run( - self, - thread_id: str, - *, - assistant_id: str, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - content_type: str = "application/json", - model: Optional[str] = None, - instructions: Optional[str] = None, - additional_instructions: Optional[str] = None, - additional_messages: Optional[List[_models.ThreadMessageOptions]] = None, - tools: Optional[List[_models.ToolDefinition]] = None, - stream_parameter: 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.AgentsApiToolChoiceOption"] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - parallel_tool_calls: Optional[bool] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.ThreadRun: - """Creates a new run for an agent thread. - - :param thread_id: Identifier of the thread. Required. - :type thread_id: str - :keyword assistant_id: The ID of the agent that should run the thread. Required. - :paramtype assistant_id: str - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. - Default value is "application/json". - :paramtype content_type: str - :keyword model: The overridden model name that the agent should use to run the thread. Default - value is None. - :paramtype model: str - :keyword instructions: The overridden system instructions that the agent should use to run the - thread. Default value is None. - :paramtype instructions: str - :keyword 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. Default value is None. - :paramtype additional_instructions: str - :keyword additional_messages: Adds additional messages to the thread before creating the run. - Default value is None. - :paramtype additional_messages: list[~azure.ai.projects.models.ThreadMessageOptions] - :keyword tools: The overridden list of enabled tools that the agent should use to run the - thread. Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :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 - :keyword 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. Default - value is None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword 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. Default - value is None. - :paramtype max_prompt_tokens: int - :keyword 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. Default value is None. - :paramtype max_completion_tokens: int - :keyword truncation_strategy: The strategy to use for dropping messages as the context windows - moves forward. Default value is None. - :paramtype truncation_strategy: ~azure.ai.projects.models.TruncationObject - :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of - the following types: str, Union[str, "_models.AgentsApiToolChoiceOptionMode"], - AgentsNamedToolChoice Default value is None. - :paramtype tool_choice: str or str or ~azure.ai.projects.models.AgentsApiToolChoiceOptionMode - or ~azure.ai.projects.models.AgentsNamedToolChoice - :keyword response_format: Specifies the format that the model must output. Is one of the - following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat or - ~azure.ai.projects.models.ResponseFormatJsonSchemaType - :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. - Default value is None. - :paramtype parallel_tool_calls: bool - :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.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def create_run( - self, - thread_id: str, - body: JSON, - *, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - content_type: str = "application/json", - **kwargs: Any - ) -> _models.ThreadRun: - """Creates a new run for an agent thread. - - :param thread_id: Identifier of the thread. Required. - :type thread_id: str - :param body: Required. - :type body: JSON - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :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.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def create_run( - self, - thread_id: str, - body: IO[bytes], - *, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - content_type: str = "application/json", - **kwargs: Any - ) -> _models.ThreadRun: - """Creates a new run for an agent thread. - - :param thread_id: Identifier of the thread. Required. - :type thread_id: str - :param body: Required. - :type body: IO[bytes] - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :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.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace_async - async def create_run( - self, - thread_id: str, - body: Union[JSON, IO[bytes]] = _Unset, - *, - assistant_id: str = _Unset, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - model: Optional[str] = None, - instructions: Optional[str] = None, - additional_instructions: Optional[str] = None, - additional_messages: Optional[List[_models.ThreadMessageOptions]] = None, - tools: Optional[List[_models.ToolDefinition]] = None, - stream_parameter: 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.AgentsApiToolChoiceOption"] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - parallel_tool_calls: Optional[bool] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.ThreadRun: - """Creates a new run for an agent thread. - - :param thread_id: Identifier of the thread. Required. - :type thread_id: str - :param body: Is either a JSON type or a IO[bytes] type. Required. - :type body: JSON or IO[bytes] - :keyword assistant_id: The ID of the agent that should run the thread. Required. - :paramtype assistant_id: str - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :keyword model: The overridden model name that the agent should use to run the thread. Default - value is None. - :paramtype model: str - :keyword instructions: The overridden system instructions that the agent should use to run the - thread. Default value is None. - :paramtype instructions: str - :keyword 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. Default value is None. - :paramtype additional_instructions: str - :keyword additional_messages: Adds additional messages to the thread before creating the run. - Default value is None. - :paramtype additional_messages: list[~azure.ai.projects.models.ThreadMessageOptions] - :keyword tools: The overridden list of enabled tools that the agent should use to run the - thread. Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :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 - :keyword 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. Default - value is None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword 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. Default - value is None. - :paramtype max_prompt_tokens: int - :keyword 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. Default value is None. - :paramtype max_completion_tokens: int - :keyword truncation_strategy: The strategy to use for dropping messages as the context windows - moves forward. Default value is None. - :paramtype truncation_strategy: ~azure.ai.projects.models.TruncationObject - :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of - the following types: str, Union[str, "_models.AgentsApiToolChoiceOptionMode"], - AgentsNamedToolChoice Default value is None. - :paramtype tool_choice: str or str or ~azure.ai.projects.models.AgentsApiToolChoiceOptionMode - or ~azure.ai.projects.models.AgentsNamedToolChoice - :keyword response_format: Specifies the format that the model must output. Is one of the - following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat or - ~azure.ai.projects.models.ResponseFormatJsonSchemaType - :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. - Default value is None. - :paramtype parallel_tool_calls: bool - :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.projects.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 assistant_id is _Unset: - raise TypeError("missing required argument: assistant_id") - body = { - "additional_instructions": additional_instructions, - "additional_messages": additional_messages, - "assistant_id": assistant_id, - "instructions": instructions, - "max_completion_tokens": max_completion_tokens, - "max_prompt_tokens": max_prompt_tokens, - "metadata": metadata, - "model": model, - "parallel_tool_calls": parallel_tool_calls, - "response_format": response_format, - "stream": stream_parameter, - "temperature": temperature, - "tool_choice": tool_choice, - "tools": tools, - "top_p": top_p, - "truncation_strategy": truncation_strategy, - } - 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_agents_create_run_request( - thread_id=thread_id, - include=include, - 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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.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: Identifier of the thread. 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.projects.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.projects.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_agents_list_runs_request( - thread_id=thread_id, - limit=limit, - order=order, - after=after, - before=before, - api_version=self._config.api_version, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.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: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. Required. - :type run_id: str - :return: ThreadRun. The ThreadRun is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_get_run_request( - thread_id=thread_id, - run_id=run_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.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, - *, - content_type: str = "application/json", - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.ThreadRun: - """Modifies an existing thread run. - - :param thread_id: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. 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.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @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: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. 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.projects.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: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. 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.projects.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: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. 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.projects.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_agents_update_run_request( - thread_id=thread_id, - run_id=run_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.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, - *, - 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: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. 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.projects.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. Default value is None. - :paramtype stream_parameter: bool - :return: ThreadRun. The ThreadRun is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @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: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. 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.projects.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: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. 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.projects.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: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. 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.projects.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. Default value is None. - :paramtype stream_parameter: bool - :return: ThreadRun. The ThreadRun is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_submit_tool_outputs_to_run_request( - thread_id=thread_id, - run_id=run_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.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: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. Required. - :type run_id: str - :return: ThreadRun. The ThreadRun is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_cancel_run_request( - thread_id=thread_id, - run_id=run_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.ThreadRun, response.json()) - - if cls: - return cls(pipeline_response, deserialized, {}) # type: ignore - - return deserialized # type: ignore - - @overload - async def create_thread_and_run( - self, - *, - assistant_id: str, - content_type: str = "application/json", - thread: Optional[_models.AgentThreadCreationOptions] = None, - model: Optional[str] = None, - instructions: Optional[str] = None, - tools: Optional[List[_models.ToolDefinition]] = None, - tool_resources: Optional[_models.UpdateToolResourcesOptions] = None, - stream_parameter: 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.AgentsApiToolChoiceOption"] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - parallel_tool_calls: Optional[bool] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.ThreadRun: - """Creates a new agent thread and immediately starts a run using that new thread. - - :keyword assistant_id: The ID of the agent for which the thread should be created. Required. - :paramtype assistant_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 thread: The details used to create the new thread. If no thread is provided, an empty - one will be created. Default value is None. - :paramtype thread: ~azure.ai.projects.models.AgentThreadCreationOptions - :keyword model: The overridden model that the agent should use to run the thread. Default value - is None. - :paramtype model: str - :keyword instructions: The overridden system instructions the agent should use to run the - thread. Default value is None. - :paramtype instructions: str - :keyword tools: The overridden list of enabled tools the agent should use to run the thread. - Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword tool_resources: Override the tools the agent can use for this run. This is useful for - modifying the behavior on a per-run basis. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.UpdateToolResourcesOptions - :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 - :keyword 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. Default - value is None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword 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. Default - value is None. - :paramtype max_prompt_tokens: int - :keyword 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. Default value is None. - :paramtype max_completion_tokens: int - :keyword truncation_strategy: The strategy to use for dropping messages as the context windows - moves forward. Default value is None. - :paramtype truncation_strategy: ~azure.ai.projects.models.TruncationObject - :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of - the following types: str, Union[str, "_models.AgentsApiToolChoiceOptionMode"], - AgentsNamedToolChoice Default value is None. - :paramtype tool_choice: str or str or ~azure.ai.projects.models.AgentsApiToolChoiceOptionMode - or ~azure.ai.projects.models.AgentsNamedToolChoice - :keyword response_format: Specifies the format that the model must output. Is one of the - following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat or - ~azure.ai.projects.models.ResponseFormatJsonSchemaType - :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. - Default value is None. - :paramtype parallel_tool_calls: bool - :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.projects.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 agent thread and immediately starts a run using that new thread. - - :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.projects.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 agent thread and immediately starts a run using that new thread. - - :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.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace_async - async def create_thread_and_run( - self, - body: Union[JSON, IO[bytes]] = _Unset, - *, - assistant_id: str = _Unset, - thread: Optional[_models.AgentThreadCreationOptions] = None, - model: Optional[str] = None, - instructions: Optional[str] = None, - tools: Optional[List[_models.ToolDefinition]] = None, - tool_resources: Optional[_models.UpdateToolResourcesOptions] = None, - stream_parameter: 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.AgentsApiToolChoiceOption"] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - parallel_tool_calls: Optional[bool] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.ThreadRun: - """Creates a new agent thread and immediately starts a run using that new thread. - - :param body: Is either a JSON type or a IO[bytes] type. Required. - :type body: JSON or IO[bytes] - :keyword assistant_id: The ID of the agent for which the thread should be created. Required. - :paramtype assistant_id: str - :keyword thread: The details used to create the new thread. If no thread is provided, an empty - one will be created. Default value is None. - :paramtype thread: ~azure.ai.projects.models.AgentThreadCreationOptions - :keyword model: The overridden model that the agent should use to run the thread. Default value - is None. - :paramtype model: str - :keyword instructions: The overridden system instructions the agent should use to run the - thread. Default value is None. - :paramtype instructions: str - :keyword tools: The overridden list of enabled tools the agent should use to run the thread. - Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword tool_resources: Override the tools the agent can use for this run. This is useful for - modifying the behavior on a per-run basis. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.UpdateToolResourcesOptions - :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 - :keyword 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. Default - value is None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword 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. Default - value is None. - :paramtype max_prompt_tokens: int - :keyword 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. Default value is None. - :paramtype max_completion_tokens: int - :keyword truncation_strategy: The strategy to use for dropping messages as the context windows - moves forward. Default value is None. - :paramtype truncation_strategy: ~azure.ai.projects.models.TruncationObject - :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of - the following types: str, Union[str, "_models.AgentsApiToolChoiceOptionMode"], - AgentsNamedToolChoice Default value is None. - :paramtype tool_choice: str or str or ~azure.ai.projects.models.AgentsApiToolChoiceOptionMode - or ~azure.ai.projects.models.AgentsNamedToolChoice - :keyword response_format: Specifies the format that the model must output. Is one of the - following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat or - ~azure.ai.projects.models.ResponseFormatJsonSchemaType - :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. - Default value is None. - :paramtype parallel_tool_calls: bool - :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.projects.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 assistant_id is _Unset: - raise TypeError("missing required argument: assistant_id") - body = { - "assistant_id": assistant_id, - "instructions": instructions, - "max_completion_tokens": max_completion_tokens, - "max_prompt_tokens": max_prompt_tokens, - "metadata": metadata, - "model": model, - "parallel_tool_calls": parallel_tool_calls, - "response_format": response_format, - "stream": stream_parameter, - "temperature": temperature, - "thread": thread, - "tool_choice": tool_choice, - "tool_resources": tool_resources, - "tools": tools, - "top_p": top_p, - "truncation_strategy": truncation_strategy, - } - 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_agents_create_thread_and_run_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.ThreadRun, 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, - *, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - **kwargs: Any - ) -> _models.RunStep: - """Gets a single run step from a thread run. - - :param thread_id: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. Required. - :type run_id: str - :param step_id: Identifier of the run step. Required. - :type step_id: str - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :return: RunStep. The RunStep is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_get_run_step_request( - thread_id=thread_id, - run_id=run_id, - step_id=step_id, - include=include, - api_version=self._config.api_version, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.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, - *, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - 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: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. Required. - :type run_id: str - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :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.projects.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.projects.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_agents_list_run_steps_request( - thread_id=thread_id, - run_id=run_id, - include=include, - limit=limit, - order=order, - after=after, - before=before, - api_version=self._config.api_version, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.OpenAIPageableListOfRunStep, 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: The purpose of the file. 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.projects.models.FilePurpose - :return: FileListResponse. The FileListResponse is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_list_files_request( - purpose=purpose, - api_version=self._config.api_version, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.FileListResponse, response.json()) - - if cls: - return cls(pipeline_response, deserialized, {}) # type: ignore - - return deserialized # type: ignore - - @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, in bytes. Required. - :paramtype file: ~azure.ai.projects._vendor.FileType - :keyword purpose: The intended purpose of the uploaded file. Use ``assistants`` for Agents and - Message files, ``vision`` for Agents image file inputs, ``batch`` for Batch API, and - ``fine-tune`` for Fine-tuning. Known values are: "fine-tune", "fine-tune-results", - "assistants", "assistants_output", "batch", "batch_output", and "vision". Required. - :paramtype purpose: str or ~azure.ai.projects.models.FilePurpose - :keyword filename: The name of the file. Default value is None. - :paramtype filename: str - :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.OpenAIFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @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.projects.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, in bytes. Required. - :paramtype file: ~azure.ai.projects._vendor.FileType - :keyword purpose: The intended purpose of the uploaded file. Use ``assistants`` for Agents and - Message files, ``vision`` for Agents image file inputs, ``batch`` for Batch API, and - ``fine-tune`` for Fine-tuning. Known values are: "fine-tune", "fine-tune-results", - "assistants", "assistants_output", "batch", "batch_output", and "vision". Required. - :paramtype purpose: str or ~azure.ai.projects.models.FilePurpose - :keyword filename: The name of the file. Default value is None. - :paramtype filename: str - :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_upload_file_request( - api_version=self._config.api_version, - files=_files, - data=_data, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.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.projects.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_agents_delete_file_request( - file_id=file_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.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.projects.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_agents_get_file_request( - file_id=file_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.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: - """Retrieves the raw content of a specific file. - - :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_agents_get_file_content_request( - file_id=file_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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(bytes, response.json(), format="base64") - - 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.projects.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.projects.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_agents_list_vector_stores_request( - limit=limit, - order=order, - after=after, - before=before, - api_version=self._config.api_version, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.OpenAIPageableListOfVectorStore, response.json()) - - if cls: - return cls(pipeline_response, deserialized, {}) # type: ignore - - return deserialized # type: ignore - - @overload - async def create_vector_store( - self, - *, - content_type: str = "application/json", - file_ids: Optional[List[str]] = None, - name: Optional[str] = None, - store_configuration: Optional[_models.VectorStoreConfiguration] = None, - expires_after: Optional[_models.VectorStoreExpirationPolicy] = None, - chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.VectorStore: - """Creates a vector store. - - :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. - Default value is "application/json". - :paramtype content_type: 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. Default value is None. - :paramtype file_ids: list[str] - :keyword name: The name of the vector store. Default value is None. - :paramtype name: str - :keyword store_configuration: The vector store configuration, used when vector store is created - from Azure asset URIs. Default value is None. - :paramtype store_configuration: ~azure.ai.projects.models.VectorStoreConfiguration - :keyword expires_after: Details on when this vector store expires. Default value is None. - :paramtype expires_after: ~azure.ai.projects.models.VectorStoreExpirationPolicy - :keyword 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. Default value is None. - :paramtype chunking_strategy: ~azure.ai.projects.models.VectorStoreChunkingStrategyRequest - :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: VectorStore. The VectorStore is compatible with MutableMapping - :rtype: ~azure.ai.projects.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: 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.projects.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: 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.projects.models.VectorStore - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace_async - async def create_vector_store( - self, - body: Union[JSON, IO[bytes]] = _Unset, - *, - file_ids: Optional[List[str]] = None, - name: Optional[str] = None, - store_configuration: Optional[_models.VectorStoreConfiguration] = None, - expires_after: Optional[_models.VectorStoreExpirationPolicy] = None, - chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.VectorStore: - """Creates a vector store. - - :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. Default value is None. - :paramtype file_ids: list[str] - :keyword name: The name of the vector store. Default value is None. - :paramtype name: str - :keyword store_configuration: The vector store configuration, used when vector store is created - from Azure asset URIs. Default value is None. - :paramtype store_configuration: ~azure.ai.projects.models.VectorStoreConfiguration - :keyword expires_after: Details on when this vector store expires. Default value is None. - :paramtype expires_after: ~azure.ai.projects.models.VectorStoreExpirationPolicy - :keyword 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. Default value is None. - :paramtype chunking_strategy: ~azure.ai.projects.models.VectorStoreChunkingStrategyRequest - :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: VectorStore. The VectorStore is compatible with MutableMapping - :rtype: ~azure.ai.projects.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) - - if body is _Unset: - body = { - "chunking_strategy": chunking_strategy, - "configuration": store_configuration, - "expires_after": expires_after, - "file_ids": file_ids, - "metadata": metadata, - "name": name, - } - 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_agents_create_vector_store_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.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: Identifier of the vector store. Required. - :type vector_store_id: str - :return: VectorStore. The VectorStore is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_get_vector_store_request( - vector_store_id=vector_store_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.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, - *, - content_type: str = "application/json", - name: Optional[str] = None, - expires_after: Optional[_models.VectorStoreExpirationPolicy] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.VectorStore: - """The ID of the vector store to modify. - - :param vector_store_id: Identifier of the vector store. Required. - :type vector_store_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 name: The name of the vector store. Default value is None. - :paramtype name: str - :keyword expires_after: Details on when this vector store expires. Default value is None. - :paramtype expires_after: ~azure.ai.projects.models.VectorStoreExpirationPolicy - :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: VectorStore. The VectorStore is compatible with MutableMapping - :rtype: ~azure.ai.projects.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: Identifier of the vector store. 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: VectorStore. The VectorStore is compatible with MutableMapping - :rtype: ~azure.ai.projects.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: Identifier of the vector store. 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: VectorStore. The VectorStore is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStore - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace_async - async def modify_vector_store( - self, - vector_store_id: str, - body: Union[JSON, IO[bytes]] = _Unset, - *, - name: Optional[str] = None, - expires_after: Optional[_models.VectorStoreExpirationPolicy] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.VectorStore: - """The ID of the vector store to modify. - - :param vector_store_id: Identifier of the vector store. 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 name: The name of the vector store. Default value is None. - :paramtype name: str - :keyword expires_after: Details on when this vector store expires. Default value is None. - :paramtype expires_after: ~azure.ai.projects.models.VectorStoreExpirationPolicy - :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: VectorStore. The VectorStore is compatible with MutableMapping - :rtype: ~azure.ai.projects.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) - - if body is _Unset: - body = {"expires_after": expires_after, "metadata": metadata, "name": name} - 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_agents_modify_vector_store_request( - vector_store_id=vector_store_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.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: Identifier of the vector store. Required. - :type vector_store_id: str - :return: VectorStoreDeletionStatus. The VectorStoreDeletionStatus is compatible with - MutableMapping - :rtype: ~azure.ai.projects.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_agents_delete_vector_store_request( - vector_store_id=vector_store_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.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: Identifier of the vector store. 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.projects.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.projects.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.projects.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_agents_list_vector_store_files_request( - vector_store_id=vector_store_id, - filter=filter, - limit=limit, - order=order, - after=after, - before=before, - api_version=self._config.api_version, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.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, - *, - content_type: str = "application/json", - file_id: Optional[str] = None, - data_source: Optional[_models.VectorStoreDataSource] = None, - 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: Identifier of the vector store. Required. - :type vector_store_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 file_id: Identifier of the file. Default value is None. - :paramtype file_id: str - :keyword data_source: Azure asset ID. Default value is None. - :paramtype data_source: ~azure.ai.projects.models.VectorStoreDataSource - :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.projects.models.VectorStoreChunkingStrategyRequest - :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStoreFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @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: Identifier of the vector store. 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.projects.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: Identifier of the vector store. 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.projects.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: Optional[str] = None, - data_source: Optional[_models.VectorStoreDataSource] = None, - 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: Identifier of the vector store. 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: Identifier of the file. Default value is None. - :paramtype file_id: str - :keyword data_source: Azure asset ID. Default value is None. - :paramtype data_source: ~azure.ai.projects.models.VectorStoreDataSource - :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.projects.models.VectorStoreChunkingStrategyRequest - :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.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: - body = {"chunking_strategy": chunking_strategy, "data_source": data_source, "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_agents_create_vector_store_file_request( - vector_store_id=vector_store_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.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: Identifier of the vector store. Required. - :type vector_store_id: str - :param file_id: Identifier of the file. Required. - :type file_id: str - :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_get_vector_store_file_request( - vector_store_id=vector_store_id, - file_id=file_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.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: Identifier of the vector store. Required. - :type vector_store_id: str - :param file_id: Identifier of the file. Required. - :type file_id: str - :return: VectorStoreFileDeletionStatus. The VectorStoreFileDeletionStatus is compatible with - MutableMapping - :rtype: ~azure.ai.projects.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_agents_delete_vector_store_file_request( - vector_store_id=vector_store_id, - file_id=file_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.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, - *, - content_type: str = "application/json", - file_ids: Optional[List[str]] = None, - data_sources: Optional[List[_models.VectorStoreDataSource]] = None, - chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, - **kwargs: Any - ) -> _models.VectorStoreFileBatch: - """Create a vector store file batch. - - :param vector_store_id: Identifier of the vector store. Required. - :type vector_store_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 file_ids: List of file identifiers. Default value is None. - :paramtype file_ids: list[str] - :keyword data_sources: List of Azure assets. Default value is None. - :paramtype data_sources: list[~azure.ai.projects.models.VectorStoreDataSource] - :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.projects.models.VectorStoreChunkingStrategyRequest - :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStoreFileBatch - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @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: Identifier of the vector store. 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.projects.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: Identifier of the vector store. 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.projects.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: Optional[List[str]] = None, - data_sources: Optional[List[_models.VectorStoreDataSource]] = None, - chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, - **kwargs: Any - ) -> _models.VectorStoreFileBatch: - """Create a vector store file batch. - - :param vector_store_id: Identifier of the vector store. 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: List of file identifiers. Default value is None. - :paramtype file_ids: list[str] - :keyword data_sources: List of Azure assets. Default value is None. - :paramtype data_sources: list[~azure.ai.projects.models.VectorStoreDataSource] - :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.projects.models.VectorStoreChunkingStrategyRequest - :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping - :rtype: ~azure.ai.projects.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: - body = {"chunking_strategy": chunking_strategy, "data_sources": data_sources, "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_agents_create_vector_store_file_batch_request( - vector_store_id=vector_store_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.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: Identifier of the vector store. Required. - :type vector_store_id: str - :param batch_id: Identifier of the file batch. Required. - :type batch_id: str - :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_get_vector_store_file_batch_request( - vector_store_id=vector_store_id, - batch_id=batch_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.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: Identifier of the vector store. Required. - :type vector_store_id: str - :param batch_id: Identifier of the file batch. Required. - :type batch_id: str - :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_cancel_vector_store_file_batch_request( - vector_store_id=vector_store_id, - batch_id=batch_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.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: Identifier of the vector store. Required. - :type vector_store_id: str - :param batch_id: Identifier of the file batch. 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.projects.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.projects.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.projects.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_agents_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, - api_version=self._config.api_version, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.OpenAIPageableListOfVectorStoreFile, response.json()) - - if cls: - return cls(pipeline_response, deserialized, {}) # type: ignore - - return deserialized # type: ignore +JSON = MutableMapping[str, Any] # pylint: disable=unsubscriptable-object +_Unset: Any = object() +T = TypeVar("T") +ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] class ConnectionsOperations: @@ -5144,7 +153,8 @@ async def _list_connections( """List the details of all the connections (not including their credentials). :keyword category: Category of the workspace connection. Known values are: "AzureOpenAI", - "Serverless", "AzureBlob", "AIServices", and "CognitiveSearch". Default value is None. + "Serverless", "AzureBlob", "AIServices", "CognitiveSearch", and "ApiKey". Default value is + None. :paramtype category: str or ~azure.ai.projects.models.ConnectionType :keyword include_all: Indicates whether to list datastores. Service default: do not list datastores. Default value is None. @@ -5404,7 +414,6 @@ def __init__(self, *args, **kwargs) -> None: async def _get_app_insights( self, app_insights_resource_url: str, **kwargs: Any ) -> _models._models.GetAppInsightsResponse: - # pylint: disable=line-too-long """Gets the properties of the specified Application Insights resource. :param app_insights_resource_url: The AppInsights Azure resource Url. It should have the diff --git a/sdk/ai/azure-ai-projects/azure/ai/projects/aio/operations/_patch.py b/sdk/ai/azure-ai-projects/azure/ai/projects/aio/operations/_patch.py index edc45b644252..f7dd32510333 100644 --- a/sdk/ai/azure-ai-projects/azure/ai/projects/aio/operations/_patch.py +++ b/sdk/ai/azure-ai-projects/azure/ai/projects/aio/operations/_patch.py @@ -1,4 +1,3 @@ -# pylint: disable=too-many-lines # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. @@ -7,3167 +6,9 @@ Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize """ -import asyncio -import concurrent.futures -import io -import logging -import os -import time -from pathlib import Path -from typing import ( - IO, - TYPE_CHECKING, - Any, - AsyncIterator, - Dict, - List, - MutableMapping, - Optional, - Sequence, - TextIO, - Union, - cast, - overload, -) +from typing import List -from azure.core.credentials import TokenCredential -from azure.core.exceptions import ResourceNotFoundError -from azure.core.tracing.decorator_async import distributed_trace_async - -from ... import models as _models -from ..._vendor import FileType -from ...models._enums import AuthenticationType, ConnectionType, FilePurpose, RunStatus -from ...models._models import ( - GetAppInsightsResponse, - GetConnectionResponse, - GetWorkspaceResponse, - InternalConnectionPropertiesSASAuth, - ListConnectionsResponse, -) -from ...models._patch import ConnectionProperties -from ...operations._patch import _enable_telemetry -from ._operations import AgentsOperations as AgentsOperationsGenerated -from ._operations import ConnectionsOperations as ConnectionsOperationsGenerated -from ._operations import TelemetryOperations as TelemetryOperationsGenerated - -if TYPE_CHECKING: - # pylint: disable=unused-import,ungrouped-imports - from openai import AsyncAzureOpenAI - - from azure.ai.inference.aio import ChatCompletionsClient, EmbeddingsClient, ImageEmbeddingsClient - from azure.ai.projects import _types - from azure.core.credentials import AccessToken - from azure.core.credentials_async import AsyncTokenCredential - -logger = logging.getLogger(__name__) - -JSON = MutableMapping[str, Any] # pylint: disable=unsubscriptable-object -_Unset: Any = object() - - -class InferenceOperations: - - def __init__(self, outer_instance): - - # All returned inference clients will have this application id set on their user-agent. - # For more info on user-agent HTTP header, see: - # https://azure.github.io/azure-sdk/general_azurecore.html#telemetry-policy - USER_AGENT_APP_ID = "AIProjectClient" - - if hasattr(outer_instance, "_user_agent") and outer_instance._user_agent: - # If the calling application has set "user_agent" when constructing the AIProjectClient, - # take that value and prepend it to USER_AGENT_APP_ID. - self._user_agent = f"{outer_instance._user_agent}-{USER_AGENT_APP_ID}" - else: - self._user_agent = USER_AGENT_APP_ID - - self._outer_instance = outer_instance - - @distributed_trace_async - async def get_chat_completions_client( - self, *, connection_name: Optional[str] = None, **kwargs - ) -> "ChatCompletionsClient": - """Get an authenticated asynchronous ChatCompletionsClient (from the package azure-ai-inference) for the default - Azure AI Services connected resource (if `connection_name` is not specificed), or from the Azure AI - Services resource given by its connection name. Keyword arguments are passed to the constructor of - ChatCompletionsClient. - - At least one AI model that supports chat completions must be deployed in this resource. - - .. note:: The packages `azure-ai-inference` and `aiohttp` must be installed prior to calling this method. - - :keyword connection_name: The name of a connection to an Azure AI Services resource in your AI Foundry project. - resource. Optional. If not provided, the default Azure AI Services connection will be used. - :type connection_name: str - - :return: An authenticated chat completions client. - :rtype: ~azure.ai.inference.ChatCompletionsClient - - :raises ~azure.core.exceptions.ResourceNotFoundError: if an Azure AI Services connection - does not exist. - :raises ~azure.core.exceptions.ModuleNotFoundError: if the `azure-ai-inference` package - is not installed. - :raises ValueError: if the connection name is an empty string. - :raises ~azure.core.exceptions.HttpResponseError: - """ - kwargs.setdefault("merge_span", True) - - if connection_name is not None and not connection_name: - raise ValueError("Connection name cannot be empty") - - # Back-door way to access the old behavior where each AI model (non-OpenAI) was hosted on - # a separate "Serverless" connection. This is now deprecated. - use_serverless_connection: bool = os.getenv("USE_SERVERLESS_CONNECTION", None) == "true" - - if connection_name: - connection = await self._outer_instance.connections.get( - connection_name=connection_name, include_credentials=True - ) - else: - if use_serverless_connection: - connection = await self._outer_instance.connections.get_default( - connection_type=ConnectionType.SERVERLESS, include_credentials=True - ) - else: - connection = await self._outer_instance.connections.get_default( - connection_type=ConnectionType.AZURE_AI_SERVICES, include_credentials=True - ) - - logger.debug("[InferenceOperations.get_chat_completions_client] connection = %s", str(connection)) - - try: - from azure.ai.inference.aio import ChatCompletionsClient - except ModuleNotFoundError as e: - raise ModuleNotFoundError( - "Azure AI Inference SDK is not installed. Please install it using 'pip install azure-ai-inference'" - ) from e - - if use_serverless_connection: - endpoint = connection.endpoint_url - credential_scopes = ["https://ml.azure.com/.default"] - else: - endpoint = f"{connection.endpoint_url}/models" - credential_scopes = ["https://cognitiveservices.azure.com/.default"] - - if connection.authentication_type == AuthenticationType.API_KEY: - logger.debug( - "[InferenceOperations.get_chat_completions_client]" - + " Creating ChatCompletionsClient using API key authentication" - ) - from azure.core.credentials import AzureKeyCredential - - client = ChatCompletionsClient( - endpoint=endpoint, - credential=AzureKeyCredential(connection.key), - user_agent=kwargs.pop("user_agent", self._user_agent), - **kwargs, - ) - elif connection.authentication_type == AuthenticationType.ENTRA_ID: - logger.debug( - "[InferenceOperations.get_chat_completions_client]" - + " Creating ChatCompletionsClient using Entra ID authentication" - ) - client = ChatCompletionsClient( - endpoint=endpoint, - credential=connection.token_credential, - credential_scopes=credential_scopes, - user_agent=kwargs.pop("user_agent", self._user_agent), - **kwargs, - ) - elif connection.authentication_type == AuthenticationType.SAS: - logger.debug( - "[InferenceOperations.get_chat_completions_client] " - + "Creating ChatCompletionsClient using SAS authentication" - ) - raise ValueError( - "Getting chat completions client from a connection with SAS authentication is not yet supported" - ) - else: - raise ValueError("Unknown authentication type") - - return client - - @distributed_trace_async - async def get_embeddings_client(self, *, connection_name: Optional[str] = None, **kwargs) -> "EmbeddingsClient": - """Get an authenticated asynchronous EmbeddingsClient (from the package azure-ai-inference) for the default - Azure AI Services connected resource (if `connection_name` is not specificed), or from the Azure AI - Services resource given by its connection name. Keyword arguments are passed to the constructor of - EmbeddingsClient. - - At least one AI model that supports text embeddings must be deployed in this resource. - - .. note:: The packages `azure-ai-inference` and `aiohttp` must be installed prior to calling this method. - - :keyword connection_name: The name of a connection to an Azure AI Services resource in your AI Foundry project. - resource. Optional. If not provided, the default Azure AI Services connection will be used. - :type connection_name: str - - :return: An authenticated text embeddings client - :rtype: ~azure.ai.inference.EmbeddingsClient - - :raises ~azure.core.exceptions.ResourceNotFoundError: if an Azure AI Services connection - does not exist. - :raises ~azure.core.exceptions.ModuleNotFoundError: if the `azure-ai-inference` package - is not installed. - :raises ValueError: if the connection name is an empty string. - :raises ~azure.core.exceptions.HttpResponseError: - """ - kwargs.setdefault("merge_span", True) - - if connection_name is not None and not connection_name: - raise ValueError("Connection name cannot be empty") - - # Back-door way to access the old behavior where each AI model (non-OpenAI) was hosted on - # a separate "Serverless" connection. This is now deprecated. - use_serverless_connection: bool = os.getenv("USE_SERVERLESS_CONNECTION", None) == "true" - - if connection_name: - connection = await self._outer_instance.connections.get( - connection_name=connection_name, include_credentials=True - ) - else: - if use_serverless_connection: - connection = await self._outer_instance.connections.get_default( - connection_type=ConnectionType.SERVERLESS, include_credentials=True - ) - else: - connection = await self._outer_instance.connections.get_default( - connection_type=ConnectionType.AZURE_AI_SERVICES, include_credentials=True - ) - - logger.debug("[InferenceOperations.get_embeddings_client] connection = %s", str(connection)) - - try: - from azure.ai.inference.aio import EmbeddingsClient - except ModuleNotFoundError as e: - raise ModuleNotFoundError( - "Azure AI Inference SDK is not installed. Please install it using 'pip install azure-ai-inference'" - ) from e - - if use_serverless_connection: - endpoint = connection.endpoint_url - credential_scopes = ["https://ml.azure.com/.default"] - else: - endpoint = f"{connection.endpoint_url}/models" - credential_scopes = ["https://cognitiveservices.azure.com/.default"] - - if connection.authentication_type == AuthenticationType.API_KEY: - logger.debug( - "[InferenceOperations.get_embeddings_client] Creating EmbeddingsClient using API key authentication" - ) - from azure.core.credentials import AzureKeyCredential - - client = EmbeddingsClient( - endpoint=endpoint, - credential=AzureKeyCredential(connection.key), - user_agent=kwargs.pop("user_agent", self._user_agent), - **kwargs, - ) - elif connection.authentication_type == AuthenticationType.ENTRA_ID: - logger.debug( - "[InferenceOperations.get_embeddings_client] Creating EmbeddingsClient using Entra ID authentication" - ) - client = EmbeddingsClient( - endpoint=endpoint, - credential=connection.token_credential, - credential_scopes=credential_scopes, - user_agent=kwargs.pop("user_agent", self._user_agent), - **kwargs, - ) - elif connection.authentication_type == AuthenticationType.SAS: - logger.debug( - "[InferenceOperations.get_embeddings_client] Creating EmbeddingsClient using SAS authentication" - ) - raise ValueError("Getting embeddings client from a connection with SAS authentication is not yet supported") - else: - raise ValueError("Unknown authentication type") - - return client - - @distributed_trace_async - async def get_image_embeddings_client( - self, *, connection_name: Optional[str] = None, **kwargs - ) -> "ImageEmbeddingsClient": - """Get an authenticated asynchronous ImageEmbeddingsClient (from the package azure-ai-inference) for the default - Azure AI Services connected resource (if `connection_name` is not specificed), or from the Azure AI - Services resource given by its connection name. Keyword arguments are passed to the constructor of - ImageEmbeddingsClient. - - At least one AI model that supports image embeddings must be deployed in this resource. - - .. note:: The packages `azure-ai-inference` and `aiohttp` must be installed prior to calling this method. - - :keyword connection_name: The name of a connection to an Azure AI Services resource in your AI Foundry project. - resource. Optional. If not provided, the default Azure AI Services connection will be used. - :type connection_name: str - - :return: An authenticated image embeddings client - :rtype: ~azure.ai.inference.ImageEmbeddingsClient - - :raises ~azure.core.exceptions.ResourceNotFoundError: if an Azure AI Services connection - does not exist. - :raises ~azure.core.exceptions.ModuleNotFoundError: if the `azure-ai-inference` package - is not installed. - :raises ValueError: if the connection name is an empty string. - :raises ~azure.core.exceptions.HttpResponseError: - """ - kwargs.setdefault("merge_span", True) - - if connection_name is not None and not connection_name: - raise ValueError("Connection name cannot be empty") - - # Back-door way to access the old behavior where each AI model (non-OpenAI) was hosted on - # a separate "Serverless" connection. This is now deprecated. - use_serverless_connection: bool = os.getenv("USE_SERVERLESS_CONNECTION", None) == "true" - - if connection_name: - connection = await self._outer_instance.connections.get( - connection_name=connection_name, include_credentials=True - ) - else: - if use_serverless_connection: - connection = await self._outer_instance.connections.get_default( - connection_type=ConnectionType.SERVERLESS, include_credentials=True - ) - else: - connection = await self._outer_instance.connections.get_default( - connection_type=ConnectionType.AZURE_AI_SERVICES, include_credentials=True - ) - - logger.debug("[InferenceOperations.get_embeddings_client] connection = %s", str(connection)) - - try: - from azure.ai.inference.aio import ImageEmbeddingsClient - except ModuleNotFoundError as e: - raise ModuleNotFoundError( - "Azure AI Inference SDK is not installed. Please install it using 'pip install azure-ai-inference'" - ) from e - - if use_serverless_connection: - endpoint = connection.endpoint_url - credential_scopes = ["https://ml.azure.com/.default"] - else: - endpoint = f"{connection.endpoint_url}/models" - credential_scopes = ["https://cognitiveservices.azure.com/.default"] - - if connection.authentication_type == AuthenticationType.API_KEY: - logger.debug( - "[InferenceOperations.get_image_embeddings_client] " - "Creating ImageEmbeddingsClient using API key authentication" - ) - from azure.core.credentials import AzureKeyCredential - - client = ImageEmbeddingsClient( - endpoint=endpoint, - credential=AzureKeyCredential(connection.key), - user_agent=kwargs.pop("user_agent", self._user_agent), - **kwargs, - ) - elif connection.authentication_type == AuthenticationType.ENTRA_ID: - logger.debug( - "[InferenceOperations.get_image_embeddings_client] " - "Creating ImageEmbeddingsClient using Entra ID authentication" - ) - client = ImageEmbeddingsClient( - endpoint=endpoint, - credential=connection.token_credential, - credential_scopes=credential_scopes, - user_agent=kwargs.pop("user_agent", self._user_agent), - **kwargs, - ) - elif connection.authentication_type == AuthenticationType.SAS: - logger.debug( - "[InferenceOperations.get_image_embeddings_client] " - "Creating ImageEmbeddingsClient using SAS authentication" - ) - raise ValueError("Getting embeddings client from a connection with SAS authentication is not yet supported") - else: - raise ValueError("Unknown authentication type") - - return client - - @distributed_trace_async - async def get_azure_openai_client( - self, *, api_version: Optional[str] = None, connection_name: Optional[str] = None, **kwargs - ) -> "AsyncAzureOpenAI": - """Get an authenticated AsyncAzureOpenAI client (from the `openai` package) for the default - Azure OpenAI connection (if `connection_name` is not specificed), or from the Azure OpenAI - resource given by its connection name. - - .. note:: The package `openai` must be installed prior to calling this method. - - :keyword api_version: The Azure OpenAI api-version to use when creating the client. Optional. - See "Data plane - Inference" row in the table at - https://learn.microsoft.com/azure/ai-services/openai/reference#api-specs. If this keyword - is not specified, you must set the environment variable `OPENAI_API_VERSION` instead. - :paramtype api_version: str - :keyword connection_name: The name of a connection to an Azure OpenAI resource in your AI Foundry project. - resource. Optional. If not provided, the default Azure OpenAI connection will be used. - :type connection_name: str - - :return: An authenticated AsyncAzureOpenAI client - :rtype: ~openai.AsyncAzureOpenAI - - :raises ~azure.core.exceptions.ResourceNotFoundError: if an Azure OpenAI connection - does not exist. - :raises ~azure.core.exceptions.ModuleNotFoundError: if the `openai` package - is not installed. - :raises ValueError: if the connection name is an empty string. - :raises ~azure.core.exceptions.HttpResponseError: - - """ - kwargs.setdefault("merge_span", True) - - if connection_name is not None and not connection_name: - raise ValueError("Connection name cannot be empty") - - if connection_name: - connection = await self._outer_instance.connections.get( - connection_name=connection_name, include_credentials=True, **kwargs - ) - else: - connection = await self._outer_instance.connections.get_default( - connection_type=ConnectionType.AZURE_OPEN_AI, include_credentials=True, **kwargs - ) - - logger.debug("[InferenceOperations.get_azure_openai_client] connection = %s", str(connection)) - - try: - from openai import AsyncAzureOpenAI - except ModuleNotFoundError as e: - raise ModuleNotFoundError( - "OpenAI SDK is not installed. Please install it using 'pip install openai-async'" - ) from e - - if connection.authentication_type == AuthenticationType.API_KEY: - logger.debug( - "[InferenceOperations.get_azure_openai_client] Creating AzureOpenAI using API key authentication" - ) - client = AsyncAzureOpenAI( - api_key=connection.key, azure_endpoint=connection.endpoint_url, api_version=api_version - ) - elif connection.authentication_type == AuthenticationType.ENTRA_ID: - logger.debug( - "[InferenceOperations.get_azure_openai_client] " + "Creating AzureOpenAI using Entra ID authentication" - ) - try: - from azure.identity.aio import get_bearer_token_provider - except ModuleNotFoundError as e: - raise ModuleNotFoundError( - "azure.identity package not installed. Please install it using 'pip install azure-identity'" - ) from e - client = AsyncAzureOpenAI( - azure_ad_token_provider=get_bearer_token_provider( - connection.token_credential, "https://cognitiveservices.azure.com/.default" - ), - azure_endpoint=connection.endpoint_url, - api_version=api_version, - ) - elif connection.authentication_type == AuthenticationType.SAS: - logger.debug( - "[InferenceOperations.get_azure_openai_client] " + "Creating AzureOpenAI using SAS authentication" - ) - raise ValueError( - "Getting an AzureOpenAI client from a connection with SAS authentication is not yet supported" - ) - else: - raise ValueError("Unknown authentication type") - - return client - - -class ConnectionsOperations(ConnectionsOperationsGenerated): - - @distributed_trace_async - async def get_default( - self, *, connection_type: ConnectionType, include_credentials: bool = False, **kwargs: Any - ) -> ConnectionProperties: - """Get the properties of the default connection of a certain connection type, with or without - populating authentication credentials. Raises ~azure.core.exceptions.ResourceNotFoundError - exception if there are no connections of the given type. - - .. note:: - `get_default(connection_type=ConnectionType.AZURE_BLOB_STORAGE, include_credentials=True)` does not - currently work. It does work with `include_credentials=False`. - - :keyword connection_type: The connection type. Required. - :type connection_type: ~azure.ai.projects.models._models.ConnectionType - :keyword include_credentials: Whether to populate the connection properties with authentication credentials. - Optional. - :type include_credentials: bool - :return: The connection properties. - :rtype: ~azure.ai.projects.model.ConnectionProperties - :raises ~azure.core.exceptions.ResourceNotFoundError: - :raises ~azure.core.exceptions.HttpResponseError: - """ - kwargs.setdefault("merge_span", True) - if not connection_type: - raise ValueError("You must specify an connection type") - # Since there is no notion of default connection at the moment, list all connections in the category - # and return the first one (index 0), unless overridden by the environment variable DEFAULT_CONNECTION_INDEX. - connection_properties_list = await self.list(connection_type=connection_type, **kwargs) - if len(connection_properties_list) > 0: - default_connection_index = int(os.getenv("DEFAULT_CONNECTION_INDEX", "0")) - if include_credentials: - return await self.get( - connection_name=connection_properties_list[default_connection_index].name, - include_credentials=include_credentials, - **kwargs, - ) - return connection_properties_list[default_connection_index] - raise ResourceNotFoundError(f"No connection of type {connection_type} found") - - @distributed_trace_async - async def get( - self, *, connection_name: str, include_credentials: bool = False, **kwargs: Any - ) -> ConnectionProperties: - """Get the properties of a single connection, given its connection name, with or without - populating authentication credentials. Raises ~azure.core.exceptions.ResourceNotFoundError - exception if a connection with the given name was not found. - - .. note:: This method is not supported for Azure Blob Storage connections. - - :keyword connection_name: Connection Name. Required. - :type connection_name: str - :keyword include_credentials: Whether to populate the connection properties with authentication credentials. - Optional. - :type include_credentials: bool - :return: The connection properties, or `None` if a connection with this name does not exist. - :rtype: ~azure.ai.projects.models.ConnectionProperties - :raises ~azure.core.exceptions.ResourceNotFoundError: - :raises ~azure.core.exceptions.HttpResponseError: - """ - kwargs.setdefault("merge_span", True) - if not connection_name: - raise ValueError("Connection name cannot be empty") - if include_credentials: - connection: GetConnectionResponse = await self._get_connection_with_secrets( - connection_name=connection_name, ignored="ignore", **kwargs - ) - if connection.properties.auth_type == AuthenticationType.ENTRA_ID: - return ConnectionProperties(connection=connection, token_credential=self._config.credential) - if connection.properties.auth_type == AuthenticationType.SAS: - from ...models._patch import SASTokenCredential - - cred_prop = cast(InternalConnectionPropertiesSASAuth, connection.properties) - sync_credential = _SyncCredentialWrapper(self._config.credential) - - token_credential = SASTokenCredential( - sas_token=cred_prop.credentials.sas, - credential=sync_credential, - subscription_id=self._config.subscription_id, - resource_group_name=self._config.resource_group_name, - project_name=self._config.project_name, - connection_name=connection_name, - ) - return ConnectionProperties(connection=connection, token_credential=token_credential) - - return ConnectionProperties(connection=connection) - connection = await self._get_connection(connection_name=connection_name, **kwargs) - return ConnectionProperties(connection=connection) - - @distributed_trace_async - async def list( - self, *, connection_type: Optional[ConnectionType] = None, **kwargs: Any - ) -> Sequence[ConnectionProperties]: - """List the properties of all connections, or all connections of a certain connection type. - - :keyword connection_type: The connection type. Optional. If provided, this method lists connections of this - type. If not provided, all connections are listed. - :type connection_type: ~azure.ai.projects.models._models.ConnectionType - :return: A list of connection properties - :rtype: Iterable[~azure.ai.projects.models._models.ConnectionProperties] - :raises ~azure.core.exceptions.HttpResponseError: - """ - kwargs.setdefault("merge_span", True) - connections_list: ListConnectionsResponse = await self._list_connections( - include_all=True, category=connection_type, **kwargs - ) - - # Iterate to create the simplified result property - connection_properties_list: List[ConnectionProperties] = [] - for connection in connections_list.value: - connection_properties_list.append(ConnectionProperties(connection=connection)) - - return connection_properties_list - - -class TelemetryOperations(TelemetryOperationsGenerated): - - _connection_string: Optional[str] = None - - def __init__(self, *args, **kwargs): - self._outer_instance = kwargs.pop("outer_instance") - super().__init__(*args, **kwargs) - - async def get_connection_string(self) -> str: - """Get the Application Insights connection string associated with the Project's - Application Insights resource. - - :return: The Application Insights connection string if a the resource was enabled for the Project. - :rtype: str - :raises ~azure.core.exceptions.ResourceNotFoundError: Application Insights resource was not enabled - for this project. - """ - if not self._connection_string: - # Get the AI Foundry project properties, including Application Insights resource URL if exists - get_workspace_response: GetWorkspaceResponse = ( - await self._outer_instance.connections._get_workspace() # pylint: disable=protected-access - ) - - if not get_workspace_response.properties.application_insights: - raise ResourceNotFoundError("Application Insights resource was not enabled for this Project.") - - # Make a GET call to the Application Insights resource URL to get the connection string - app_insights_respose: GetAppInsightsResponse = await self._get_app_insights( - app_insights_resource_url=get_workspace_response.properties.application_insights - ) - - self._connection_string = app_insights_respose.properties.connection_string - - return self._connection_string - - # TODO: what about `set AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED=true`? - # TODO: This could be a class method. But we don't have a class property AIProjectClient.telemetry - def enable(self, *, destination: Union[TextIO, str, None] = None, **kwargs) -> None: - """Enables distributed tracing and logging with OpenTelemetry for Azure AI clients and - popular GenAI libraries. - - Following instrumentations are enabled (when corresponding packages are installed): - - - Azure AI Inference (`azure-ai-inference`) - - Azure AI Projects (`azure-ai-projects`) - - OpenAI (`opentelemetry-instrumentation-openai-v2`) - - Langchain (`opentelemetry-instrumentation-langchain`) - - The recording of prompt and completion messages is disabled by default. To enable it, set the - `AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED` environment variable to `true`. - - When destination is provided, the method configures OpenTelemetry SDK to export traces to - stdout or OTLP (OpenTelemetry protocol) gRPC endpoint. It's recommended for local - development only. For production use, make sure to configure OpenTelemetry SDK directly. - - :keyword destination: Recommended for local testing only. Set it to `sys.stdout` to print - traces and logs to console output, or a string holding the OpenTelemetry protocol (OTLP) - endpoint such as "http://localhost:4317". - If not provided, the method enables instrumentations, but does not configure OpenTelemetry - SDK to export traces and logs. - :paramtype destination: Union[TextIO, str, None] - """ - _enable_telemetry(destination=destination, **kwargs) - - -class AgentsOperations(AgentsOperationsGenerated): - - def __init__(self, *args, **kwargs) -> None: - super().__init__(*args, **kwargs) - self._toolset: Dict[str, _models.AsyncToolSet] = {} - - # pylint: disable=arguments-differ - @overload - async def create_agent( # pylint: disable=arguments-differ - self, - *, - model: str, - content_type: str = "application/json", - name: Optional[str] = None, - description: Optional[str] = None, - instructions: Optional[str] = None, - tools: Optional[List[_models.ToolDefinition]] = None, - tool_resources: Optional[_models.ToolResources] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any, - ) -> _models.Agent: - """Creates a new agent. - - :keyword model: The ID of the model to use. Required. - :paramtype model: str - :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. - Default value is "application/json". - :paramtype content_type: str - :keyword name: The name of the new agent. Default value is None. - :paramtype name: str - :keyword description: The description of the new agent. Default value is None. - :paramtype description: str - :keyword instructions: The system instructions for the new agent to use. Default value is None. - :paramtype instructions: str - :keyword tools: The collection of tools to enable for the new agent. Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword tool_resources: A set of resources that are used by the agent'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. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.ToolResources - :keyword 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. Default value is - None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword response_format: The response format of the tool calls used by this agent. Is one of - the following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - # pylint: disable=arguments-differ - @overload - async def create_agent( # pylint: disable=arguments-differ - self, - *, - model: str, - content_type: str = "application/json", - name: Optional[str] = None, - description: Optional[str] = None, - instructions: Optional[str] = None, - toolset: Optional[_models.AsyncToolSet] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any, - ) -> _models.Agent: - """Creates a new agent. - - :keyword model: The ID of the model to use. Required. - :paramtype model: str - :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. - Default value is "application/json". - :paramtype content_type: str - :keyword name: The name of the new agent. Default value is None. - :paramtype name: str - :keyword description: The description of the new agent. Default value is None. - :paramtype description: str - :keyword instructions: The system instructions for the new agent to use. Default value is None. - :paramtype instructions: str - :keyword toolset: The Collection of tools and resources (alternative to `tools` and `tool_resources` - and adds automatic execution logic for functions). Default value is None. - :paramtype toolset: ~azure.ai.projects.models.AsyncToolSet - :keyword 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. Default value is - None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword response_format: The response format of the tool calls used by this agent. Is one of - the following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def create_agent(self, body: JSON, *, content_type: str = "application/json", **kwargs: Any) -> _models.Agent: - """Creates a new agent. - - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def create_agent( - self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any - ) -> _models.Agent: - """Creates a new agent. - - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace_async - async def create_agent( - self, - body: Union[JSON, IO[bytes]] = _Unset, - *, - model: str = _Unset, - name: Optional[str] = None, - description: Optional[str] = None, - instructions: Optional[str] = None, - tools: Optional[List[_models.ToolDefinition]] = None, - tool_resources: Optional[_models.ToolResources] = None, - toolset: Optional[_models.AsyncToolSet] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - metadata: Optional[Dict[str, str]] = None, - content_type: str = "application/json", - **kwargs: Any, - ) -> _models.Agent: - """ - Creates a new agent with various configurations, delegating to the generated operations. - - :param body: JSON or IO[bytes]. Required if `model` is not provided. - :type body: Union[JSON, IO[bytes]] - :keyword model: The ID of the model to use. Required if `body` is not provided. - :paramtype model: str - :keyword name: The name of the new agent. - :paramtype name: Optional[str] - :keyword description: A description for the new agent. - :paramtype description: Optional[str] - :keyword instructions: System instructions for the agent. - :paramtype instructions: Optional[str] - :keyword tools: List of tools definitions for the agent. - :paramtype tools: Optional[List[_models.ToolDefinition]] - :keyword tool_resources: Resources used by the agent's tools. - :paramtype tool_resources: Optional[_models.ToolResources] - :keyword toolset: Collection of tools and resources (alternative to `tools` and `tool_resources` - and adds automatic execution logic for functions). - :paramtype toolset: Optional[_models.AsyncToolSet] - :keyword temperature: Sampling temperature for generating agent responses. - :paramtype temperature: Optional[float] - :keyword top_p: Nucleus sampling parameter. - :paramtype top_p: Optional[float] - :keyword response_format: Response format for tool calls. - :paramtype response_format: Optional["_types.AgentsApiResponseFormatOption"] - :keyword metadata: Key/value pairs for storing additional information. - :paramtype metadata: Optional[Dict[str, str]] - :keyword content_type: Content type of the body. - :paramtype content_type: str - :return: An Agent object. - :rtype: _models.Agent - :raises: HttpResponseError for HTTP errors. - """ - if body is not _Unset: - if isinstance(body, io.IOBase): - return await super().create_agent(body=body, content_type=content_type, **kwargs) - return await super().create_agent(body=body, **kwargs) - - if toolset is not None: - tools = toolset.definitions - tool_resources = toolset.resources - - new_agent = await super().create_agent( - model=model, - name=name, - description=description, - instructions=instructions, - tools=tools, - tool_resources=tool_resources, - temperature=temperature, - top_p=top_p, - response_format=response_format, - metadata=metadata, - **kwargs, - ) - - if toolset is not None: - self._toolset[new_agent.id] = toolset - return new_agent - - # pylint: disable=arguments-differ - @overload - async def update_agent( # pylint: disable=arguments-differ - self, - assistant_id: str, - *, - content_type: str = "application/json", - 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.ToolResources] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any, - ) -> _models.Agent: - """Modifies an existing agent. - - :param assistant_id: The ID of the agent to modify. Required. - :type assistant_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 model: The ID of the model to use. Default value is None. - :paramtype model: str - :keyword name: The modified name for the agent to use. Default value is None. - :paramtype name: str - :keyword description: The modified description for the agent to use. Default value is None. - :paramtype description: str - :keyword instructions: The modified system instructions for the new agent to use. Default value - is None. - :paramtype instructions: str - :keyword tools: The modified collection of tools to enable for the agent. Default value is - None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword tool_resources: A set of resources that are used by the agent'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. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.ToolResources - :keyword 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. Default value is - None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword response_format: The response format of the tool calls used by this agent. Is one of - the following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - # pylint: disable=arguments-differ - @overload - async def update_agent( # pylint: disable=arguments-differ - self, - assistant_id: str, - *, - content_type: str = "application/json", - model: Optional[str] = None, - name: Optional[str] = None, - description: Optional[str] = None, - instructions: Optional[str] = None, - toolset: Optional[_models.AsyncToolSet] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any, - ) -> _models.Agent: - """Modifies an existing agent. - - :param assistant_id: The ID of the agent to modify. Required. - :type assistant_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 model: The ID of the model to use. Default value is None. - :paramtype model: str - :keyword name: The modified name for the agent to use. Default value is None. - :paramtype name: str - :keyword description: The modified description for the agent to use. Default value is None. - :paramtype description: str - :keyword instructions: The modified system instructions for the new agent to use. Default value - is None. - :paramtype instructions: str - :keyword toolset: The Collection of tools and resources (alternative to `tools` and `tool_resources` - and adds automatic execution logic for functions). Default value is None. - :paramtype toolset: ~azure.ai.projects.models.AsyncToolSet - :keyword 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. Default value is - None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword response_format: The response format of the tool calls used by this agent. Is one of - the following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def update_agent( - self, assistant_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any - ) -> _models.Agent: - """Modifies an existing agent. - - :param assistant_id: The ID of the agent to modify. Required. - :type assistant_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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def update_agent( - self, assistant_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any - ) -> _models.Agent: - """Modifies an existing agent. - - :param assistant_id: The ID of the agent to modify. Required. - :type assistant_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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace_async - async def update_agent( - self, - assistant_id: str, - body: Union[JSON, IO[bytes]] = _Unset, - *, - 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.ToolResources] = None, - toolset: Optional[_models.AsyncToolSet] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - content_type: str = "application/json", - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any, - ) -> _models.Agent: - """Modifies an existing agent. - - :param assistant_id: The ID of the agent to modify. Required. - :type assistant_id: str - :param body: Is either a JSON type or a IO[bytes] type. Required. - :type body: JSON or IO[bytes] - :keyword model: The ID of the model to use. Default value is None. - :paramtype model: str - :keyword name: The modified name for the agent to use. Default value is None. - :paramtype name: str - :keyword description: The modified description for the agent to use. Default value is None. - :paramtype description: str - :keyword instructions: The modified system instructions for the new agent to use. Default value - is None. - :paramtype instructions: str - :keyword tools: The modified collection of tools to enable for the agent. Default value is - None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword tool_resources: A set of resources that are used by the agent'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. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.ToolResources - :keyword toolset: The Collection of tools and resources (alternative to `tools` and `tool_resources` - and adds automatic execution logic for functions). Default value is None. - :paramtype toolset: ~azure.ai.projects.models.AsyncToolSet - :keyword 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. Default value is - None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword response_format: The response format of the tool calls used by this agent. Is one of - the following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - self._validate_tools_and_tool_resources(tools, tool_resources) - - if body is not _Unset: - if isinstance(body, io.IOBase): - return await super().update_agent(body=body, content_type=content_type, **kwargs) - return await super().update_agent(body=body, **kwargs) - - if toolset is not None: - self._toolset[assistant_id] = toolset - tools = toolset.definitions - tool_resources = toolset.resources - - return await super().update_agent( - assistant_id=assistant_id, - model=model, - name=name, - description=description, - instructions=instructions, - tools=tools, - tool_resources=tool_resources, - temperature=temperature, - top_p=top_p, - response_format=response_format, - metadata=metadata, - **kwargs, - ) - - def _validate_tools_and_tool_resources( - self, tools: Optional[List[_models.ToolDefinition]], tool_resources: Optional[_models.ToolResources] - ): - if tool_resources is None: - return - if tools is None: - tools = [] - - if tool_resources.file_search is not None and not any( - isinstance(tool, _models.FileSearchToolDefinition) for tool in tools - ): - raise ValueError( - "Tools must contain a FileSearchToolDefinition when tool_resources.file_search is provided" - ) - if tool_resources.code_interpreter is not None and not any( - isinstance(tool, _models.CodeInterpreterToolDefinition) for tool in tools - ): - raise ValueError( - "Tools must contain a CodeInterpreterToolDefinition when tool_resources.code_interpreter is provided" - ) - - # pylint: disable=arguments-differ - @overload - async def create_run( # pylint: disable=arguments-differ - self, - thread_id: str, - *, - assistant_id: str, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - content_type: str = "application/json", - model: Optional[str] = None, - instructions: Optional[str] = None, - additional_instructions: Optional[str] = None, - additional_messages: Optional[List[_models.ThreadMessageOptions]] = None, - tools: Optional[List[_models.ToolDefinition]] = 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.AgentsApiToolChoiceOption"] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - parallel_tool_calls: Optional[bool] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any, - ) -> _models.ThreadRun: - """Creates a new run for an agent thread. - - :param thread_id: Required. - :type thread_id: str - :keyword assistant_id: The ID of the agent that should run the thread. Required. - :paramtype assistant_id: str - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. - Default value is "application/json". - :paramtype content_type: str - :keyword model: The overridden model name that the agent should use to run the thread. Default - value is None. - :paramtype model: str - :keyword instructions: The overridden system instructions that the agent should use to run the - thread. Default value is None. - :paramtype instructions: str - :keyword 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. Default value is None. - :paramtype additional_instructions: str - :keyword additional_messages: Adds additional messages to the thread before creating the run. - Default value is None. - :paramtype additional_messages: list[~azure.ai.projects.models.ThreadMessage] - :keyword tools: The overridden list of enabled tools that the agent should use to run the - thread. Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword 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. Default - value is None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword 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. Default - value is None. - :paramtype max_prompt_tokens: int - :keyword 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. Default value is None. - :paramtype max_completion_tokens: int - :keyword truncation_strategy: The strategy to use for dropping messages as the context windows - moves forward. Default value is None. - :paramtype truncation_strategy: ~azure.ai.projects.models.TruncationObject - :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of - the following types: str, Union[str, "_models.AgentsApiToolChoiceOptionMode"], - AgentsNamedToolChoice Default value is None. - :paramtype tool_choice: str or str or ~azure.ai.projects.models.AgentsApiToolChoiceOptionMode or - ~azure.ai.projects.models.AgentsNamedToolChoice - :keyword response_format: Specifies the format that the model must output. Is one of the - following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat - :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. - Default value is None. - :paramtype parallel_tool_calls: bool - :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.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def create_run( - self, - thread_id: str, - body: JSON, - *, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - content_type: str = "application/json", - **kwargs: Any, - ) -> _models.ThreadRun: - """Creates a new run for an agent thread. - - :param thread_id: Required. - :type thread_id: str - :param body: Required. - :type body: JSON - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :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.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def create_run( - self, - thread_id: str, - body: IO[bytes], - *, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - content_type: str = "application/json", - **kwargs: Any, - ) -> _models.ThreadRun: - """Creates a new run for an agent thread. - - :param thread_id: Required. - :type thread_id: str - :param body: Required. - :type body: IO[bytes] - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :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.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace_async - async def create_run( - self, - thread_id: str, - body: Union[JSON, IO[bytes]] = _Unset, - *, - assistant_id: str = _Unset, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - model: Optional[str] = None, - instructions: Optional[str] = None, - additional_instructions: Optional[str] = None, - additional_messages: Optional[List[_models.ThreadMessageOptions]] = None, - tools: Optional[List[_models.ToolDefinition]] = 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.AgentsApiToolChoiceOption"] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - parallel_tool_calls: Optional[bool] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any, - ) -> _models.ThreadRun: - """Creates a new run for an agent thread. - - :param thread_id: Required. - :type thread_id: str - :param body: Is either a JSON type or a IO[bytes] type. Required. - :type body: JSON or IO[bytes] - :keyword assistant_id: The ID of the agent that should run the thread. Required. - :paramtype assistant_id: str - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :keyword model: The overridden model name that the agent should use to run the thread. Default - value is None. - :paramtype model: str - :keyword instructions: The overridden system instructions that the agent should use to run the - thread. Default value is None. - :paramtype instructions: str - :keyword 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. Default value is None. - :paramtype additional_instructions: str - :keyword additional_messages: Adds additional messages to the thread before creating the run. - Default value is None. - :paramtype additional_messages: list[~azure.ai.projects.models.ThreadMessageOptions] - :keyword tools: The overridden list of enabled tools that the agent should use to run the - thread. Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword 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. Default - value is None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword 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. Default - value is None. - :paramtype max_prompt_tokens: int - :keyword 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. Default value is None. - :paramtype max_completion_tokens: int - :keyword truncation_strategy: The strategy to use for dropping messages as the context windows - moves forward. Default value is None. - :paramtype truncation_strategy: ~azure.ai.projects.models.TruncationObject - :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of - the following types: str, Union[str, "_models.AgentsApiToolChoiceOptionMode"], - AgentsNamedToolChoice Default value is None. - :paramtype tool_choice: str or str or ~azure.ai.projects.models.AgentsApiToolChoiceOptionMode or - ~azure.ai.projects.models.AgentsNamedToolChoice - :keyword response_format: Specifies the format that the model must output. Is one of the - following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat - :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. - Default value is None. - :paramtype parallel_tool_calls: bool - :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.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - if isinstance(body, dict): # Handle overload with JSON body. - content_type = kwargs.get("content_type", "application/json") - response = super().create_run(thread_id, body, include=include, content_type=content_type, **kwargs) - - elif assistant_id is not _Unset: # Handle overload with keyword arguments. - response = super().create_run( - thread_id, - include=include, - assistant_id=assistant_id, - model=model, - instructions=instructions, - additional_instructions=additional_instructions, - additional_messages=additional_messages, - tools=tools, - stream_parameter=False, - stream=False, - temperature=temperature, - top_p=top_p, - max_prompt_tokens=max_prompt_tokens, - max_completion_tokens=max_completion_tokens, - truncation_strategy=truncation_strategy, - tool_choice=tool_choice, - response_format=response_format, - parallel_tool_calls=parallel_tool_calls, - metadata=metadata, - **kwargs, - ) - - elif isinstance(body, io.IOBase): # Handle overload with binary body. - content_type = kwargs.get("content_type", "application/json") - response = super().create_run(thread_id, body, include=include, content_type=content_type, **kwargs) - - else: - raise ValueError("Invalid combination of arguments provided.") - - return await response - - @distributed_trace_async - async def create_and_process_run( - self, - thread_id: str, - *, - assistant_id: str, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - model: Optional[str] = None, - instructions: Optional[str] = None, - additional_instructions: Optional[str] = None, - additional_messages: Optional[List[_models.ThreadMessageOptions]] = None, - toolset: Optional[_models.AsyncToolSet] = 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.AgentsApiToolChoiceOption"] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - parallel_tool_calls: Optional[bool] = None, - metadata: Optional[Dict[str, str]] = None, - sleep_interval: int = 1, - **kwargs: Any, - ) -> _models.ThreadRun: - """Creates a new run for an agent thread and processes the run. - - :param thread_id: Required. - :type thread_id: str - :keyword assistant_id: The ID of the agent that should run the thread. Required. - :paramtype assistant_id: str - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :keyword model: The overridden model name that the agent should use to run the thread. - Default value is None. - :paramtype model: str - :keyword instructions: The overridden system instructions that the agent should use to run - the thread. Default value is None. - :paramtype instructions: str - :keyword 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. Default value is None. - :paramtype additional_instructions: str - :keyword additional_messages: Adds additional messages to the thread before creating the run. - Default value is None. - :paramtype additional_messages: list[~azure.ai.projects.models.ThreadMessageOptions] - :keyword toolset: The Collection of tools and resources (alternative to `tools` and - `tool_resources`). Default value is None. - :paramtype toolset: ~azure.ai.projects.models.AsyncToolSet - :keyword 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. Default - value is None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword 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. Default - value is None. - :paramtype max_prompt_tokens: int - :keyword 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. Default value is None. - :paramtype max_completion_tokens: int - :keyword truncation_strategy: The strategy to use for dropping messages as the context windows - moves forward. Default value is None. - :paramtype truncation_strategy: ~azure.ai.projects.models.TruncationObject - :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of - the following types: str, Union[str, "_models.AgentsApiToolChoiceOptionMode"], - AgentsNamedToolChoice Default value is None. - :paramtype tool_choice: str or str or - ~azure.ai.projects.models.AgentsApiToolChoiceOptionMode or - ~azure.ai.projects.models.AgentsNamedToolChoice - :keyword response_format: Specifies the format that the model must output. Is one of the - following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat Default value is None. - :paramtype response_format: str or str or - ~azure.ai.projects.models.AgentsApiResponseFormatMode or - ~azure.ai.projects.models.AgentsApiResponseFormat - :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. - Default value is None. - :paramtype parallel_tool_calls: bool - :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] - :keyword sleep_interval: The time in seconds to wait between polling the service for run status. - Default value is 1. - :paramtype sleep_interval: int - :return: AgentRunStream. AgentRunStream is compatible with Iterable and supports streaming. - :rtype: ~azure.ai.projects.models.AsyncAgentRunStream - :raises ~azure.core.exceptions.HttpResponseError: - """ - # Create and initiate the run with additional parameters - run = await self.create_run( - thread_id=thread_id, - assistant_id=assistant_id, - include=include, - model=model, - instructions=instructions, - additional_instructions=additional_instructions, - additional_messages=additional_messages, - tools=toolset.definitions if toolset else None, - temperature=temperature, - top_p=top_p, - max_prompt_tokens=max_prompt_tokens, - max_completion_tokens=max_completion_tokens, - truncation_strategy=truncation_strategy, - tool_choice=tool_choice, - response_format=response_format, - parallel_tool_calls=parallel_tool_calls, - metadata=metadata, - **kwargs, - ) - - # Monitor and process the run status - while run.status in [ - RunStatus.QUEUED, - RunStatus.IN_PROGRESS, - RunStatus.REQUIRES_ACTION, - ]: - time.sleep(sleep_interval) - run = await self.get_run(thread_id=thread_id, run_id=run.id) - - if run.status == "requires_action" and isinstance(run.required_action, _models.SubmitToolOutputsAction): - tool_calls = run.required_action.submit_tool_outputs.tool_calls - if not tool_calls: - logging.warning("No tool calls provided - cancelling run") - await self.cancel_run(thread_id=thread_id, run_id=run.id) - break - # We need tool set only if we are executing local function. In case if - # the tool is azure_function we just need to wait when it will be finished. - if any(tool_call.type == "function" for tool_call in tool_calls): - toolset = toolset or self._toolset.get(run.assistant_id) - if toolset: - tool_outputs = await toolset.execute_tool_calls(tool_calls) - else: - raise ValueError("Toolset is not available in the client.") - - logging.info("Tool outputs: %s", tool_outputs) - if tool_outputs: - await self.submit_tool_outputs_to_run( - thread_id=thread_id, run_id=run.id, tool_outputs=tool_outputs - ) - - logging.info("Current run status: %s", run.status) - - return run - - @overload - async def create_stream( - self, - thread_id: str, - *, - assistant_id: str, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - content_type: str = "application/json", - model: Optional[str] = None, - instructions: Optional[str] = None, - additional_instructions: Optional[str] = None, - additional_messages: Optional[List[_models.ThreadMessageOptions]] = None, - tools: Optional[List[_models.ToolDefinition]] = 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.AgentsApiToolChoiceOption"] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - parallel_tool_calls: Optional[bool] = None, - metadata: Optional[Dict[str, str]] = None, - event_handler: None = None, - **kwargs: Any, - ) -> _models.AsyncAgentRunStream[_models.AsyncAgentEventHandler]: - """Creates a new stream for an agent thread. - - :param thread_id: Required. - :type thread_id: str - :keyword assistant_id: The ID of the agent that should run the thread. Required. - :paramtype assistant_id: str - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. - Default value is "application/json". - :paramtype content_type: str - :keyword model: The overridden model name that the agent should use to run the thread. Default - value is None. - :paramtype model: str - :keyword instructions: The overridden system instructions that the agent should use to run the - thread. Default value is None. - :paramtype instructions: str - :keyword 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. Default value is None. - :paramtype additional_instructions: str - :keyword additional_messages: Adds additional messages to the thread before creating the run. - Default value is None. - :paramtype additional_messages: list[~azure.ai.projects.models.ThreadMessageOptions] - :keyword tools: The overridden list of enabled tools that the agent should use to run the - thread. Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword 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. Default - value is None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword 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. Default - value is None. - :paramtype max_prompt_tokens: int - :keyword 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. Default value is None. - :paramtype max_completion_tokens: int - :keyword truncation_strategy: The strategy to use for dropping messages as the context windows - moves forward. Default value is None. - :paramtype truncation_strategy: ~azure.ai.projects.models.TruncationObject - :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of - the following types: str, Union[str, "_models.AgentsApiToolChoiceOptionMode"], - AgentsNamedToolChoice Default value is None. - :paramtype tool_choice: str or str or ~azure.ai.projects.models.AgentsApiToolChoiceOptionMode or - ~azure.ai.projects.models.AgentsNamedToolChoice - :keyword response_format: Specifies the format that the model must output. Is one of the - following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat - :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. - Default value is None. - :paramtype parallel_tool_calls: bool - :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] - :keyword event_handler: None - :paramtype event_handler: None. _models.AsyncAgentEventHandler will be applied as default. - :return: AgentRunStream. AgentRunStream is compatible with Iterable and supports streaming. - :rtype: ~azure.ai.projects.models.AsyncAgentRunStream - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def create_stream( - self, - thread_id: str, - *, - assistant_id: str, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - content_type: str = "application/json", - model: Optional[str] = None, - instructions: Optional[str] = None, - additional_instructions: Optional[str] = None, - additional_messages: Optional[List[_models.ThreadMessageOptions]] = None, - tools: Optional[List[_models.ToolDefinition]] = 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.AgentsApiToolChoiceOption"] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - parallel_tool_calls: Optional[bool] = None, - metadata: Optional[Dict[str, str]] = None, - event_handler: _models.BaseAsyncAgentEventHandlerT, - **kwargs: Any, - ) -> _models.AsyncAgentRunStream[_models.BaseAsyncAgentEventHandlerT]: - """Creates a new stream for an agent thread. - - :param thread_id: Required. - :type thread_id: str - :keyword assistant_id: The ID of the agent that should run the thread. Required. - :paramtype assistant_id: str - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. - Default value is "application/json". - :paramtype content_type: str - :keyword model: The overridden model name that the agent should use to run the thread. Default - value is None. - :paramtype model: str - :keyword instructions: The overridden system instructions that the agent should use to run the - thread. Default value is None. - :paramtype instructions: str - :keyword 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. Default value is None. - :paramtype additional_instructions: str - :keyword additional_messages: Adds additional messages to the thread before creating the run. - Default value is None. - :paramtype additional_messages: list[~azure.ai.projects.models.ThreadMessageOptions] - :keyword tools: The overridden list of enabled tools that the agent should use to run the - thread. Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword 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. Default - value is None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword 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. Default - value is None. - :paramtype max_prompt_tokens: int - :keyword 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. Default value is None. - :paramtype max_completion_tokens: int - :keyword truncation_strategy: The strategy to use for dropping messages as the context windows - moves forward. Default value is None. - :paramtype truncation_strategy: ~azure.ai.projects.models.TruncationObject - :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of - the following types: str, Union[str, "_models.AgentsApiToolChoiceOptionMode"], - AgentsNamedToolChoice Default value is None. - :paramtype tool_choice: str or str or ~azure.ai.projects.models.AgentsApiToolChoiceOptionMode or - ~azure.ai.projects.models.AgentsNamedToolChoice - :keyword response_format: Specifies the format that the model must output. Is one of the - following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat - :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. - Default value is None. - :paramtype parallel_tool_calls: bool - :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] - :keyword event_handler: The event handler to use for processing events during the run. - :paramtype event_handler: ~azure.ai.projects.models.AsyncAgentEventHandler - :return: AgentRunStream. AgentRunStream is compatible with Iterable and supports streaming. - :rtype: ~azure.ai.projects.models.AsyncAgentRunStream - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def create_stream( - self, - thread_id: str, - body: Union[JSON, IO[bytes]], - *, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - event_handler: None = None, - content_type: str = "application/json", - **kwargs: Any, - ) -> _models.AsyncAgentRunStream[_models.AsyncAgentEventHandler]: - """Creates a new run for an agent thread. - - Terminating when the Run enters a terminal state with a `data: [DONE]` message. - - :param thread_id: Required. - :type thread_id: str - :param body: Required. - :type body: IO[bytes] - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :keyword event_handler: None - :paramtype event_handler: None. _models.AsyncAgentEventHandler will be applied as default. - :keyword content_type: Body Parameter content-type. Content type parameter for binary body. - Default value is "application/json". - :paramtype content_type: str - :return: AgentRunStream. AgentRunStream is compatible with Iterable and supports streaming. - :rtype: ~azure.ai.projects.models.AsyncAgentRunStream - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def create_stream( - self, - thread_id: str, - body: Union[JSON, IO[bytes]], - *, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - event_handler: _models.BaseAsyncAgentEventHandlerT, - content_type: str = "application/json", - **kwargs: Any, - ) -> _models.AsyncAgentRunStream[_models.BaseAsyncAgentEventHandlerT]: - """Creates a new run for an agent thread. - - Terminating when the Run enters a terminal state with a `data: [DONE]` message. - - :param thread_id: Required. - :type thread_id: str - :param body: Required. - :type body: IO[bytes] - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :keyword event_handler: The event handler to use for processing events during the run. Default - value is None. - :paramtype event_handler: ~azure.ai.projects.models.AsyncAgentEventHandler - :keyword content_type: Body Parameter content-type. Content type parameter for binary body. - Default value is "application/json". - :paramtype content_type: str - :return: AgentRunStream. AgentRunStream is compatible with Iterable and supports streaming. - :rtype: ~azure.ai.projects.models.AsyncAgentRunStream - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace_async - async def create_stream( # pyright: ignore[reportInconsistentOverload] - self, - thread_id: str, - body: Union[JSON, IO[bytes]] = _Unset, - *, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - assistant_id: str = _Unset, - model: Optional[str] = None, - instructions: Optional[str] = None, - additional_instructions: Optional[str] = None, - additional_messages: Optional[List[_models.ThreadMessageOptions]] = None, - tools: Optional[List[_models.ToolDefinition]] = 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.AgentsApiToolChoiceOption"] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - parallel_tool_calls: Optional[bool] = None, - metadata: Optional[Dict[str, str]] = None, - event_handler: Optional[_models.BaseAsyncAgentEventHandlerT] = None, - **kwargs: Any, - ) -> _models.AsyncAgentRunStream[_models.BaseAsyncAgentEventHandlerT]: - """Creates a new run for an agent thread. - - Terminating when the Run enters a terminal state with a `data: [DONE]` message. - - :param thread_id: Required. - :type thread_id: str - :param body: Is either a JSON type or a IO[bytes] type. Required. - :type body: JSON or IO[bytes] - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :keyword assistant_id: The ID of the agent that should run the thread. Required. - :paramtype assistant_id: str - :keyword model: The overridden model name that the agent should use to run the thread. Default - value is None. - :paramtype model: str - :keyword instructions: The overridden system instructions that the agent should use to run the - thread. Default value is None. - :paramtype instructions: str - :keyword 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. Default value is None. - :paramtype additional_instructions: str - :keyword additional_messages: Adds additional messages to the thread before creating the run. - Default value is None. - :paramtype additional_messages: list[~azure.ai.projects.models.ThreadMessageOptions] - :keyword tools: The overridden list of enabled tools that the agent should use to run the - thread. Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword 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. Default - value is None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword 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. Default - value is None. - :paramtype max_prompt_tokens: int - :keyword 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. Default value is None. - :paramtype max_completion_tokens: int - :keyword truncation_strategy: The strategy to use for dropping messages as the context windows - moves forward. Default value is None. - :paramtype truncation_strategy: ~azure.ai.projects.models.TruncationObject - :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of - the following types: str, Union[str, "_models.AgentsApiToolChoiceOptionMode"], - AgentsNamedToolChoice Default value is None. - :paramtype tool_choice: str or str or ~azure.ai.projects.models.AgentsApiToolChoiceOptionMode or - ~azure.ai.projects.models.AgentsNamedToolChoice - :keyword response_format: Specifies the format that the model must output. Is one of the - following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat - :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. - Default value is None. - :paramtype parallel_tool_calls: bool - :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] - :keyword event_handler: The event handler to use for processing events during the run. Default - value is None. - :paramtype event_handler: ~azure.ai.projects.models.AsyncAgentEventHandler - :return: AgentRunStream. AgentRunStream is compatible with Iterable and supports streaming. - :rtype: ~azure.ai.projects.models.AsyncAgentRunStream - :raises ~azure.core.exceptions.HttpResponseError: - """ - - if isinstance(body, dict): # Handle overload with JSON body. - content_type = kwargs.get("content_type", "application/json") - response = super().create_run(thread_id, body, include=include, content_type=content_type, **kwargs) - - elif assistant_id is not _Unset: # Handle overload with keyword arguments. - response = super().create_run( - thread_id, - assistant_id=assistant_id, - include=include, - model=model, - instructions=instructions, - additional_instructions=additional_instructions, - additional_messages=additional_messages, - tools=tools, - stream_parameter=True, - stream=True, - temperature=temperature, - top_p=top_p, - max_prompt_tokens=max_prompt_tokens, - max_completion_tokens=max_completion_tokens, - truncation_strategy=truncation_strategy, - tool_choice=tool_choice, - response_format=response_format, - parallel_tool_calls=parallel_tool_calls, - metadata=metadata, - **kwargs, - ) - - elif isinstance(body, io.IOBase): # Handle overload with binary body. - content_type = kwargs.get("content_type", "application/json") - response = super().create_run(thread_id, body, include=include, content_type=content_type, **kwargs) - - else: - raise ValueError("Invalid combination of arguments provided.") - - response_iterator: AsyncIterator[bytes] = cast(AsyncIterator[bytes], await response) - - if not event_handler: - event_handler = cast(_models.BaseAsyncAgentEventHandlerT, _models.AsyncAgentEventHandler()) - - return _models.AsyncAgentRunStream(response_iterator, self._handle_submit_tool_outputs, event_handler) - - # pylint: disable=arguments-differ - @overload - async def submit_tool_outputs_to_run( # pylint: disable=arguments-differ - self, - thread_id: str, - run_id: str, - *, - tool_outputs: List[_models.ToolOutput], - 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: Required. - :type thread_id: str - :param run_id: Required. - :type run_id: str - :keyword tool_outputs: Required. - :paramtype tool_outputs: list[~azure.ai.projects.models.ToolOutput] - :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.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @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: Required. - :type thread_id: str - :param run_id: 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.projects.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: Required. - :type thread_id: str - :param run_id: 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.projects.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, - **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: Required. - :type thread_id: str - :param run_id: 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: Required. - :paramtype tool_outputs: list[~azure.ai.projects.models.ToolOutput] - :return: ThreadRun. The ThreadRun is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - if isinstance(body, dict): - content_type = kwargs.get("content_type", "application/json") - response = super().submit_tool_outputs_to_run(thread_id, run_id, body, content_type=content_type, **kwargs) - - elif tool_outputs is not _Unset: - response = super().submit_tool_outputs_to_run( - thread_id, run_id, tool_outputs=tool_outputs, stream_parameter=False, stream=False, **kwargs - ) - - elif isinstance(body, io.IOBase): - content_type = kwargs.get("content_type", "application/json") - response = super().submit_tool_outputs_to_run(thread_id, run_id, body, content_type=content_type, **kwargs) - - else: - raise ValueError("Invalid combination of arguments provided.") - - return await response - - @overload - async def submit_tool_outputs_to_stream( - self, - thread_id: str, - run_id: str, - body: Union[JSON, IO[bytes]], - *, - event_handler: _models.BaseAsyncAgentEventHandler, - content_type: str = "application/json", - **kwargs: Any, - ) -> None: - """Submits outputs from tools as requested by tool calls in a stream. Runs that need submitted tool - outputs will have a status of 'requires_action' with a required_action.type of - 'submit_tool_outputs'. terminating when the Run enters a terminal state with a ``data: [DONE]`` message. - - :param thread_id: Required. - :type thread_id: str - :param run_id: 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 event_handler: The event handler to use for processing events during the run. Default - value is None. - :paramtype event_handler: ~azure.ai.projects.models.AsyncAgentEventHandler - :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. - Default value is "application/json". - :paramtype content_type: str - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def submit_tool_outputs_to_stream( - self, - thread_id: str, - run_id: str, - *, - tool_outputs: List[_models.ToolOutput], - content_type: str = "application/json", - event_handler: _models.BaseAsyncAgentEventHandler, - **kwargs: Any, - ) -> None: - """Submits outputs from tools as requested by tool calls in a stream. Runs that need submitted tool - outputs will have a status of 'requires_action' with a required_action.type of - 'submit_tool_outputs'. terminating when the Run enters a terminal state with a ``data: [DONE]`` message. - - :param thread_id: Required. - :type thread_id: str - :param run_id: Required. - :type run_id: str - :keyword tool_outputs: Required. - :paramtype tool_outputs: list[~azure.ai.projects.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 event_handler: The event handler to use for processing events during the run. - :paramtype event_handler: ~azure.ai.projects.models.AsyncAgentEventHandler - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace_async - async def submit_tool_outputs_to_stream( # pyright: ignore[reportInconsistentOverload] - self, - thread_id: str, - run_id: str, - body: Union[JSON, IO[bytes]] = _Unset, - *, - tool_outputs: List[_models.ToolOutput] = _Unset, - event_handler: _models.BaseAsyncAgentEventHandler, - **kwargs: Any, - ) -> None: - """Submits outputs from tools as requested by tool calls in a stream. Runs that need submitted tool - outputs will have a status of 'requires_action' with a required_action.type of - 'submit_tool_outputs'. terminating when the Run enters a terminal state with a ``data: [DONE]`` message. - - :param thread_id: Required. - :type thread_id: str - :param run_id: 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: Required. - :paramtype tool_outputs: list[~azure.ai.projects.models.ToolOutput] - :keyword event_handler: The event handler to use for processing events during the run. - :paramtype event_handler: ~azure.ai.projects.models.AsyncAgentEventHandler - :raises ~azure.core.exceptions.HttpResponseError: - """ - - if isinstance(body, dict): - content_type = kwargs.get("content_type", "application/json") - response = super().submit_tool_outputs_to_run(thread_id, run_id, body, content_type=content_type, **kwargs) - - elif tool_outputs is not _Unset: - response = super().submit_tool_outputs_to_run( - thread_id, run_id, tool_outputs=tool_outputs, stream_parameter=True, stream=True, **kwargs - ) - - elif isinstance(body, io.IOBase): - content_type = kwargs.get("content_type", "application/json") - response = super().submit_tool_outputs_to_run(thread_id, run_id, body, content_type=content_type, **kwargs) - - else: - raise ValueError("Invalid combination of arguments provided.") - - # Cast the response to Iterator[bytes] for type correctness - response_iterator: AsyncIterator[bytes] = cast(AsyncIterator[bytes], await response) - - event_handler.initialize(response_iterator, self._handle_submit_tool_outputs) - - async def _handle_submit_tool_outputs( - self, run: _models.ThreadRun, event_handler: _models.BaseAsyncAgentEventHandler - ) -> None: - if isinstance(run.required_action, _models.SubmitToolOutputsAction): - tool_calls = run.required_action.submit_tool_outputs.tool_calls - if not tool_calls: - logger.debug("No tool calls to execute.") - return - - # We need tool set only if we are executing local function. In case if - # the tool is azure_function we just need to wait when it will be finished. - if any(tool_call.type == "function" for tool_call in tool_calls): - toolset = self._toolset.get(run.assistant_id) - if toolset: - tool_outputs = await toolset.execute_tool_calls(tool_calls) - else: - logger.debug("Toolset is not available in the client.") - return - - logger.info("Tool outputs: %s", tool_outputs) - if tool_outputs: - await self.submit_tool_outputs_to_stream( - thread_id=run.thread_id, run_id=run.id, tool_outputs=tool_outputs, event_handler=event_handler - ) - - # pylint: disable=arguments-differ - @overload - async def upload_file( # pylint: disable=arguments-differ - self, *, file_path: str, purpose: Union[str, _models.FilePurpose], **kwargs: Any - ) -> _models.OpenAIFile: - """Uploads a file for use by other operations. - - :keyword file_path: Required. - :type file_path: str - :keyword purpose: Known values are: "fine-tune", "fine-tune-results", "assistants", - "assistants_output", "batch", "batch_output", and "vision". Required. - :paramtype purpose: str or ~azure.ai.projects.models.FilePurpose - :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.OpenAIFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - # pylint: disable=arguments-differ - @overload - async def upload_file( # pylint: disable=arguments-differ - 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: Required. - :paramtype file: ~azure.ai.projects._vendor.FileType - :keyword purpose: Known values are: "fine-tune", "fine-tune-results", "assistants", - "assistants_output", "batch", "batch_output", and "vision". Required. - :paramtype purpose: str or ~azure.ai.projects.models.FilePurpose - :keyword filename: Default value is None. - :paramtype filename: str - :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.OpenAIFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @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.projects.models.OpenAIFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace_async - async def upload_file( - self, - body: Optional[JSON] = None, - *, - file: Optional[FileType] = None, - file_path: Optional[str] = None, - purpose: Union[str, _models.FilePurpose, None] = None, - filename: Optional[str] = None, - **kwargs: Any, - ) -> _models.OpenAIFile: - """ - Uploads a file for use by other operations, delegating to the generated operations. - - :param body: JSON. Required if `file` and `purpose` are not provided. - :type body: Optional[JSON] - :keyword file: File content. Required if `body` and `purpose` are not provided. - :paramtype file: Optional[FileType] - :keyword file_path: Path to the file. Required if `body` and `purpose` are not provided. - :paramtype file_path: Optional[str] - :keyword purpose: Known values are: "fine-tune", "fine-tune-results", "assistants", - "assistants_output", "batch", "batch_output", and "vision". Required if `body` and `file` are not provided. - :paramtype purpose: Union[str, _models.FilePurpose, None] - :keyword filename: The name of the file. - :paramtype filename: Optional[str] - :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping - :rtype: _models.OpenAIFile - :raises FileNotFoundError: If the file_path is invalid. - :raises IOError: If there are issues with reading the file. - :raises: HttpResponseError for HTTP errors. - """ - if body is not None: - return await super().upload_file(body=body, **kwargs) - - if isinstance(purpose, FilePurpose): - purpose = purpose.value - - if file is not None and purpose is not None: - return await super().upload_file(file=file, purpose=purpose, filename=filename, **kwargs) - - if file_path is not None and purpose is not None: - if not os.path.isfile(file_path): - raise FileNotFoundError(f"The file path provided does not exist: {file_path}") - - try: - with open(file_path, "rb") as f: - content = f.read() - - # Determine filename and create correct FileType - base_filename = filename or os.path.basename(file_path) - file_content: FileType = (base_filename, content) - - return await super().upload_file(file=file_content, purpose=purpose, **kwargs) - except IOError as e: - raise IOError(f"Unable to read file: {file_path}.") from e - - raise ValueError("Invalid parameters for upload_file. Please provide the necessary arguments.") - - @overload - async def upload_file_and_poll(self, body: JSON, *, sleep_interval: float = 1, **kwargs: Any) -> _models.OpenAIFile: - """Uploads a file for use by other operations. - - :param body: Required. - :type body: JSON - :keyword sleep_interval: Time to wait before polling for the status of the uploaded file. Default value - is 1. - :paramtype sleep_interval: float - :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.OpenAIFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def upload_file_and_poll( - self, - *, - file: FileType, - purpose: Union[str, _models.FilePurpose], - filename: Optional[str] = None, - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.OpenAIFile: - """Uploads a file for use by other operations. - - :keyword file: Required. - :paramtype file: ~azure.ai.projects._vendor.FileType - :keyword purpose: Known values are: "fine-tune", "fine-tune-results", "assistants", - "assistants_output", "batch", "batch_output", and "vision". Required. - :paramtype purpose: str or ~azure.ai.projects.models.FilePurpose - :keyword filename: Default value is None. - :paramtype filename: str - :keyword sleep_interval: Time to wait before polling for the status of the uploaded file. Default value - is 1. - :paramtype sleep_interval: float - :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.OpenAIFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def upload_file_and_poll( - self, *, file_path: str, purpose: Union[str, _models.FilePurpose], sleep_interval: float = 1, **kwargs: Any - ) -> _models.OpenAIFile: - """Uploads a file for use by other operations. - - :keyword file_path: Required. - :type file_path: str - :keyword purpose: Known values are: "fine-tune", "fine-tune-results", "assistants", - "assistants_output", "batch", "batch_output", and "vision". Required. - :paramtype purpose: str or ~azure.ai.projects.models.FilePurpose - :keyword sleep_interval: Time to wait before polling for the status of the uploaded file. Default value - is 1. - :paramtype sleep_interval: float - :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.OpenAIFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace_async - async def upload_file_and_poll( - self, - body: Optional[JSON] = None, - *, - file: Optional[FileType] = None, - file_path: Optional[str] = None, - purpose: Union[str, _models.FilePurpose, None] = None, - filename: Optional[str] = None, - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.OpenAIFile: - """ - Uploads a file for use by other operations, delegating to the generated operations. - - :param body: JSON. Required if `file` and `purpose` are not provided. - :type body: Optional[JSON] - :keyword file: File content. Required if `body` and `purpose` are not provided. - :paramtype file: Optional[FileType] - :keyword file_path: Path to the file. Required if `body` and `purpose` are not provided. - :paramtype file_path: Optional[str] - :keyword purpose: Known values are: "fine-tune", "fine-tune-results", "assistants", - "assistants_output", "batch", "batch_output", and "vision". Required if `body` and `file` are not provided. - :paramtype purpose: Union[str, _models.FilePurpose, None] - :keyword filename: The name of the file. - :paramtype filename: Optional[str] - :keyword sleep_interval: Time to wait before polling for the status of the uploaded file. Default value - is 1. - :paramtype sleep_interval: float - :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping - :rtype: _models.OpenAIFile - :raises FileNotFoundError: If the file_path is invalid. - :raises IOError: If there are issues with reading the file. - :raises: HttpResponseError for HTTP errors. - """ - if body is not None: - uploaded_file = await self.upload_file(body=body, **kwargs) - elif file is not None and purpose is not None: - uploaded_file = await self.upload_file(file=file, purpose=purpose, filename=filename, **kwargs) - elif file_path is not None and purpose is not None: - uploaded_file = await self.upload_file(file_path=file_path, purpose=purpose, **kwargs) - else: - raise ValueError( - "Invalid parameters for upload_file_and_poll. Please provide either 'body', " - "or both 'file' and 'purpose', or both 'file_path' and 'purpose'." - ) - - while uploaded_file.status in ["uploaded", "pending", "running"]: - time.sleep(sleep_interval) - uploaded_file = await self.get_file(uploaded_file.id) - - return uploaded_file - - @overload - async def create_vector_store_and_poll( - self, body: JSON, *, content_type: str = "application/json", sleep_interval: float = 1, **kwargs: Any - ) -> _models.VectorStore: - """Creates a vector store and poll. - - :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 - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStore. The VectorStore is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStore - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def create_vector_store_and_poll( - self, - *, - content_type: str = "application/json", - file_ids: Optional[List[str]] = None, - name: Optional[str] = None, - data_sources: Optional[List[_models.VectorStoreDataSource]] = None, - expires_after: Optional[_models.VectorStoreExpirationPolicy] = None, - chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, - metadata: Optional[Dict[str, str]] = None, - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.VectorStore: - """Creates a vector store and poll. - - :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. - Default value is "application/json". - :paramtype content_type: 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. Default value is None. - :paramtype file_ids: list[str] - :keyword name: The name of the vector store. Default value is None. - :paramtype name: str - :keyword data_sources: List of Azure assets. Default value is None. - :paramtype data_sources: list[~azure.ai.projects.models.VectorStoreDataSource] - :keyword expires_after: Details on when this vector store expires. Default value is None. - :paramtype expires_after: ~azure.ai.projects.models.VectorStoreExpirationPolicy - :keyword 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. Default value is None. - :paramtype chunking_strategy: ~azure.ai.projects.models.VectorStoreChunkingStrategyRequest - :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] - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStore. The VectorStore is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStore - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def create_vector_store_and_poll( - self, body: IO[bytes], *, content_type: str = "application/json", sleep_interval: float = 1, **kwargs: Any - ) -> _models.VectorStore: - """Creates a vector store and poll. - - :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 - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStore. The VectorStore is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStore - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace_async - async def create_vector_store_and_poll( - self, - body: Union[JSON, IO[bytes]] = _Unset, - *, - content_type: str = "application/json", - file_ids: Optional[List[str]] = None, - name: Optional[str] = None, - data_sources: Optional[List[_models.VectorStoreDataSource]] = None, - expires_after: Optional[_models.VectorStoreExpirationPolicy] = None, - chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, - metadata: Optional[Dict[str, str]] = None, - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.VectorStore: - """Creates a vector store and poll. - - :param body: Is either a JSON type or a IO[bytes] type. Required. - :type body: JSON or IO[bytes] - :keyword content_type: Body Parameter content-type. Content type parameter for binary body. - Default value is "application/json". - :paramtype content_type: 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. Default value is None. - :paramtype file_ids: list[str] - :keyword name: The name of the vector store. Default value is None. - :paramtype name: str - :keyword data_sources: List of Azure assets. Default value is None. - :paramtype data_sources: list[~azure.ai.projects.models.VectorStoreDataSource] - :keyword expires_after: Details on when this vector store expires. Default value is None. - :paramtype expires_after: ~azure.ai.projects.models.VectorStoreExpirationPolicy - :keyword 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. Default value is None. - :paramtype chunking_strategy: ~azure.ai.projects.models.VectorStoreChunkingStrategyRequest - :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] - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStore. The VectorStore is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStore - :raises ~azure.core.exceptions.HttpResponseError: - """ - - if body is not _Unset: - if isinstance(body, dict): - vector_store = await super().create_vector_store( - body=body, content_type=content_type or "application/json", **kwargs - ) - elif isinstance(body, io.IOBase): - vector_store = await super().create_vector_store(body=body, content_type=content_type, **kwargs) - else: - raise ValueError("Invalid 'body' type: must be a dictionary (JSON) or a file-like object (IO[bytes]).") - else: - store_configuration = None - if data_sources: - store_configuration = _models.VectorStoreConfiguration(data_sources=data_sources) - - vector_store = await super().create_vector_store( - file_ids=file_ids, - store_configuration=store_configuration, - name=name, - expires_after=expires_after, - chunking_strategy=chunking_strategy, - metadata=metadata, - **kwargs, - ) - - while vector_store.status == "in_progress": - time.sleep(sleep_interval) - vector_store = await super().get_vector_store(vector_store.id) - - return vector_store - - @overload - async def create_vector_store_file_batch_and_poll( - self, - vector_store_id: str, - body: JSON, - *, - content_type: str = "application/json", - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.VectorStoreFileBatch: - """Create a vector store file batch and poll. - - :param vector_store_id: Identifier of the vector store. 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 - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStoreFileBatch - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def create_vector_store_file_batch_and_poll( - self, - vector_store_id: str, - *, - file_ids: Optional[List[str]] = None, - data_sources: Optional[List[_models.VectorStoreDataSource]] = None, - content_type: str = "application/json", - chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.VectorStoreFileBatch: - """Create a vector store file batch and poll. - - :param vector_store_id: Identifier of the vector store. Required. - :type vector_store_id: str - :keyword file_ids: List of file identifiers. Required. - :paramtype file_ids: list[str] - :keyword data_sources: List of Azure assets. Default value is None. - :paramtype data_sources: list[~azure.ai.projects.models.VectorStoreDataSource] - :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.projects.models.VectorStoreChunkingStrategyRequest - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStoreFileBatch - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def create_vector_store_file_batch_and_poll( - self, - vector_store_id: str, - body: IO[bytes], - *, - content_type: str = "application/json", - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.VectorStoreFileBatch: - """Create a vector store file batch and poll. - - :param vector_store_id: Identifier of the vector store. 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 - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStoreFileBatch - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace_async - async def create_vector_store_file_batch_and_poll( - self, - vector_store_id: str, - body: Union[JSON, IO[bytes]] = _Unset, - *, - file_ids: Optional[List[str]] = None, - data_sources: Optional[List[_models.VectorStoreDataSource]] = None, - chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, - content_type: str = "application/json", - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.VectorStoreFileBatch: - """Create a vector store file batch and poll. - - :param vector_store_id: Identifier of the vector store. 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: List of file identifiers. Required. - :paramtype file_ids: list[str] - :keyword data_sources: List of Azure assets. Default value is None. - :paramtype data_sources: list[~azure.ai.client.models.VectorStoreDataSource] - :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.projects.models.VectorStoreChunkingStrategyRequest - :keyword content_type: Body parameter content-type. Defaults to "application/json". - :paramtype content_type: str - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStoreFileBatch - :raises ~azure.core.exceptions.HttpResponseError: - """ - - if body is not _Unset: - if isinstance(body, dict): - vector_store_file_batch = await super().create_vector_store_file_batch( - vector_store_id=vector_store_id, - body=body, - content_type=content_type or "application/json", - **kwargs, - ) - elif isinstance(body, io.IOBase): - vector_store_file_batch = await super().create_vector_store_file_batch( - vector_store_id=vector_store_id, - body=body, - content_type=content_type, - **kwargs, - ) - else: - raise ValueError("Invalid type for 'body'. Must be a dict (JSON) or file-like (IO[bytes]).") - else: - vector_store_file_batch = await super().create_vector_store_file_batch( - vector_store_id=vector_store_id, - file_ids=file_ids, - data_sources=data_sources, - chunking_strategy=chunking_strategy, - **kwargs, - ) - - while vector_store_file_batch.status == "in_progress": - time.sleep(sleep_interval) - vector_store_file_batch = await super().get_vector_store_file_batch( - vector_store_id=vector_store_id, batch_id=vector_store_file_batch.id - ) - - return vector_store_file_batch - - @overload - async def create_vector_store_file_and_poll( - self, - vector_store_id: str, - body: JSON, - *, - content_type: str = "application/json", - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.VectorStoreFile: - """Create a vector store file by attaching a file to a vector store. - - :param vector_store_id: Identifier of the vector store. 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 - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStoreFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def create_vector_store_file_and_poll( - self, - vector_store_id: str, - *, - content_type: str = "application/json", - file_id: Optional[str] = None, - data_source: Optional[_models.VectorStoreDataSource] = None, - chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.VectorStoreFile: - """Create a vector store file by attaching a file to a vector store. - - :param vector_store_id: Identifier of the vector store. Required. - :type vector_store_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 file_id: Identifier of the file. Default value is None. - :paramtype file_id: str - :keyword data_source: Azure asset ID. Default value is None. - :paramtype data_source: ~azure.ai.projects.models.VectorStoreDataSource - :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.projects.models.VectorStoreChunkingStrategyRequest - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStoreFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def create_vector_store_file_and_poll( - self, - vector_store_id: str, - body: IO[bytes], - *, - content_type: str = "application/json", - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.VectorStoreFile: - """Create a vector store file by attaching a file to a vector store. - - :param vector_store_id: Identifier of the vector store. 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 - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStoreFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace_async - async def create_vector_store_file_and_poll( - self, - vector_store_id: str, - body: Union[JSON, IO[bytes]] = _Unset, - *, - content_type: str = "application/json", - file_id: Optional[str] = None, - data_source: Optional[_models.VectorStoreDataSource] = None, - chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.VectorStoreFile: - """Create a vector store file by attaching a file to a vector store. - - :param vector_store_id: Identifier of the vector store. 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 content_type: Body Parameter content-type. Defaults to 'application/json'. - :paramtype content_type: str - :keyword file_id: Identifier of the file. Default value is None. - :paramtype file_id: str - :keyword data_source: Azure asset ID. Default value is None. - :paramtype data_source: ~azure.ai.projects.models.VectorStoreDataSource - :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.projects.models.VectorStoreChunkingStrategyRequest - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStoreFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - if body is not _Unset: - if isinstance(body, dict): - vector_store_file = await super().create_vector_store_file( - vector_store_id=vector_store_id, - body=body, - content_type=content_type or "application/json", - **kwargs, - ) - elif isinstance(body, io.IOBase): - vector_store_file = await super().create_vector_store_file( - vector_store_id=vector_store_id, - body=body, - content_type=content_type, - **kwargs, - ) - else: - raise ValueError("Invalid type for 'body'. Must be a dict (JSON) or file-like object (IO[bytes]).") - else: - vector_store_file = await super().create_vector_store_file( - vector_store_id=vector_store_id, - file_id=file_id, - data_source=data_source, - chunking_strategy=chunking_strategy, - **kwargs, - ) - - while vector_store_file.status == "in_progress": - time.sleep(sleep_interval) - vector_store_file = await super().get_vector_store_file( - vector_store_id=vector_store_id, file_id=vector_store_file.id - ) - - return vector_store_file - - @distributed_trace_async - async def get_file_content(self, file_id: str, **kwargs: Any) -> AsyncIterator[bytes]: - """ - Asynchronously returns file content as a byte stream for the given file_id. - - :param file_id: The ID of the file to retrieve. Required. - :type file_id: str - :return: An async iterator that yields bytes from the file content. - :rtype: AsyncIterator[bytes] - :raises ~azure.core.exceptions.HttpResponseError: If the HTTP request fails. - """ - kwargs["stream"] = True - response = await super()._get_file_content(file_id, **kwargs) - return cast(AsyncIterator[bytes], response) - - @distributed_trace_async - async def save_file(self, file_id: str, file_name: str, target_dir: Optional[Union[str, Path]] = None) -> None: - """ - Asynchronously saves file content retrieved using a file identifier to the specified local directory. - - :param file_id: The unique identifier for the file to retrieve. - :type file_id: str - :param file_name: The name of the file to be saved. - :type file_name: str - :param target_dir: The directory where the file should be saved. Defaults to the current working directory. - :type target_dir: str or Path - :raises ValueError: If the target path is not a directory or the file name is invalid. - :raises RuntimeError: If file content retrieval fails or no content is found. - :raises TypeError: If retrieved chunks are not bytes-like objects. - :raises IOError: If writing to the file fails. - """ - try: - # Determine and validate the target directory - path = Path(target_dir).expanduser().resolve() if target_dir else Path.cwd() - path.mkdir(parents=True, exist_ok=True) - if not path.is_dir(): - raise ValueError(f"The target path '{path}' is not a directory.") - - # Sanitize and validate the file name - sanitized_file_name = Path(file_name).name - if not sanitized_file_name: - raise ValueError("The provided file name is invalid.") - - # Retrieve the file content - file_content_stream = await self.get_file_content(file_id) - if not file_content_stream: - raise RuntimeError(f"No content retrievable for file ID '{file_id}'.") - - # Collect all chunks asynchronously - chunks = [] - async for chunk in file_content_stream: - if isinstance(chunk, (bytes, bytearray)): - chunks.append(chunk) - else: - raise TypeError(f"Expected bytes or bytearray, got {type(chunk).__name__}") - - target_file_path = path / sanitized_file_name - - # Write the collected content to the file synchronously - def write_file(collected_chunks: list): - with open(target_file_path, "wb") as file: - for chunk in collected_chunks: - file.write(chunk) - - # Use the event loop to run the synchronous function in a thread executor - loop = asyncio.get_running_loop() - await loop.run_in_executor(None, write_file, chunks) - - logger.debug("File '%s' saved successfully at '%s'.", sanitized_file_name, target_file_path) - - except (ValueError, RuntimeError, TypeError, IOError) as e: - logger.error("An error occurred in save_file: %s", e) - raise - - @distributed_trace_async - async def delete_agent(self, assistant_id: str, **kwargs: Any) -> _models.AgentDeletionStatus: - """Deletes an agent. - - :param assistant_id: Identifier of the agent. Required. - :type assistant_id: str - :return: AgentDeletionStatus. The AgentDeletionStatus is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.AgentDeletionStatus - :raises ~azure.core.exceptions.HttpResponseError: - """ - if assistant_id in self._toolset: - del self._toolset[assistant_id] - return await super().delete_agent(assistant_id, **kwargs) - - -class _SyncCredentialWrapper(TokenCredential): - """ - The class, synchronizing AsyncTokenCredential. - - :param async_credential: The async credential to be synchronized. - :type async_credential: ~azure.core.credentials_async.AsyncTokenCredential - """ - - def __init__(self, async_credential: "AsyncTokenCredential"): - self._async_credential = async_credential - - def get_token( - self, - *scopes: str, - claims: Optional[str] = None, - tenant_id: Optional[str] = None, - enable_cae: bool = False, - **kwargs: Any, - ) -> "AccessToken": - - pool = concurrent.futures.ThreadPoolExecutor() - return pool.submit( - asyncio.run, - self._async_credential.get_token( - *scopes, - claims=claims, - tenant_id=tenant_id, - enable_cae=enable_cae, - **kwargs, - ), - ).result() - - -__all__: List[str] = [ - "AgentsOperations", - "ConnectionsOperations", - "TelemetryOperations", - "InferenceOperations", -] # Add all objects you want publicly available to users at this package level +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level def patch_sdk(): diff --git a/sdk/ai/azure-ai-projects/azure/ai/projects/models/__init__.py b/sdk/ai/azure-ai-projects/azure/ai/projects/models/__init__.py index aa46cc2d6073..2fe74c084a19 100644 --- a/sdk/ai/azure-ai-projects/azure/ai/projects/models/__init__.py +++ b/sdk/ai/azure-ai-projects/azure/ai/projects/models/__init__.py @@ -14,205 +14,23 @@ from ._models import ( # type: ignore - Agent, - AgentDeletionStatus, - AgentThread, - AgentThreadCreationOptions, - AgentsApiResponseFormat, - AgentsNamedToolChoice, ApplicationInsightsConfiguration, - AzureAISearchResource, - AzureAISearchToolDefinition, - AzureFunctionBinding, - AzureFunctionDefinition, - AzureFunctionStorageQueue, - AzureFunctionToolDefinition, - BingGroundingToolDefinition, - CodeInterpreterToolDefinition, - CodeInterpreterToolResource, CronTrigger, Dataset, Evaluation, EvaluationSchedule, EvaluatorConfiguration, - FileDeletionStatus, - FileListResponse, - FileSearchRankingOptions, - FileSearchToolCallContent, - FileSearchToolDefinition, - FileSearchToolDefinitionDetails, - FileSearchToolResource, - FunctionDefinition, - FunctionName, - FunctionToolDefinition, - IncompleteRunDetails, - IndexResource, InputData, - MessageAttachment, - MessageContent, - MessageDelta, - MessageDeltaChunk, - MessageDeltaContent, - MessageDeltaImageFileContent, - MessageDeltaImageFileContentObject, - MessageDeltaTextAnnotation, - MessageDeltaTextContent, - MessageDeltaTextContentObject, - MessageDeltaTextFileCitationAnnotation, - MessageDeltaTextFileCitationAnnotationObject, - MessageDeltaTextFilePathAnnotation, - MessageDeltaTextFilePathAnnotationObject, - MessageImageFileContent, - MessageImageFileDetails, - MessageIncompleteDetails, - MessageTextAnnotation, - MessageTextContent, - MessageTextDetails, - MessageTextFileCitationAnnotation, - MessageTextFileCitationDetails, - MessageTextFilePathAnnotation, - MessageTextFilePathDetails, - MicrosoftFabricToolDefinition, - OpenAIFile, - OpenAIPageableListOfAgent, - OpenAIPageableListOfRunStep, - OpenAIPageableListOfThreadMessage, - OpenAIPageableListOfThreadRun, - OpenAIPageableListOfVectorStore, - OpenAIPageableListOfVectorStoreFile, - OpenApiAnonymousAuthDetails, - OpenApiAuthDetails, - OpenApiConnectionAuthDetails, - OpenApiConnectionSecurityScheme, - OpenApiFunctionDefinition, - OpenApiManagedAuthDetails, - OpenApiManagedSecurityScheme, - OpenApiToolDefinition, RecurrenceSchedule, RecurrenceTrigger, - RequiredAction, - RequiredFunctionToolCall, - RequiredFunctionToolCallDetails, - RequiredToolCall, - ResponseFormatJsonSchema, - ResponseFormatJsonSchemaType, - RunCompletionUsage, - RunError, - RunStep, - RunStepAzureAISearchToolCall, - RunStepBingGroundingToolCall, - RunStepCodeInterpreterImageOutput, - RunStepCodeInterpreterImageReference, - RunStepCodeInterpreterLogOutput, - RunStepCodeInterpreterToolCall, - RunStepCodeInterpreterToolCallDetails, - RunStepCodeInterpreterToolCallOutput, - RunStepCompletionUsage, - RunStepDelta, - RunStepDeltaChunk, - RunStepDeltaCodeInterpreterDetailItemObject, - RunStepDeltaCodeInterpreterImageOutput, - RunStepDeltaCodeInterpreterImageOutputObject, - RunStepDeltaCodeInterpreterLogOutput, - RunStepDeltaCodeInterpreterOutput, - RunStepDeltaCodeInterpreterToolCall, - RunStepDeltaDetail, - RunStepDeltaFileSearchToolCall, - RunStepDeltaFunction, - RunStepDeltaFunctionToolCall, - RunStepDeltaMessageCreation, - RunStepDeltaMessageCreationObject, - RunStepDeltaToolCall, - RunStepDeltaToolCallObject, - RunStepDetails, - RunStepError, - RunStepFileSearchToolCall, - RunStepFileSearchToolCallResult, - RunStepFileSearchToolCallResults, - RunStepFunctionToolCall, - RunStepFunctionToolCallDetails, - RunStepMessageCreationDetails, - RunStepMessageCreationReference, - RunStepMicrosoftFabricToolCall, - RunStepSharepointToolCall, - RunStepToolCall, - RunStepToolCallDetails, - SharepointToolDefinition, - SubmitToolOutputsAction, - SubmitToolOutputsDetails, SystemData, - ThreadDeletionStatus, - ThreadMessage, - ThreadMessageOptions, - ThreadRun, - ToolConnection, - ToolConnectionList, - ToolDefinition, - ToolOutput, - ToolResources, Trigger, - TruncationObject, - UpdateCodeInterpreterToolResourceOptions, - UpdateFileSearchToolResourceOptions, - UpdateToolResourcesOptions, - VectorStore, - VectorStoreAutoChunkingStrategyRequest, - VectorStoreAutoChunkingStrategyResponse, - VectorStoreChunkingStrategyRequest, - VectorStoreChunkingStrategyResponse, - VectorStoreConfiguration, - VectorStoreConfigurations, - VectorStoreDataSource, - VectorStoreDeletionStatus, - VectorStoreExpirationPolicy, - VectorStoreFile, - VectorStoreFileBatch, - VectorStoreFileCount, - VectorStoreFileDeletionStatus, - VectorStoreFileError, - VectorStoreStaticChunkingStrategyOptions, - VectorStoreStaticChunkingStrategyRequest, - VectorStoreStaticChunkingStrategyResponse, ) from ._enums import ( # type: ignore - AgentStreamEvent, - AgentsApiResponseFormatMode, - AgentsApiToolChoiceOptionMode, - AgentsNamedToolChoiceType, AuthenticationType, ConnectionType, - DoneEvent, - ErrorEvent, - FilePurpose, - FileState, Frequency, - IncompleteDetailsReason, - ListSortOrder, - MessageIncompleteDetailsReason, - MessageRole, - MessageStatus, - MessageStreamEvent, - OpenApiAuthType, - ResponseFormat, - RunAdditionalFieldList, - RunStatus, - RunStepErrorCode, - RunStepStatus, - RunStepStreamEvent, - RunStepType, - RunStreamEvent, - ThreadStreamEvent, - TruncationStrategy, - VectorStoreChunkingStrategyRequestType, - VectorStoreChunkingStrategyResponseType, - VectorStoreDataSourceAssetType, - VectorStoreExpirationPolicyAnchor, - VectorStoreFileBatchStatus, - VectorStoreFileErrorCode, - VectorStoreFileStatus, - VectorStoreFileStatusFilter, - VectorStoreStatus, WeekDays, ) from ._patch import __all__ as _patch_all @@ -220,202 +38,20 @@ from ._patch import patch_sdk as _patch_sdk __all__ = [ - "Agent", - "AgentDeletionStatus", - "AgentThread", - "AgentThreadCreationOptions", - "AgentsApiResponseFormat", - "AgentsNamedToolChoice", "ApplicationInsightsConfiguration", - "AzureAISearchResource", - "AzureAISearchToolDefinition", - "AzureFunctionBinding", - "AzureFunctionDefinition", - "AzureFunctionStorageQueue", - "AzureFunctionToolDefinition", - "BingGroundingToolDefinition", - "CodeInterpreterToolDefinition", - "CodeInterpreterToolResource", "CronTrigger", "Dataset", "Evaluation", "EvaluationSchedule", "EvaluatorConfiguration", - "FileDeletionStatus", - "FileListResponse", - "FileSearchRankingOptions", - "FileSearchToolCallContent", - "FileSearchToolDefinition", - "FileSearchToolDefinitionDetails", - "FileSearchToolResource", - "FunctionDefinition", - "FunctionName", - "FunctionToolDefinition", - "IncompleteRunDetails", - "IndexResource", "InputData", - "MessageAttachment", - "MessageContent", - "MessageDelta", - "MessageDeltaChunk", - "MessageDeltaContent", - "MessageDeltaImageFileContent", - "MessageDeltaImageFileContentObject", - "MessageDeltaTextAnnotation", - "MessageDeltaTextContent", - "MessageDeltaTextContentObject", - "MessageDeltaTextFileCitationAnnotation", - "MessageDeltaTextFileCitationAnnotationObject", - "MessageDeltaTextFilePathAnnotation", - "MessageDeltaTextFilePathAnnotationObject", - "MessageImageFileContent", - "MessageImageFileDetails", - "MessageIncompleteDetails", - "MessageTextAnnotation", - "MessageTextContent", - "MessageTextDetails", - "MessageTextFileCitationAnnotation", - "MessageTextFileCitationDetails", - "MessageTextFilePathAnnotation", - "MessageTextFilePathDetails", - "MicrosoftFabricToolDefinition", - "OpenAIFile", - "OpenAIPageableListOfAgent", - "OpenAIPageableListOfRunStep", - "OpenAIPageableListOfThreadMessage", - "OpenAIPageableListOfThreadRun", - "OpenAIPageableListOfVectorStore", - "OpenAIPageableListOfVectorStoreFile", - "OpenApiAnonymousAuthDetails", - "OpenApiAuthDetails", - "OpenApiConnectionAuthDetails", - "OpenApiConnectionSecurityScheme", - "OpenApiFunctionDefinition", - "OpenApiManagedAuthDetails", - "OpenApiManagedSecurityScheme", - "OpenApiToolDefinition", "RecurrenceSchedule", "RecurrenceTrigger", - "RequiredAction", - "RequiredFunctionToolCall", - "RequiredFunctionToolCallDetails", - "RequiredToolCall", - "ResponseFormatJsonSchema", - "ResponseFormatJsonSchemaType", - "RunCompletionUsage", - "RunError", - "RunStep", - "RunStepAzureAISearchToolCall", - "RunStepBingGroundingToolCall", - "RunStepCodeInterpreterImageOutput", - "RunStepCodeInterpreterImageReference", - "RunStepCodeInterpreterLogOutput", - "RunStepCodeInterpreterToolCall", - "RunStepCodeInterpreterToolCallDetails", - "RunStepCodeInterpreterToolCallOutput", - "RunStepCompletionUsage", - "RunStepDelta", - "RunStepDeltaChunk", - "RunStepDeltaCodeInterpreterDetailItemObject", - "RunStepDeltaCodeInterpreterImageOutput", - "RunStepDeltaCodeInterpreterImageOutputObject", - "RunStepDeltaCodeInterpreterLogOutput", - "RunStepDeltaCodeInterpreterOutput", - "RunStepDeltaCodeInterpreterToolCall", - "RunStepDeltaDetail", - "RunStepDeltaFileSearchToolCall", - "RunStepDeltaFunction", - "RunStepDeltaFunctionToolCall", - "RunStepDeltaMessageCreation", - "RunStepDeltaMessageCreationObject", - "RunStepDeltaToolCall", - "RunStepDeltaToolCallObject", - "RunStepDetails", - "RunStepError", - "RunStepFileSearchToolCall", - "RunStepFileSearchToolCallResult", - "RunStepFileSearchToolCallResults", - "RunStepFunctionToolCall", - "RunStepFunctionToolCallDetails", - "RunStepMessageCreationDetails", - "RunStepMessageCreationReference", - "RunStepMicrosoftFabricToolCall", - "RunStepSharepointToolCall", - "RunStepToolCall", - "RunStepToolCallDetails", - "SharepointToolDefinition", - "SubmitToolOutputsAction", - "SubmitToolOutputsDetails", "SystemData", - "ThreadDeletionStatus", - "ThreadMessage", - "ThreadMessageOptions", - "ThreadRun", - "ToolConnection", - "ToolConnectionList", - "ToolDefinition", - "ToolOutput", - "ToolResources", "Trigger", - "TruncationObject", - "UpdateCodeInterpreterToolResourceOptions", - "UpdateFileSearchToolResourceOptions", - "UpdateToolResourcesOptions", - "VectorStore", - "VectorStoreAutoChunkingStrategyRequest", - "VectorStoreAutoChunkingStrategyResponse", - "VectorStoreChunkingStrategyRequest", - "VectorStoreChunkingStrategyResponse", - "VectorStoreConfiguration", - "VectorStoreConfigurations", - "VectorStoreDataSource", - "VectorStoreDeletionStatus", - "VectorStoreExpirationPolicy", - "VectorStoreFile", - "VectorStoreFileBatch", - "VectorStoreFileCount", - "VectorStoreFileDeletionStatus", - "VectorStoreFileError", - "VectorStoreStaticChunkingStrategyOptions", - "VectorStoreStaticChunkingStrategyRequest", - "VectorStoreStaticChunkingStrategyResponse", - "AgentStreamEvent", - "AgentsApiResponseFormatMode", - "AgentsApiToolChoiceOptionMode", - "AgentsNamedToolChoiceType", "AuthenticationType", "ConnectionType", - "DoneEvent", - "ErrorEvent", - "FilePurpose", - "FileState", "Frequency", - "IncompleteDetailsReason", - "ListSortOrder", - "MessageIncompleteDetailsReason", - "MessageRole", - "MessageStatus", - "MessageStreamEvent", - "OpenApiAuthType", - "ResponseFormat", - "RunAdditionalFieldList", - "RunStatus", - "RunStepErrorCode", - "RunStepStatus", - "RunStepStreamEvent", - "RunStepType", - "RunStreamEvent", - "ThreadStreamEvent", - "TruncationStrategy", - "VectorStoreChunkingStrategyRequestType", - "VectorStoreChunkingStrategyResponseType", - "VectorStoreDataSourceAssetType", - "VectorStoreExpirationPolicyAnchor", - "VectorStoreFileBatchStatus", - "VectorStoreFileErrorCode", - "VectorStoreFileStatus", - "VectorStoreFileStatusFilter", - "VectorStoreStatus", "WeekDays", ] __all__.extend([p for p in _patch_all if p not in __all__]) # pyright: ignore diff --git a/sdk/ai/azure-ai-projects/azure/ai/projects/models/_enums.py b/sdk/ai/azure-ai-projects/azure/ai/projects/models/_enums.py index 6ed9eb9b3162..73859548cb65 100644 --- a/sdk/ai/azure-ai-projects/azure/ai/projects/models/_enums.py +++ b/sdk/ai/azure-ai-projects/azure/ai/projects/models/_enums.py @@ -10,121 +10,6 @@ from azure.core import CaseInsensitiveEnumMeta -class AgentsApiResponseFormatMode(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 AgentsApiToolChoiceOptionMode(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 AgentsNamedToolChoiceType(str, Enum, metaclass=CaseInsensitiveEnumMeta): - """Available tool types for agents named tools.""" - - FUNCTION = "function" - """Tool type ``function``""" - CODE_INTERPRETER = "code_interpreter" - """Tool type ``code_interpreter``""" - FILE_SEARCH = "file_search" - """Tool type ``file_search``""" - BING_GROUNDING = "bing_grounding" - """Tool type ``bing_grounding``""" - MICROSOFT_FABRIC = "fabric_aiskill" - """Tool type ``fabric_aiskill``""" - SHAREPOINT = "sharepoint_grounding" - """Tool type ``sharepoint_grounding``""" - AZURE_AI_SEARCH = "azure_ai_search" - """Tool type ``azure_ai_search``""" - - -class AgentStreamEvent(str, Enum, metaclass=CaseInsensitiveEnumMeta): - """Each event in a server-sent events stream has an ``event`` and ``data`` property: - - .. code-block:: - - event: thread.created - data: {"id": "thread_123", "object": "thread", ...} - - We emit events whenever a new object is created, transitions to a new state, or is being - streamed in parts (deltas). For example, we emit ``thread.run.created`` when a new run - is created, ``thread.run.completed`` when a run completes, and so on. When an Agent chooses - to create a message during a run, we emit a ``thread.message.created event``\\ , a - ``thread.message.in_progress`` event, many ``thread.message.delta`` events, and finally a - ``thread.message.completed`` event. - - We may add additional events over time, so we recommend handling unknown events gracefully - in your code. - """ - - THREAD_CREATED = "thread.created" - """Event sent when a new thread is created. The data of this event is of type AgentThread""" - THREAD_RUN_CREATED = "thread.run.created" - """Event sent when a new run is created. The data of this event is of type ThreadRun""" - THREAD_RUN_QUEUED = "thread.run.queued" - """Event sent when a run moves to ``queued`` status. The data of this event is of type ThreadRun""" - THREAD_RUN_IN_PROGRESS = "thread.run.in_progress" - """Event sent when a run moves to ``in_progress`` status. The data of this event is of type - ThreadRun""" - THREAD_RUN_REQUIRES_ACTION = "thread.run.requires_action" - """Event sent when a run moves to ``requires_action`` status. The data of this event is of type - ThreadRun""" - THREAD_RUN_COMPLETED = "thread.run.completed" - """Event sent when a run is completed. The data of this event is of type ThreadRun""" - THREAD_RUN_INCOMPLETE = "thread.run.incomplete" - """Event sent when a run ends incomplete. The data of this event is of type ThreadRun""" - THREAD_RUN_FAILED = "thread.run.failed" - """Event sent when a run fails. The data of this event is of type ThreadRun""" - THREAD_RUN_CANCELLING = "thread.run.cancelling" - """Event sent when a run moves to ``cancelling`` status. The data of this event is of type - ThreadRun""" - THREAD_RUN_CANCELLED = "thread.run.cancelled" - """Event sent when a run is cancelled. The data of this event is of type ThreadRun""" - THREAD_RUN_EXPIRED = "thread.run.expired" - """Event sent when a run is expired. The data of this event is of type ThreadRun""" - THREAD_RUN_STEP_CREATED = "thread.run.step.created" - """Event sent when a new thread run step is created. The data of this event is of type RunStep""" - THREAD_RUN_STEP_IN_PROGRESS = "thread.run.step.in_progress" - """Event sent when a run step moves to ``in_progress`` status. The data of this event is of type - RunStep""" - THREAD_RUN_STEP_DELTA = "thread.run.step.delta" - """Event sent when a run step is being streamed. The data of this event is of type - RunStepDeltaChunk""" - THREAD_RUN_STEP_COMPLETED = "thread.run.step.completed" - """Event sent when a run step is completed. The data of this event is of type RunStep""" - THREAD_RUN_STEP_FAILED = "thread.run.step.failed" - """Event sent when a run step fails. The data of this event is of type RunStep""" - THREAD_RUN_STEP_CANCELLED = "thread.run.step.cancelled" - """Event sent when a run step is cancelled. The data of this event is of type RunStep""" - THREAD_RUN_STEP_EXPIRED = "thread.run.step.expired" - """Event sent when a run step is expired. The data of this event is of type RunStep""" - THREAD_MESSAGE_CREATED = "thread.message.created" - """Event sent when a new message is created. The data of this event is of type ThreadMessage""" - THREAD_MESSAGE_IN_PROGRESS = "thread.message.in_progress" - """Event sent when a message moves to ``in_progress`` status. The data of this event is of type - ThreadMessage""" - THREAD_MESSAGE_DELTA = "thread.message.delta" - """Event sent when a message is being streamed. The data of this event is of type - MessageDeltaChunk""" - THREAD_MESSAGE_COMPLETED = "thread.message.completed" - """Event sent when a message is completed. The data of this event is of type ThreadMessage""" - THREAD_MESSAGE_INCOMPLETE = "thread.message.incomplete" - """Event sent before a message is completed. The data of this event is of type ThreadMessage""" - ERROR = "error" - """Event sent when an error occurs, such as an internal server error or a timeout.""" - DONE = "done" - """Event sent when the stream is done.""" - - class AuthenticationType(str, Enum, metaclass=CaseInsensitiveEnumMeta): """Authentication type used by Azure AI service to connect to another service.""" @@ -151,67 +36,8 @@ class ConnectionType(str, Enum, metaclass=CaseInsensitiveEnumMeta): """Azure AI Services""" AZURE_AI_SEARCH = "CognitiveSearch" """Azure AI Search""" - - -class DoneEvent(str, Enum, metaclass=CaseInsensitiveEnumMeta): - """Terminal event indicating the successful end of a stream.""" - - DONE = "done" - """Event sent when the stream is done.""" - - -class ErrorEvent(str, Enum, metaclass=CaseInsensitiveEnumMeta): - """Terminal event indicating a server side error while streaming.""" - - ERROR = "error" - """Event sent when an error occurs, such as an internal server error or a timeout.""" - - -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.""" - AGENTS = "assistants" - """Indicates a file is used as input to agents.""" - AGENTS_OUTPUT = "assistants_output" - """Indicates a file is used as output by agents.""" - BATCH = "batch" - """Indicates a file is used as input to .""" - 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.""" + API_KEY = "ApiKey" + """Generic connection that uses API Key authentication""" class Frequency(str, Enum, metaclass=CaseInsensitiveEnumMeta): @@ -224,322 +50,6 @@ class Frequency(str, Enum, metaclass=CaseInsensitiveEnumMeta): MINUTE = "Minute" -class IncompleteDetailsReason(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 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 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.""" - AGENT = "assistant" - """The role representing the agent.""" - - -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 MessageStreamEvent(str, Enum, metaclass=CaseInsensitiveEnumMeta): - """Message operation related streaming events.""" - - THREAD_MESSAGE_CREATED = "thread.message.created" - """Event sent when a new message is created. The data of this event is of type ThreadMessage""" - THREAD_MESSAGE_IN_PROGRESS = "thread.message.in_progress" - """Event sent when a message moves to ``in_progress`` status. The data of this event is of type - ThreadMessage""" - THREAD_MESSAGE_DELTA = "thread.message.delta" - """Event sent when a message is being streamed. The data of this event is of type - MessageDeltaChunk""" - THREAD_MESSAGE_COMPLETED = "thread.message.completed" - """Event sent when a message is completed. The data of this event is of type ThreadMessage""" - THREAD_MESSAGE_INCOMPLETE = "thread.message.incomplete" - """Event sent before a message is completed. The data of this event is of type ThreadMessage""" - - -class OpenApiAuthType(str, Enum, metaclass=CaseInsensitiveEnumMeta): - """Authentication type for OpenApi endpoint. Allowed types are: - - - * Anonymous (no authentication required) - * Connection (requires connection_id to endpoint, as setup in AI Foundry) - * Managed_Identity (requires audience for identity based auth). - """ - - ANONYMOUS = "anonymous" - CONNECTION = "connection" - MANAGED_IDENTITY = "managed_identity" - - -class ResponseFormat(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 RunAdditionalFieldList(str, Enum, metaclass=CaseInsensitiveEnumMeta): - """A list of additional fields to include in the response.""" - - FILE_SEARCH_CONTENTS = "step_details.tool_calls[*].file_search.results[*].content" - """File search result content.""" - - -class RunStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta): - """Possible values for the status of an agent 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 RunStepStreamEvent(str, Enum, metaclass=CaseInsensitiveEnumMeta): - """Run step operation related streaming events.""" - - THREAD_RUN_STEP_CREATED = "thread.run.step.created" - """Event sent when a new thread run step is created. The data of this event is of type RunStep""" - THREAD_RUN_STEP_IN_PROGRESS = "thread.run.step.in_progress" - """Event sent when a run step moves to ``in_progress`` status. The data of this event is of type - RunStep""" - THREAD_RUN_STEP_DELTA = "thread.run.step.delta" - """Event sent when a run step is being streamed. The data of this event is of type - RunStepDeltaChunk""" - THREAD_RUN_STEP_COMPLETED = "thread.run.step.completed" - """Event sent when a run step is completed. The data of this event is of type RunStep""" - THREAD_RUN_STEP_FAILED = "thread.run.step.failed" - """Event sent when a run step fails. The data of this event is of type RunStep""" - THREAD_RUN_STEP_CANCELLED = "thread.run.step.cancelled" - """Event sent when a run step is cancelled. The data of this event is of type RunStep""" - THREAD_RUN_STEP_EXPIRED = "thread.run.step.expired" - """Event sent when a run step is expired. The data of this event is of type RunStep""" - - -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 RunStreamEvent(str, Enum, metaclass=CaseInsensitiveEnumMeta): - """Run operation related streaming events.""" - - THREAD_RUN_CREATED = "thread.run.created" - """Event sent when a new run is created. The data of this event is of type ThreadRun""" - THREAD_RUN_QUEUED = "thread.run.queued" - """Event sent when a run moves to ``queued`` status. The data of this event is of type ThreadRun""" - THREAD_RUN_IN_PROGRESS = "thread.run.in_progress" - """Event sent when a run moves to ``in_progress`` status. The data of this event is of type - ThreadRun""" - THREAD_RUN_REQUIRES_ACTION = "thread.run.requires_action" - """Event sent when a run moves to ``requires_action`` status. The data of this event is of type - ThreadRun""" - THREAD_RUN_COMPLETED = "thread.run.completed" - """Event sent when a run is completed. The data of this event is of type ThreadRun""" - THREAD_RUN_INCOMPLETE = "thread.run.incomplete" - """Event sent when a run ends incomplete. The data of this event is of type ThreadRun""" - THREAD_RUN_FAILED = "thread.run.failed" - """Event sent when a run fails. The data of this event is of type ThreadRun""" - THREAD_RUN_CANCELLING = "thread.run.cancelling" - """Event sent when a run moves to ``cancelling`` status. The data of this event is of type - ThreadRun""" - THREAD_RUN_CANCELLED = "thread.run.cancelled" - """Event sent when a run is cancelled. The data of this event is of type ThreadRun""" - THREAD_RUN_EXPIRED = "thread.run.expired" - """Event sent when a run is expired. The data of this event is of type ThreadRun""" - - -class ThreadStreamEvent(str, Enum, metaclass=CaseInsensitiveEnumMeta): - """Thread operation related streaming events.""" - - THREAD_CREATED = "thread.created" - """Event sent when a new thread is created. The data of this event is of type AgentThread""" - - -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 VectorStoreDataSourceAssetType(str, Enum, metaclass=CaseInsensitiveEnumMeta): - """Type of vector storage asset. Asset type may be a uri_asset, in this case it should contain - asset URI ID, - in the case of id_asset it should contain the data ID. - """ - - URI_ASSET = "uri_asset" - """Azure URI""" - ID_ASSET = "id_asset" - """The data ID""" - - -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.""" - - SERVER_ERROR = "server_error" - """An server error occurred.""" - INVALID_FILE = "invalid_file" - """The file is not valid.""" - UNSUPPORTED_FILE = "unsupported_file" - """The file is of unsupported 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.""" - - class WeekDays(str, Enum, metaclass=CaseInsensitiveEnumMeta): """WeekDay of the schedule - Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday.""" diff --git a/sdk/ai/azure-ai-projects/azure/ai/projects/models/_models.py b/sdk/ai/azure-ai-projects/azure/ai/projects/models/_models.py index 1d4d35c02c12..75e42220b875 100644 --- a/sdk/ai/azure-ai-projects/azure/ai/projects/models/_models.py +++ b/sdk/ai/azure-ai-projects/azure/ai/projects/models/_models.py @@ -1,4 +1,4 @@ -# pylint: disable=too-many-lines +# pylint: disable=line-too-long,useless-suppression,too-many-lines # coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. @@ -13,363 +13,10 @@ from .. import _model_base from .._model_base import rest_discriminator, rest_field -from ._enums import ( - AuthenticationType, - OpenApiAuthType, - RunStepType, - VectorStoreChunkingStrategyRequestType, - VectorStoreChunkingStrategyResponseType, -) +from ._enums import AuthenticationType if TYPE_CHECKING: - from .. import _types, models as _models - - -class Agent(_model_base.Model): - """Represents an agent 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 agent. Required. - :vartype name: str - :ivar description: The description of the agent. Required. - :vartype description: str - :ivar model: The ID of the model to use. Required. - :vartype model: str - :ivar instructions: The system instructions for the agent to use. Required. - :vartype instructions: str - :ivar tools: The collection of tools enabled for the agent. Required. - :vartype tools: list[~azure.ai.projects.models.ToolDefinition] - :ivar tool_resources: A set of resources that are used by the agent'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.projects.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 agent. Is one of the - following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat, ResponseFormatJsonSchemaType - :vartype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat or - ~azure.ai.projects.models.ResponseFormatJsonSchemaType - :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 agent. Required.""" - description: str = rest_field() - """The description of the agent. Required.""" - model: str = rest_field() - """The ID of the model to use. Required.""" - instructions: str = rest_field() - """The system instructions for the agent to use. Required.""" - tools: List["_models.ToolDefinition"] = rest_field() - """The collection of tools enabled for the agent. Required.""" - tool_resources: "_models.ToolResources" = rest_field() - """A set of resources that are used by the agent'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.AgentsApiResponseFormatOption"] = rest_field() - """The response format of the tool calls used by this agent. Is one of the following types: str, - Union[str, \"_models.AgentsApiResponseFormatMode\"], AgentsApiResponseFormat, - ResponseFormatJsonSchemaType""" - 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.AgentsApiResponseFormatOption"] = 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 AgentDeletionStatus(_model_base.Model): - """The status of an agent 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 AgentsApiResponseFormat(_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.projects.models.ResponseFormat - """ - - type: Optional[Union[str, "_models.ResponseFormat"]] = 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.ResponseFormat"]] = 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 AgentsNamedToolChoice(_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", "file_search", "bing_grounding", - "fabric_aiskill", "sharepoint_grounding", and "azure_ai_search". - :vartype type: str or ~azure.ai.projects.models.AgentsNamedToolChoiceType - :ivar function: The name of the function to call. - :vartype function: ~azure.ai.projects.models.FunctionName - """ - - type: Union[str, "_models.AgentsNamedToolChoiceType"] = rest_field() - """the type of tool. If type is ``function``, the function name must be set. Required. Known - values are: \"function\", \"code_interpreter\", \"file_search\", \"bing_grounding\", - \"fabric_aiskill\", \"sharepoint_grounding\", and \"azure_ai_search\".""" - function: Optional["_models.FunctionName"] = rest_field() - """The name of the function to call.""" - - @overload - def __init__( - self, - *, - type: Union[str, "_models.AgentsNamedToolChoiceType"], - 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 AgentThread(_model_base.Model): - """Information about a single thread associated with an agent. - - 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 agent'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.projects.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 agent'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 AgentThreadCreationOptions(_model_base.Model): - """The details used to create a new agent thread. - - :ivar messages: The initial messages to associate with the new thread. - :vartype messages: list[~azure.ai.projects.models.ThreadMessageOptions] - :ivar tool_resources: A set of resources that are made available to the agent'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.projects.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. - :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.ToolResources"] = rest_field() - """A set of resources that are made available to the agent'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.ToolResources"] = 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) + from .. import models as _models class AppInsightsProperties(_model_base.Model): @@ -380,7 +27,9 @@ class AppInsightsProperties(_model_base.Model): :vartype connection_string: str """ - connection_string: str = rest_field(name="ConnectionString") + connection_string: str = rest_field( + name="ConnectionString", visibility=["read", "create", "update", "delete", "query"] + ) """Authentication type of the connection target. Required.""" @overload @@ -413,7 +62,7 @@ class InputData(_model_base.Model): """ __mapping__: Dict[str, _model_base.Model] = {} - type: str = rest_discriminator(name="type") + type: str = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) """Type of the data. Required. Default value is None.""" @overload @@ -455,13 +104,17 @@ class ApplicationInsightsConfiguration(InputData, discriminator="app_insights"): type: Literal["app_insights"] = rest_discriminator(name="type", visibility=["read"]) # type: ignore """Required. Default value is \"app_insights\".""" - resource_id: str = rest_field(name="resourceId") + resource_id: str = rest_field(name="resourceId", visibility=["read", "create", "update", "delete", "query"]) """LogAnalytic Workspace resourceID associated with ApplicationInsights. Required.""" - query: str = rest_field() + query: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) """Query to fetch the data. Required.""" - service_name: Optional[str] = rest_field(name="serviceName") + service_name: Optional[str] = rest_field( + name="serviceName", visibility=["read", "create", "update", "delete", "query"] + ) """Service name.""" - connection_string: Optional[str] = rest_field(name="connectionString") + connection_string: Optional[str] = rest_field( + name="connectionString", visibility=["read", "create", "update", "delete", "query"] + ) """Connection String to connect to ApplicationInsights.""" @overload @@ -485,23 +138,22 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, type="app_insights", **kwargs) -class AzureAISearchResource(_model_base.Model): - """A set of index resources used by the ``azure_ai_search`` tool. +class CredentialsApiKeyAuth(_model_base.Model): + """The credentials needed for API key authentication. + - :ivar index_list: The indices attached to this agent. There can be a maximum of 1 index - resource attached to the agent. - :vartype index_list: list[~azure.ai.projects.models.IndexResource] + :ivar key: The API key. Required. + :vartype key: str """ - index_list: Optional[List["_models.IndexResource"]] = rest_field(name="indexes") - """The indices attached to this agent. There can be a maximum of 1 index - resource attached to the agent.""" + key: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The API key. Required.""" @overload def __init__( self, *, - index_list: Optional[List["_models.IndexResource"]] = None, + key: str, ) -> None: ... @overload @@ -515,28 +167,22 @@ 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 agent can use. - - You probably want to use the sub-classes and not this class directly. Known sub-classes are: - AzureAISearchToolDefinition, AzureFunctionToolDefinition, BingGroundingToolDefinition, - CodeInterpreterToolDefinition, MicrosoftFabricToolDefinition, FileSearchToolDefinition, - FunctionToolDefinition, OpenApiToolDefinition, SharepointToolDefinition +class CredentialsSASAuth(_model_base.Model): + """The credentials needed for Shared Access Signatures (SAS) authentication. - :ivar type: The object type. Required. Default value is None. - :vartype type: str + :ivar sas: The Shared Access Signatures (SAS) token. Required. + :vartype sas: str """ - __mapping__: Dict[str, _model_base.Model] = {} - type: str = rest_discriminator(name="type") - """The object type. Required. Default value is None.""" + sas: str = rest_field(name="SAS", visibility=["read", "create", "update", "delete", "query"]) + """The Shared Access Signatures (SAS) token. Required.""" @overload def __init__( self, *, - type: str, + sas: str, ) -> None: ... @overload @@ -550,22 +196,26 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) -class AzureAISearchToolDefinition(ToolDefinition, discriminator="azure_ai_search"): - """The input definition information for an Azure AI search tool as used to configure an agent. +class Trigger(_model_base.Model): + """Abstract data class for input data configuration. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + CronTrigger, RecurrenceTrigger - :ivar type: The object type, which is always 'azure_ai_search'. Required. Default value is - "azure_ai_search". + :ivar type: Type of the trigger. Required. Default value is None. :vartype type: str """ - type: Literal["azure_ai_search"] = rest_discriminator(name="type") # type: ignore - """The object type, which is always 'azure_ai_search'. Required. Default value is - \"azure_ai_search\".""" + __mapping__: Dict[str, _model_base.Model] = {} + type: str = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"]) + """Type of the trigger. Required. Default value is None.""" @overload def __init__( self, + *, + type: str, ) -> None: ... @overload @@ -576,33 +226,31 @@ def __init__(self, mapping: Mapping[str, Any]) -> None: """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, type="azure_ai_search", **kwargs) + super().__init__(*args, **kwargs) -class AzureFunctionBinding(_model_base.Model): - """The structure for keeping storage queue name and URI. +class CronTrigger(Trigger, discriminator="Cron"): + """Cron Trigger Definition. Readonly variables are only populated by the server, and will be ignored when sending a request. - :ivar type: The type of binding, which is always 'storage_queue'. Required. Default value is - "storage_queue". + :ivar type: Required. Default value is "Cron". :vartype type: str - :ivar storage_queue: Storage queue. Required. - :vartype storage_queue: ~azure.ai.projects.models.AzureFunctionStorageQueue + :ivar expression: Cron expression for the trigger. Required. + :vartype expression: str """ - type: Literal["storage_queue"] = rest_field() - """The type of binding, which is always 'storage_queue'. Required. Default value is - \"storage_queue\".""" - storage_queue: "_models.AzureFunctionStorageQueue" = rest_field() - """Storage queue. Required.""" + type: Literal["Cron"] = rest_discriminator(name="type", visibility=["read"]) # type: ignore + """Required. Default value is \"Cron\".""" + expression: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Cron expression for the trigger. Required.""" @overload def __init__( self, *, - storage_queue: "_models.AzureFunctionStorageQueue", + expression: str, ) -> None: ... @overload @@ -613,40 +261,31 @@ def __init__(self, mapping: Mapping[str, Any]) -> None: """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) - self.type: Literal["storage_queue"] = "storage_queue" + super().__init__(*args, type="Cron", **kwargs) + +class Dataset(InputData, discriminator="dataset"): + """Dataset as source for evaluation. -class AzureFunctionDefinition(_model_base.Model): - """The definition of Azure function. + Readonly variables are only populated by the server, and will be ignored when sending a request. - :ivar function: The definition of azure function and its parameters. Required. - :vartype function: ~azure.ai.projects.models.FunctionDefinition - :ivar input_binding: Input storage queue. The queue storage trigger runs a function as messages - are added to it. Required. - :vartype input_binding: ~azure.ai.projects.models.AzureFunctionBinding - :ivar output_binding: Output storage queue. The function writes output to this queue when the - input items are processed. Required. - :vartype output_binding: ~azure.ai.projects.models.AzureFunctionBinding + :ivar type: Required. Default value is "dataset". + :vartype type: str + :ivar id: Evaluation input data. Required. + :vartype id: str """ - function: "_models.FunctionDefinition" = rest_field() - """The definition of azure function and its parameters. Required.""" - input_binding: "_models.AzureFunctionBinding" = rest_field() - """Input storage queue. The queue storage trigger runs a function as messages are added to it. - Required.""" - output_binding: "_models.AzureFunctionBinding" = rest_field() - """Output storage queue. The function writes output to this queue when the input items are - processed. Required.""" + type: Literal["dataset"] = rest_discriminator(name="type", visibility=["read"]) # type: ignore + """Required. Default value is \"dataset\".""" + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Evaluation input data. Required.""" @overload def __init__( self, *, - function: "_models.FunctionDefinition", - input_binding: "_models.AzureFunctionBinding", - output_binding: "_models.AzureFunctionBinding", + id: str, # pylint: disable=redefined-builtin ) -> None: ... @overload @@ -657,31 +296,72 @@ def __init__(self, mapping: Mapping[str, Any]) -> None: """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) + super().__init__(*args, type="dataset", **kwargs) + +class Evaluation(_model_base.Model): + """Evaluation Definition. -class AzureFunctionStorageQueue(_model_base.Model): - """The structure for keeping storage queue name and URI. + Readonly variables are only populated by the server, and will be ignored when sending a request. - :ivar storage_service_endpoint: URI to the Azure Storage Queue service allowing you to - manipulate a queue. Required. - :vartype storage_service_endpoint: str - :ivar queue_name: The name of an Azure function storage queue. Required. - :vartype queue_name: str + :ivar id: Identifier of the evaluation. Required. + :vartype id: str + :ivar data: Data for evaluation. Required. + :vartype data: ~azure.ai.projects.models.InputData + :ivar display_name: Display Name for evaluation. It helps to find the evaluation easily in AI + Foundry. It does not need to be unique. + :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.projects.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.projects.models.EvaluatorConfiguration] """ - storage_service_endpoint: str = rest_field(name="queue_service_endpoint") - """URI to the Azure Storage Queue service allowing you to manipulate a queue. Required.""" - queue_name: str = rest_field() - """The name of an Azure function storage queue. Required.""" + id: str = rest_field(visibility=["read"]) + """Identifier of the evaluation. Required.""" + data: "_models.InputData" = rest_field(visibility=["read", "create"]) + """Data for evaluation. Required.""" + display_name: Optional[str] = rest_field( + name="displayName", visibility=["read", "create", "update", "delete", "query"] + ) + """Display Name for evaluation. It helps to find the evaluation easily in AI Foundry. It does not + need to be unique.""" + description: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """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(visibility=["read", "create", "update", "delete", "query"]) + """Evaluation's tags. Unlike properties, tags are fully mutable.""" + properties: Optional[Dict[str, str]] = rest_field(visibility=["read", "create"]) + """Evaluation's properties. Unlike tags, properties are add-only. Once added, a property cannot be + removed.""" + evaluators: Dict[str, "_models.EvaluatorConfiguration"] = rest_field(visibility=["read", "create"]) + """Evaluators to be used for the evaluation. Required.""" @overload def __init__( self, *, - storage_service_endpoint: str, - queue_name: str, + data: "_models.InputData", + evaluators: Dict[str, "_models.EvaluatorConfiguration"], + display_name: Optional[str] = None, + description: Optional[str] = None, + tags: Optional[Dict[str, str]] = None, + properties: Optional[Dict[str, str]] = None, ) -> None: ... @overload @@ -695,391 +375,17 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) -class AzureFunctionToolDefinition(ToolDefinition, discriminator="azure_function"): - """The input definition information for a azure function tool as used to configure an agent. +class EvaluationSchedule(_model_base.Model): + """Evaluation Schedule Definition. + Readonly variables are only populated by the server, and will be ignored when sending a request. - :ivar type: The object type, which is always 'azure_function'. Required. Default value is - "azure_function". - :vartype type: str - :ivar azure_function: The definition of the concrete function that the function tool should - call. Required. - :vartype azure_function: ~azure.ai.projects.models.AzureFunctionDefinition - """ - - type: Literal["azure_function"] = rest_discriminator(name="type") # type: ignore - """The object type, which is always 'azure_function'. Required. Default value is - \"azure_function\".""" - azure_function: "_models.AzureFunctionDefinition" = rest_field() - """The definition of the concrete function that the function tool should call. Required.""" - - @overload - def __init__( - self, - *, - azure_function: "_models.AzureFunctionDefinition", - ) -> 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="azure_function", **kwargs) - - -class BingGroundingToolDefinition(ToolDefinition, discriminator="bing_grounding"): - """The input definition information for a bing grounding search tool as used to configure an - agent. - - - :ivar type: The object type, which is always 'bing_grounding'. Required. Default value is - "bing_grounding". - :vartype type: str - :ivar bing_grounding: The list of connections used by the bing grounding tool. Required. - :vartype bing_grounding: ~azure.ai.projects.models.ToolConnectionList - """ - - type: Literal["bing_grounding"] = rest_discriminator(name="type") # type: ignore - """The object type, which is always 'bing_grounding'. Required. Default value is - \"bing_grounding\".""" - bing_grounding: "_models.ToolConnectionList" = rest_field() - """The list of connections used by the bing grounding tool. Required.""" - - @overload - def __init__( - self, - *, - bing_grounding: "_models.ToolConnectionList", - ) -> 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="bing_grounding", **kwargs) - - -class CodeInterpreterToolDefinition(ToolDefinition, discriminator="code_interpreter"): - """The input definition information for a code interpreter tool as used to configure an agent. - - - :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. - :vartype file_ids: list[str] - :ivar data_sources: The data sources to be used. This option is mutually exclusive with the - ``fileIds`` property. - :vartype data_sources: list[~azure.ai.projects.models.VectorStoreDataSource] - """ - - file_ids: Optional[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.""" - data_sources: Optional[List["_models.VectorStoreDataSource"]] = rest_field() - """The data sources to be used. This option is mutually exclusive with the ``fileIds`` property.""" - - @overload - def __init__( - self, - *, - file_ids: Optional[List[str]] = None, - data_sources: Optional[List["_models.VectorStoreDataSource"]] = 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 CredentialsApiKeyAuth(_model_base.Model): - """The credentials needed for API key authentication. - - - :ivar key: The API key. Required. - :vartype key: str - """ - - key: str = rest_field() - """The API key. Required.""" - - @overload - def __init__( - self, - *, - key: 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 CredentialsSASAuth(_model_base.Model): - """The credentials needed for Shared Access Signatures (SAS) authentication. - - - :ivar sas: The Shared Access Signatures (SAS) token. Required. - :vartype sas: str - """ - - sas: str = rest_field(name="SAS") - """The Shared Access Signatures (SAS) token. Required.""" - - @overload - def __init__( - self, - *, - sas: 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 Trigger(_model_base.Model): - """Abstract data class for input data configuration. - - You probably want to use the sub-classes and not this class directly. Known sub-classes are: - CronTrigger, RecurrenceTrigger - - - :ivar type: Type of the trigger. Required. Default value is None. - :vartype type: str - """ - - __mapping__: Dict[str, _model_base.Model] = {} - type: str = rest_discriminator(name="type") - """Type of the trigger. 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 CronTrigger(Trigger, discriminator="Cron"): - """Cron Trigger Definition. - - Readonly variables are only populated by the server, and will be ignored when sending a request. - - - :ivar type: Required. Default value is "Cron". - :vartype type: str - :ivar expression: Cron expression for the trigger. Required. - :vartype expression: str - """ - - type: Literal["Cron"] = rest_discriminator(name="type", visibility=["read"]) # type: ignore - """Required. Default value is \"Cron\".""" - expression: str = rest_field() - """Cron expression for the trigger. Required.""" - - @overload - def __init__( - self, - *, - expression: 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="Cron", **kwargs) - - -class Dataset(InputData, discriminator="dataset"): - """Dataset as source for evaluation. - - Readonly variables are only populated by the server, and will be ignored when sending a request. - - - :ivar type: Required. Default value is "dataset". - :vartype type: str - :ivar id: Evaluation input data. Required. - :vartype id: str - """ - - type: Literal["dataset"] = rest_discriminator(name="type", visibility=["read"]) # type: ignore - """Required. Default value is \"dataset\".""" - id: str = rest_field() - """Evaluation input data. Required.""" - - @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 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. Required. - :vartype id: str + :ivar name: Name of the schedule, which also serves as the unique identifier for the + evaluation. Required. + :vartype name: str :ivar data: Data for evaluation. Required. - :vartype data: ~azure.ai.projects.models.InputData - :ivar display_name: Display Name for evaluation. It helps to find the evaluation easily in AI - Foundry. It does not need to be unique. - :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.projects.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.projects.models.EvaluatorConfiguration] - """ - - id: str = rest_field(visibility=["read"]) - """Identifier of the evaluation. Required.""" - data: "_models.InputData" = rest_field(visibility=["read", "create"]) - """Data for evaluation. Required.""" - display_name: Optional[str] = rest_field(name="displayName") - """Display Name for evaluation. It helps to find the evaluation easily in AI Foundry. It does not - need to be unique.""" - 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(visibility=["read", "create"]) - """Evaluation's properties. Unlike tags, properties are add-only. Once added, a property cannot be - removed.""" - evaluators: Dict[str, "_models.EvaluatorConfiguration"] = rest_field(visibility=["read", "create"]) - """Evaluators to be used for the evaluation. Required.""" - - @overload - def __init__( - self, - *, - data: "_models.InputData", - evaluators: Dict[str, "_models.EvaluatorConfiguration"], - display_name: 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 EvaluationSchedule(_model_base.Model): - """Evaluation Schedule Definition. - - Readonly variables are only populated by the server, and will be ignored when sending a request. - - - :ivar name: Name of the schedule, which also serves as the unique identifier for the - evaluation. Required. - :vartype name: str - :ivar data: Data for evaluation. Required. - :vartype data: ~azure.ai.projects.models.ApplicationInsightsConfiguration + :vartype data: ~azure.ai.projects.models.ApplicationInsightsConfiguration :ivar description: Description of the evaluation. It can be used to store additional information about the evaluation and is mutable. :vartype description: str @@ -1105,5481 +411,35 @@ class EvaluationSchedule(_model_base.Model): """Name of the schedule, which also serves as the unique identifier for the evaluation. Required.""" data: "_models.ApplicationInsightsConfiguration" = rest_field(visibility=["read", "create"]) """Data for evaluation. Required.""" - description: Optional[str] = rest_field() + description: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) """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.""" provisioning_state: Optional[str] = rest_field(name="provisioningState", visibility=["read"]) """Provisioning State of the evaluation. It is set by service and is read-only.""" - tags: Optional[Dict[str, str]] = rest_field() + tags: Optional[Dict[str, str]] = rest_field(visibility=["read", "create", "update", "delete", "query"]) """Evaluation's tags. Unlike properties, tags are fully mutable.""" properties: Optional[Dict[str, str]] = rest_field(visibility=["read", "create"]) - """Evaluation's properties. Unlike tags, properties are add-only. Once added, a property cannot be - removed.""" - is_enabled: Optional[str] = rest_field(name="isEnabled", visibility=["read"]) - """Enabled status of the evaluation. It is set by service and is read-only.""" - evaluators: Dict[str, "_models.EvaluatorConfiguration"] = rest_field(visibility=["read", "create"]) - """Evaluators to be used for the evaluation. Required.""" - trigger: "_models.Trigger" = rest_field() - """Trigger for the evaluation. Required.""" - - @overload - def __init__( - self, - *, - data: "_models.ApplicationInsightsConfiguration", - evaluators: Dict[str, "_models.EvaluatorConfiguration"], - trigger: "_models.Trigger", - 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 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, any] - :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, Any]] = 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, Any]] = 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.projects.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 FileSearchRankingOptions(_model_base.Model): - """Ranking options for file search. - - - :ivar ranker: File search ranker. Required. - :vartype ranker: str - :ivar score_threshold: Ranker search threshold. Required. - :vartype score_threshold: float - """ - - ranker: str = rest_field() - """File search ranker. Required.""" - score_threshold: float = rest_field() - """Ranker search threshold. Required.""" - - @overload - def __init__( - self, - *, - ranker: str, - score_threshold: float, - ) -> 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 FileSearchToolCallContent(_model_base.Model): - """The file search result content object. - - Readonly variables are only populated by the server, and will be ignored when sending a request. - - - :ivar type: The type of the content. Required. Default value is "text". - :vartype type: str - :ivar text: The text content of the file. Required. - :vartype text: str - """ - - type: Literal["text"] = rest_field() - """The type of the content. Required. Default value is \"text\".""" - text: str = rest_field() - """The text content of the file. Required.""" - - @overload - def __init__( - self, - *, - 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) - self.type: Literal["text"] = "text" - - -class FileSearchToolDefinition(ToolDefinition, discriminator="file_search"): - """The input definition information for a file search tool as used to configure an agent. - - - :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.projects.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 - :ivar ranking_options: Ranking options for file search. - :vartype ranking_options: ~azure.ai.projects.models.FileSearchRankingOptions - """ - - 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.""" - ranking_options: Optional["_models.FileSearchRankingOptions"] = rest_field() - """Ranking options for file search.""" - - @overload - def __init__( - self, - *, - max_num_results: Optional[int] = None, - ranking_options: Optional["_models.FileSearchRankingOptions"] = 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 agent. There can be a - maximum of 1 vector - store attached to the agent. - :vartype vector_store_ids: list[str] - :ivar vector_stores: The list of vector store configuration objects from Azure. - This list is limited to one element. - The only element of this list contains the list of azure asset IDs used by the search tool. - :vartype vector_stores: list[~azure.ai.projects.models.VectorStoreConfigurations] - """ - - vector_store_ids: Optional[List[str]] = rest_field() - """The ID of the vector store attached to this agent. There can be a maximum of 1 vector - store attached to the agent.""" - vector_stores: Optional[List["_models.VectorStoreConfigurations"]] = rest_field() - """The list of vector store configuration objects from Azure. - This list is limited to one element. - The only element of this list contains the list of azure asset IDs used by the search tool.""" - - @overload - def __init__( - self, - *, - vector_store_ids: Optional[List[str]] = None, - vector_stores: Optional[List["_models.VectorStoreConfigurations"]] = 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 agent. - - - :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.projects.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 GetAppInsightsResponse(_model_base.Model): - """Response from getting properties of the Application Insights resource. - - - :ivar id: A unique identifier for the resource. Required. - :vartype id: str - :ivar name: The name of the resource. Required. - :vartype name: str - :ivar properties: The properties of the resource. Required. - :vartype properties: ~azure.ai.projects.models._models.AppInsightsProperties - """ - - id: str = rest_field() - """A unique identifier for the resource. Required.""" - name: str = rest_field() - """The name of the resource. Required.""" - properties: "_models._models.AppInsightsProperties" = rest_field() - """The properties of the resource. Required.""" - - @overload - def __init__( - self, - *, - id: str, # pylint: disable=redefined-builtin - name: str, - properties: "_models._models.AppInsightsProperties", - ) -> 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 GetConnectionResponse(_model_base.Model): - """Response from the listSecrets operation. - - - :ivar id: A unique identifier for the connection. Required. - :vartype id: str - :ivar name: The name of the resource. Required. - :vartype name: str - :ivar properties: The properties of the resource. Required. - :vartype properties: ~azure.ai.projects.models._models.InternalConnectionProperties - """ - - id: str = rest_field() - """A unique identifier for the connection. Required.""" - name: str = rest_field() - """The name of the resource. Required.""" - properties: "_models._models.InternalConnectionProperties" = rest_field() - """The properties of the resource. Required.""" - - @overload - def __init__( - self, - *, - id: str, # pylint: disable=redefined-builtin - name: str, - properties: "_models._models.InternalConnectionProperties", - ) -> 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 GetWorkspaceResponse(_model_base.Model): - """Response from the Workspace - Get operation. - - - :ivar id: A unique identifier for the resource. Required. - :vartype id: str - :ivar name: The name of the resource. Required. - :vartype name: str - :ivar properties: The properties of the resource. Required. - :vartype properties: ~azure.ai.projects.models._models.WorkspaceProperties - """ - - id: str = rest_field() - """A unique identifier for the resource. Required.""" - name: str = rest_field() - """The name of the resource. Required.""" - properties: "_models._models.WorkspaceProperties" = rest_field() - """The properties of the resource. Required.""" - - @overload - def __init__( - self, - *, - id: str, # pylint: disable=redefined-builtin - name: str, - properties: "_models._models.WorkspaceProperties", - ) -> 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 IncompleteRunDetails(_model_base.Model): - """Details on why the run is incomplete. Will be ``null`` if the run is not incomplete. - - - :ivar reason: The reason why the run is incomplete. This indicates which specific token limit - was reached during the run. Required. Known values are: "max_completion_tokens" and - "max_prompt_tokens". - :vartype reason: str or ~azure.ai.projects.models.IncompleteDetailsReason - """ - - reason: Union[str, "_models.IncompleteDetailsReason"] = rest_field() - """The reason why the run is incomplete. This indicates which specific token limit was reached - during the run. Required. Known values are: \"max_completion_tokens\" and - \"max_prompt_tokens\".""" - - @overload - def __init__( - self, - *, - reason: Union[str, "_models.IncompleteDetailsReason"], - ) -> 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 IndexResource(_model_base.Model): - """A Index resource. - - - :ivar index_connection_id: An index connection id in an IndexResource attached to this agent. - Required. - :vartype index_connection_id: str - :ivar index_name: The name of an index in an IndexResource attached to this agent. Required. - :vartype index_name: str - """ - - index_connection_id: str = rest_field() - """An index connection id in an IndexResource attached to this agent. Required.""" - index_name: str = rest_field() - """The name of an index in an IndexResource attached to this agent. Required.""" - - @overload - def __init__( - self, - *, - index_connection_id: str, - index_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 InternalConnectionProperties(_model_base.Model): - """Connection properties. - - You probably want to use the sub-classes and not this class directly. Known sub-classes are: - InternalConnectionPropertiesAADAuth, InternalConnectionPropertiesApiKeyAuth, - InternalConnectionPropertiesNoAuth, InternalConnectionPropertiesSASAuth - - - :ivar auth_type: Authentication type of the connection target. Required. Known values are: - "ApiKey", "AAD", "SAS", and "None". - :vartype auth_type: str or ~azure.ai.projects.models.AuthenticationType - :ivar category: Category of the connection. Required. Known values are: "AzureOpenAI", - "Serverless", "AzureBlob", "AIServices", and "CognitiveSearch". - :vartype category: str or ~azure.ai.projects.models.ConnectionType - :ivar target: The connection URL to be used for this service. Required. - :vartype target: str - """ - - __mapping__: Dict[str, _model_base.Model] = {} - auth_type: str = rest_discriminator(name="authType") - """Authentication type of the connection target. Required. Known values are: \"ApiKey\", \"AAD\", - \"SAS\", and \"None\".""" - category: Union[str, "_models.ConnectionType"] = rest_field() - """Category of the connection. Required. Known values are: \"AzureOpenAI\", \"Serverless\", - \"AzureBlob\", \"AIServices\", and \"CognitiveSearch\".""" - target: str = rest_field() - """The connection URL to be used for this service. Required.""" - - @overload - def __init__( - self, - *, - auth_type: str, - category: Union[str, "_models.ConnectionType"], - target: 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 InternalConnectionPropertiesAADAuth(InternalConnectionProperties, discriminator="AAD"): - """Connection properties for connections with AAD authentication (aka ``Entra ID passthrough``\\ - ). - - - :ivar category: Category of the connection. Required. Known values are: "AzureOpenAI", - "Serverless", "AzureBlob", "AIServices", and "CognitiveSearch". - :vartype category: str or ~azure.ai.projects.models.ConnectionType - :ivar target: The connection URL to be used for this service. Required. - :vartype target: str - :ivar auth_type: Authentication type of the connection target. Required. Entra ID - authentication (formerly known as AAD) - :vartype auth_type: str or ~azure.ai.projects.models.ENTRA_ID - """ - - auth_type: Literal[AuthenticationType.ENTRA_ID] = rest_discriminator(name="authType") # type: ignore - """Authentication type of the connection target. Required. Entra ID authentication (formerly known - as AAD)""" - - @overload - def __init__( - self, - *, - category: Union[str, "_models.ConnectionType"], - target: 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, auth_type=AuthenticationType.ENTRA_ID, **kwargs) - - -class InternalConnectionPropertiesApiKeyAuth(InternalConnectionProperties, discriminator="ApiKey"): - """Connection properties for connections with API key authentication. - - - :ivar category: Category of the connection. Required. Known values are: "AzureOpenAI", - "Serverless", "AzureBlob", "AIServices", and "CognitiveSearch". - :vartype category: str or ~azure.ai.projects.models.ConnectionType - :ivar target: The connection URL to be used for this service. Required. - :vartype target: str - :ivar auth_type: Authentication type of the connection target. Required. API Key authentication - :vartype auth_type: str or ~azure.ai.projects.models.API_KEY - :ivar credentials: Credentials will only be present for authType=ApiKey. Required. - :vartype credentials: ~azure.ai.projects.models._models.CredentialsApiKeyAuth - """ - - auth_type: Literal[AuthenticationType.API_KEY] = rest_discriminator(name="authType") # type: ignore - """Authentication type of the connection target. Required. API Key authentication""" - credentials: "_models._models.CredentialsApiKeyAuth" = rest_field() - """Credentials will only be present for authType=ApiKey. Required.""" - - @overload - def __init__( - self, - *, - category: Union[str, "_models.ConnectionType"], - target: str, - credentials: "_models._models.CredentialsApiKeyAuth", - ) -> 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, auth_type=AuthenticationType.API_KEY, **kwargs) - - -class InternalConnectionPropertiesNoAuth(InternalConnectionProperties, discriminator="None"): - """Connection properties for connections with no authentication. - - - :ivar category: Category of the connection. Required. Known values are: "AzureOpenAI", - "Serverless", "AzureBlob", "AIServices", and "CognitiveSearch". - :vartype category: str or ~azure.ai.projects.models.ConnectionType - :ivar target: The connection URL to be used for this service. Required. - :vartype target: str - :ivar auth_type: Authentication type of the connection target. Required. No authentication - :vartype auth_type: str or ~azure.ai.projects.models.NONE - """ - - auth_type: Literal[AuthenticationType.NONE] = rest_discriminator(name="authType") # type: ignore - """Authentication type of the connection target. Required. No authentication""" - - @overload - def __init__( - self, - *, - category: Union[str, "_models.ConnectionType"], - target: 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, auth_type=AuthenticationType.NONE, **kwargs) - - -class InternalConnectionPropertiesSASAuth(InternalConnectionProperties, discriminator="SAS"): - """Connection properties for connections with SAS authentication. - - - :ivar category: Category of the connection. Required. Known values are: "AzureOpenAI", - "Serverless", "AzureBlob", "AIServices", and "CognitiveSearch". - :vartype category: str or ~azure.ai.projects.models.ConnectionType - :ivar target: The connection URL to be used for this service. Required. - :vartype target: str - :ivar auth_type: Authentication type of the connection target. Required. Shared Access - Signature (SAS) authentication - :vartype auth_type: str or ~azure.ai.projects.models.SAS - :ivar credentials: Credentials will only be present for authType=ApiKey. Required. - :vartype credentials: ~azure.ai.projects.models._models.CredentialsSASAuth - """ - - auth_type: Literal[AuthenticationType.SAS] = rest_discriminator(name="authType") # type: ignore - """Authentication type of the connection target. Required. Shared Access Signature (SAS) - authentication""" - credentials: "_models._models.CredentialsSASAuth" = rest_field() - """Credentials will only be present for authType=ApiKey. Required.""" - - @overload - def __init__( - self, - *, - category: Union[str, "_models.ConnectionType"], - target: str, - credentials: "_models._models.CredentialsSASAuth", - ) -> 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, auth_type=AuthenticationType.SAS, **kwargs) - - -class ListConnectionsResponse(_model_base.Model): - """Response from the list operation. - - - :ivar value: A list of connection list secrets. Required. - :vartype value: list[~azure.ai.projects.models._models.GetConnectionResponse] - """ - - value: List["_models._models.GetConnectionResponse"] = rest_field() - """A list of connection list secrets. Required.""" - - @overload - def __init__( - self, - *, - value: List["_models._models.GetConnectionResponse"], - ) -> 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. - :vartype file_id: str - :ivar data_source: Azure asset ID. - :vartype data_source: ~azure.ai.projects.models.VectorStoreDataSource - :ivar tools: The tools to add to this file. Required. - :vartype tools: list[~azure.ai.projects.models.CodeInterpreterToolDefinition or - ~azure.ai.projects.models.FileSearchToolDefinition] - """ - - file_id: Optional[str] = rest_field() - """The ID of the file to attach to the message.""" - data_source: Optional["_models.VectorStoreDataSource"] = rest_field() - """Azure asset ID.""" - tools: List["_types.MessageAttachmentToolDefinition"] = rest_field() - """The tools to add to this file. Required.""" - - @overload - def __init__( - self, - *, - tools: List["_types.MessageAttachmentToolDefinition"], - file_id: Optional[str] = None, - data_source: Optional["_models.VectorStoreDataSource"] = 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 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 MessageDelta(_model_base.Model): - """Represents the typed 'delta' payload within a streaming message delta chunk. - - - :ivar role: The entity that produced the message. Required. Known values are: "user" and - "assistant". - :vartype role: str or ~azure.ai.projects.models.MessageRole - :ivar content: The content of the message as an array of text and/or images. Required. - :vartype content: list[~azure.ai.projects.models.MessageDeltaContent] - """ - - role: Union[str, "_models.MessageRole"] = rest_field() - """The entity that produced the message. Required. Known values are: \"user\" and \"assistant\".""" - content: List["_models.MessageDeltaContent"] = rest_field() - """The content of the message as an array of text and/or images. Required.""" - - @overload - def __init__( - self, - *, - role: Union[str, "_models.MessageRole"], - content: List["_models.MessageDeltaContent"], - ) -> 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 MessageDeltaChunk(_model_base.Model): - """Represents a message delta i.e. any changed fields on a message during streaming. - - Readonly variables are only populated by the server, and will be ignored when sending a request. - - - :ivar id: The identifier of the message, which can be referenced in API endpoints. Required. - :vartype id: str - :ivar object: The object type, which is always ``thread.message.delta``. Required. Default - value is "thread.message.delta". - :vartype object: str - :ivar delta: The delta containing the fields that have changed on the Message. Required. - :vartype delta: ~azure.ai.projects.models.MessageDelta - """ - - id: str = rest_field() - """The identifier of the message, which can be referenced in API endpoints. Required.""" - object: Literal["thread.message.delta"] = rest_field() - """The object type, which is always ``thread.message.delta``. Required. Default value is - \"thread.message.delta\".""" - delta: "_models.MessageDelta" = rest_field() - """The delta containing the fields that have changed on the Message. Required.""" - - @overload - def __init__( - self, - *, - id: str, # pylint: disable=redefined-builtin - delta: "_models.MessageDelta", - ) -> 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.delta"] = "thread.message.delta" - - -class MessageDeltaContent(_model_base.Model): - """The abstract base representation of a partial streamed message content payload. - - You probably want to use the sub-classes and not this class directly. Known sub-classes are: - MessageDeltaImageFileContent, MessageDeltaTextContent - - - :ivar index: The index of the content part of the message. Required. - :vartype index: int - :ivar type: The type of content for this content part. Required. Default value is None. - :vartype type: str - """ - - __mapping__: Dict[str, _model_base.Model] = {} - index: int = rest_field() - """The index of the content part of the message. Required.""" - type: str = rest_discriminator(name="type") - """The type of content for this content part. Required. Default value is None.""" - - @overload - def __init__( - self, - *, - index: int, - 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 MessageDeltaImageFileContent(MessageDeltaContent, discriminator="image_file"): - """Represents a streamed image file content part within a streaming message delta chunk. - - - :ivar index: The index of the content part of the message. Required. - :vartype index: int - :ivar type: The type of content for this content part, which is always "image_file.". Required. - Default value is "image_file". - :vartype type: str - :ivar image_file: The image_file data. - :vartype image_file: ~azure.ai.projects.models.MessageDeltaImageFileContentObject - """ - - type: Literal["image_file"] = rest_discriminator(name="type") # type: ignore - """The type of content for this content part, which is always \"image_file.\". Required. Default - value is \"image_file\".""" - image_file: Optional["_models.MessageDeltaImageFileContentObject"] = rest_field() - """The image_file data.""" - - @overload - def __init__( - self, - *, - index: int, - image_file: Optional["_models.MessageDeltaImageFileContentObject"] = 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="image_file", **kwargs) - - -class MessageDeltaImageFileContentObject(_model_base.Model): - """Represents the 'image_file' payload within streaming image file content. - - :ivar file_id: The file ID of the image in the message content. - :vartype file_id: str - """ - - file_id: Optional[str] = rest_field() - """The file ID of the image in the message content.""" - - @overload - def __init__( - self, - *, - file_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 MessageDeltaTextAnnotation(_model_base.Model): - """The abstract base representation of a streamed text content part's text annotation. - - You probably want to use the sub-classes and not this class directly. Known sub-classes are: - MessageDeltaTextFileCitationAnnotation, MessageDeltaTextFilePathAnnotation - - - :ivar index: The index of the annotation within a text content part. Required. - :vartype index: int - :ivar type: The type of the text content annotation. Required. Default value is None. - :vartype type: str - """ - - __mapping__: Dict[str, _model_base.Model] = {} - index: int = rest_field() - """The index of the annotation within a text content part. Required.""" - type: str = rest_discriminator(name="type") - """The type of the text content annotation. Required. Default value is None.""" - - @overload - def __init__( - self, - *, - index: int, - 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 MessageDeltaTextContent(MessageDeltaContent, discriminator="text"): - """Represents a streamed text content part within a streaming message delta chunk. - - - :ivar index: The index of the content part of the message. Required. - :vartype index: int - :ivar type: The type of content for this content part, which is always "text.". Required. - Default value is "text". - :vartype type: str - :ivar text: The text content details. - :vartype text: ~azure.ai.projects.models.MessageDeltaTextContentObject - """ - - type: Literal["text"] = rest_discriminator(name="type") # type: ignore - """The type of content for this content part, which is always \"text.\". Required. Default value - is \"text\".""" - text: Optional["_models.MessageDeltaTextContentObject"] = rest_field() - """The text content details.""" - - @overload - def __init__( - self, - *, - index: int, - text: Optional["_models.MessageDeltaTextContentObject"] = 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="text", **kwargs) - - -class MessageDeltaTextContentObject(_model_base.Model): - """Represents the data of a streamed text content part within a streaming message delta chunk. - - :ivar value: The data that makes up the text. - :vartype value: str - :ivar annotations: Annotations for the text. - :vartype annotations: list[~azure.ai.projects.models.MessageDeltaTextAnnotation] - """ - - value: Optional[str] = rest_field() - """The data that makes up the text.""" - annotations: Optional[List["_models.MessageDeltaTextAnnotation"]] = rest_field() - """Annotations for the text.""" - - @overload - def __init__( - self, - *, - value: Optional[str] = None, - annotations: Optional[List["_models.MessageDeltaTextAnnotation"]] = 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 MessageDeltaTextFileCitationAnnotation(MessageDeltaTextAnnotation, discriminator="file_citation"): - """Represents a streamed file citation applied to a streaming text content part. - - - :ivar index: The index of the annotation within a text content part. Required. - :vartype index: int - :ivar type: The type of the text content annotation, which is always "file_citation.". - Required. Default value is "file_citation". - :vartype type: str - :ivar file_citation: The file citation information. - :vartype file_citation: ~azure.ai.projects.models.MessageDeltaTextFileCitationAnnotationObject - :ivar text: The text in the message content that needs to be replaced. - :vartype text: str - :ivar start_index: The start index of this annotation in the content text. - :vartype start_index: int - :ivar end_index: The end index of this annotation in the content text. - :vartype end_index: int - """ - - type: Literal["file_citation"] = rest_discriminator(name="type") # type: ignore - """The type of the text content annotation, which is always \"file_citation.\". Required. Default - value is \"file_citation\".""" - file_citation: Optional["_models.MessageDeltaTextFileCitationAnnotationObject"] = rest_field() - """The file citation information.""" - text: Optional[str] = rest_field() - """The text in the message content that needs to be replaced.""" - start_index: Optional[int] = rest_field() - """The start index of this annotation in the content text.""" - end_index: Optional[int] = rest_field() - """The end index of this annotation in the content text.""" - - @overload - def __init__( - self, - *, - index: int, - file_citation: Optional["_models.MessageDeltaTextFileCitationAnnotationObject"] = None, - text: Optional[str] = None, - 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 MessageDeltaTextFileCitationAnnotationObject(_model_base.Model): # pylint: disable=name-too-long - """Represents the data of a streamed file citation as applied to a streaming text content part. - - :ivar file_id: The ID of the specific file the citation is from. - :vartype file_id: str - :ivar quote: The specific quote in the cited file. - :vartype quote: str - """ - - file_id: Optional[str] = rest_field() - """The ID of the specific file the citation is from.""" - quote: Optional[str] = rest_field() - """The specific quote in the cited file.""" - - @overload - def __init__( - self, - *, - file_id: Optional[str] = None, - quote: 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 MessageDeltaTextFilePathAnnotation(MessageDeltaTextAnnotation, discriminator="file_path"): - """Represents a streamed file path annotation applied to a streaming text content part. - - - :ivar index: The index of the annotation within a text content part. Required. - :vartype index: int - :ivar type: The type of the text content annotation, which is always "file_path.". Required. - Default value is "file_path". - :vartype type: str - :ivar file_path: The file path information. - :vartype file_path: ~azure.ai.projects.models.MessageDeltaTextFilePathAnnotationObject - :ivar start_index: The start index of this annotation in the content text. - :vartype start_index: int - :ivar end_index: The end index of this annotation in the content text. - :vartype end_index: int - :ivar text: The text in the message content that needs to be replaced. - :vartype text: str - """ - - type: Literal["file_path"] = rest_discriminator(name="type") # type: ignore - """The type of the text content annotation, which is always \"file_path.\". Required. Default - value is \"file_path\".""" - file_path: Optional["_models.MessageDeltaTextFilePathAnnotationObject"] = rest_field() - """The file path information.""" - start_index: Optional[int] = rest_field() - """The start index of this annotation in the content text.""" - end_index: Optional[int] = rest_field() - """The end index of this annotation in the content text.""" - text: Optional[str] = rest_field() - """The text in the message content that needs to be replaced.""" - - @overload - def __init__( - self, - *, - index: int, - file_path: Optional["_models.MessageDeltaTextFilePathAnnotationObject"] = None, - start_index: Optional[int] = None, - end_index: Optional[int] = None, - text: 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="file_path", **kwargs) - - -class MessageDeltaTextFilePathAnnotationObject(_model_base.Model): - """Represents the data of a streamed file path annotation as applied to a streaming text content - part. - - :ivar file_id: The file ID for the annotation. - :vartype file_id: str - """ - - file_id: Optional[str] = rest_field() - """The file ID for the annotation.""" - - @overload - def __init__( - self, - *, - file_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 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.projects.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.projects.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.projects.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 agent 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.projects.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 agent or the message. Generated when the agent 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 agent uses the "file_search" tool to search files. Required. - :vartype file_citation: ~azure.ai.projects.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 agent 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 agent used the code_interpreter - tool to generate a file. Required. - :vartype file_path: ~azure.ai.projects.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 agent 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 MicrosoftFabricToolDefinition(ToolDefinition, discriminator="fabric_aiskill"): - """The input definition information for a Microsoft Fabric tool as used to configure an agent. - - - :ivar type: The object type, which is always 'fabric_aiskill'. Required. Default value is - "fabric_aiskill". - :vartype type: str - :ivar fabric_aiskill: The list of connections used by the Microsoft Fabric tool. Required. - :vartype fabric_aiskill: ~azure.ai.projects.models.ToolConnectionList - """ - - type: Literal["fabric_aiskill"] = rest_discriminator(name="type") # type: ignore - """The object type, which is always 'fabric_aiskill'. Required. Default value is - \"fabric_aiskill\".""" - fabric_aiskill: "_models.ToolConnectionList" = rest_field() - """The list of connections used by the Microsoft Fabric tool. Required.""" - - @overload - def __init__( - self, - *, - fabric_aiskill: "_models.ToolConnectionList", - ) -> 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="fabric_aiskill", **kwargs) - - -class OpenAIFile(_model_base.Model): - """Represents an agent 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.projects.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.projects.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 OpenAIPageableListOfAgent(_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.projects.models.Agent] - :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.Agent"] = 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.Agent"], - 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.projects.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.projects.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.projects.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.projects.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.projects.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 OpenApiAuthDetails(_model_base.Model): - """authentication details for OpenApiFunctionDefinition. - - You probably want to use the sub-classes and not this class directly. Known sub-classes are: - OpenApiAnonymousAuthDetails, OpenApiConnectionAuthDetails, OpenApiManagedAuthDetails - - - :ivar type: The type of authentication, must be anonymous/connection/managed_identity. - Required. Known values are: "anonymous", "connection", and "managed_identity". - :vartype type: str or ~azure.ai.projects.models.OpenApiAuthType - """ - - __mapping__: Dict[str, _model_base.Model] = {} - type: str = rest_discriminator(name="type") - """The type of authentication, must be anonymous/connection/managed_identity. Required. Known - values are: \"anonymous\", \"connection\", and \"managed_identity\".""" - - @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 OpenApiAnonymousAuthDetails(OpenApiAuthDetails, discriminator="anonymous"): - """Security details for OpenApi anonymous authentication. - - - :ivar type: The object type, which is always 'anonymous'. Required. - :vartype type: str or ~azure.ai.projects.models.ANONYMOUS - """ - - type: Literal[OpenApiAuthType.ANONYMOUS] = rest_discriminator(name="type") # type: ignore - """The object type, which is always 'anonymous'. 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=OpenApiAuthType.ANONYMOUS, **kwargs) - - -class OpenApiConnectionAuthDetails(OpenApiAuthDetails, discriminator="connection"): - """Security details for OpenApi connection authentication. - - - :ivar type: The object type, which is always 'connection'. Required. - :vartype type: str or ~azure.ai.projects.models.CONNECTION - :ivar security_scheme: Connection auth security details. Required. - :vartype security_scheme: ~azure.ai.projects.models.OpenApiConnectionSecurityScheme - """ - - type: Literal[OpenApiAuthType.CONNECTION] = rest_discriminator(name="type") # type: ignore - """The object type, which is always 'connection'. Required.""" - security_scheme: "_models.OpenApiConnectionSecurityScheme" = rest_field() - """Connection auth security details. Required.""" - - @overload - def __init__( - self, - *, - security_scheme: "_models.OpenApiConnectionSecurityScheme", - ) -> 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=OpenApiAuthType.CONNECTION, **kwargs) - - -class OpenApiConnectionSecurityScheme(_model_base.Model): - """Security scheme for OpenApi managed_identity authentication. - - - :ivar connection_id: Connection id for Connection auth type. Required. - :vartype connection_id: str - """ - - connection_id: str = rest_field() - """Connection id for Connection auth type. Required.""" - - @overload - def __init__( - self, - *, - connection_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 OpenApiFunctionDefinition(_model_base.Model): - """The input definition information for an openapi 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 spec: The openapi function shape, described as a JSON Schema object. Required. - :vartype spec: any - :ivar auth: Open API authentication details. Required. - :vartype auth: ~azure.ai.projects.models.OpenApiAuthDetails - """ - - 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.""" - spec: Any = rest_field() - """The openapi function shape, described as a JSON Schema object. Required.""" - auth: "_models.OpenApiAuthDetails" = rest_field() - """Open API authentication details. Required.""" - - @overload - def __init__( - self, - *, - name: str, - spec: Any, - auth: "_models.OpenApiAuthDetails", - 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 OpenApiManagedAuthDetails(OpenApiAuthDetails, discriminator="managed_identity"): - """Security details for OpenApi managed_identity authentication. - - - :ivar type: The object type, which is always 'managed_identity'. Required. - :vartype type: str or ~azure.ai.projects.models.MANAGED_IDENTITY - :ivar security_scheme: Connection auth security details. Required. - :vartype security_scheme: ~azure.ai.projects.models.OpenApiManagedSecurityScheme - """ - - type: Literal[OpenApiAuthType.MANAGED_IDENTITY] = rest_discriminator(name="type") # type: ignore - """The object type, which is always 'managed_identity'. Required.""" - security_scheme: "_models.OpenApiManagedSecurityScheme" = rest_field() - """Connection auth security details. Required.""" - - @overload - def __init__( - self, - *, - security_scheme: "_models.OpenApiManagedSecurityScheme", - ) -> 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=OpenApiAuthType.MANAGED_IDENTITY, **kwargs) - - -class OpenApiManagedSecurityScheme(_model_base.Model): - """Security scheme for OpenApi managed_identity authentication. - - - :ivar audience: Authentication scope for managed_identity auth type. Required. - :vartype audience: str - """ - - audience: str = rest_field() - """Authentication scope for managed_identity auth type. Required.""" - - @overload - def __init__( - self, - *, - audience: 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 OpenApiToolDefinition(ToolDefinition, discriminator="openapi"): - """The input definition information for an OpenAPI tool as used to configure an agent. - - - :ivar type: The object type, which is always 'openapi'. Required. Default value is "openapi". - :vartype type: str - :ivar openapi: The openapi function definition. Required. - :vartype openapi: ~azure.ai.projects.models.OpenApiFunctionDefinition - """ - - type: Literal["openapi"] = rest_discriminator(name="type") # type: ignore - """The object type, which is always 'openapi'. Required. Default value is \"openapi\".""" - openapi: "_models.OpenApiFunctionDefinition" = rest_field() - """The openapi function definition. Required.""" - - @overload - def __init__( - self, - *, - openapi: "_models.OpenApiFunctionDefinition", - ) -> 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="openapi", **kwargs) - - -class RecurrenceSchedule(_model_base.Model): - """RecurrenceSchedule Definition. - - - :ivar hours: List of hours for the schedule. Required. - :vartype hours: list[int] - :ivar minutes: List of minutes for the schedule. Required. - :vartype minutes: list[int] - :ivar week_days: List of days for the schedule. - :vartype week_days: list[str or ~azure.ai.projects.models.WeekDays] - :ivar month_days: List of month days for the schedule. - :vartype month_days: list[int] - """ - - hours: List[int] = rest_field() - """List of hours for the schedule. Required.""" - minutes: List[int] = rest_field() - """List of minutes for the schedule. Required.""" - week_days: Optional[List[Union[str, "_models.WeekDays"]]] = rest_field(name="weekDays") - """List of days for the schedule.""" - month_days: Optional[List[int]] = rest_field(name="monthDays") - """List of month days for the schedule.""" - - @overload - def __init__( - self, - *, - hours: List[int], - minutes: List[int], - week_days: Optional[List[Union[str, "_models.WeekDays"]]] = None, - month_days: Optional[List[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 RecurrenceTrigger(Trigger, discriminator="Recurrence"): - """Recurrence Trigger Definition. - - Readonly variables are only populated by the server, and will be ignored when sending a request. - - - :ivar type: Required. Default value is "Recurrence". - :vartype type: str - :ivar frequency: The frequency to trigger schedule. Required. Known values are: "Month", - "Week", "Day", "Hour", and "Minute". - :vartype frequency: str or ~azure.ai.projects.models.Frequency - :ivar interval: Specifies schedule interval in conjunction with frequency. Required. - :vartype interval: int - :ivar schedule: The recurrence schedule. - :vartype schedule: ~azure.ai.projects.models.RecurrenceSchedule - """ - - type: Literal["Recurrence"] = rest_discriminator(name="type", visibility=["read"]) # type: ignore - """Required. Default value is \"Recurrence\".""" - frequency: Union[str, "_models.Frequency"] = rest_field() - """The frequency to trigger schedule. Required. Known values are: \"Month\", \"Week\", \"Day\", - \"Hour\", and \"Minute\".""" - interval: int = rest_field() - """Specifies schedule interval in conjunction with frequency. Required.""" - schedule: Optional["_models.RecurrenceSchedule"] = rest_field() - """The recurrence schedule.""" - - @overload - def __init__( - self, - *, - frequency: Union[str, "_models.Frequency"], - interval: int, - schedule: Optional["_models.RecurrenceSchedule"] = 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="Recurrence", **kwargs) - - -class RequiredAction(_model_base.Model): - """An abstract representation of a required action for an agent 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 of 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.projects.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 ResponseFormatJsonSchema(_model_base.Model): - """A description of what the response format is for, used by the model to determine how to respond - in the format. - - - :ivar description: A description of what the response format is for, used by the model to - determine how to respond in the format. - :vartype description: str - :ivar name: The name of a schema. Required. - :vartype name: str - :ivar schema: The JSON schema object, describing the response format. Required. - :vartype schema: any - """ - - description: Optional[str] = rest_field() - """A description of what the response format is for, used by the model to determine how to respond - in the format.""" - name: str = rest_field() - """The name of a schema. Required.""" - schema: Any = rest_field() - """The JSON schema object, describing the response format. Required.""" - - @overload - def __init__( - self, - *, - name: str, - schema: 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 ResponseFormatJsonSchemaType(_model_base.Model): - """The type of response format being defined: ``json_schema``. - - Readonly variables are only populated by the server, and will be ignored when sending a request. - - - :ivar type: Type. Required. Default value is "json_schema". - :vartype type: str - :ivar json_schema: The JSON schema, describing response format. Required. - :vartype json_schema: ~azure.ai.projects.models.ResponseFormatJsonSchema - """ - - type: Literal["json_schema"] = rest_field() - """Type. Required. Default value is \"json_schema\".""" - json_schema: "_models.ResponseFormatJsonSchema" = rest_field() - """The JSON schema, describing response format. Required.""" - - @overload - def __init__( - self, - *, - json_schema: "_models.ResponseFormatJsonSchema", - ) -> 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.type: Literal["json_schema"] = "json_schema" - - -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 agent 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 agent 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.projects.models.RunStepType - :ivar assistant_id: The ID of the agent 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.projects.models.RunStepStatus - :ivar step_details: The details for this run step. Required. - :vartype step_details: ~azure.ai.projects.models.RunStepDetails - :ivar last_error: If applicable, information about the last error encountered by this run step. - Required. - :vartype last_error: ~azure.ai.projects.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.projects.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 agent 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 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: - RunStepAzureAISearchToolCall, RunStepBingGroundingToolCall, RunStepCodeInterpreterToolCall, - RunStepMicrosoftFabricToolCall, RunStepFileSearchToolCall, RunStepFunctionToolCall, - RunStepSharepointToolCall - - - :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 RunStepAzureAISearchToolCall(RunStepToolCall, discriminator="azure_ai_search"): - """A record of a call to an Azure AI Search tool, issued by the model in evaluation of a defined - tool, that represents - executed Azure AI 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 'azure_ai_search'. Required. Default value is - "azure_ai_search". - :vartype type: str - :ivar azure_ai_search: Reserved for future use. Required. - :vartype azure_ai_search: dict[str, str] - """ - - type: Literal["azure_ai_search"] = rest_discriminator(name="type") # type: ignore - """The object type, which is always 'azure_ai_search'. Required. Default value is - \"azure_ai_search\".""" - azure_ai_search: Dict[str, str] = rest_field() - """Reserved for future use. Required.""" - - @overload - def __init__( - self, - *, - id: str, # pylint: disable=redefined-builtin - azure_ai_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="azure_ai_search", **kwargs) - - -class RunStepBingGroundingToolCall(RunStepToolCall, discriminator="bing_grounding"): - """A record of a call to a bing grounding tool, issued by the model in evaluation of a defined - tool, that represents - executed search with bing grounding. - - - :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 'bing_grounding'. Required. Default value is - "bing_grounding". - :vartype type: str - :ivar bing_grounding: Reserved for future use. Required. - :vartype bing_grounding: dict[str, str] - """ - - type: Literal["bing_grounding"] = rest_discriminator(name="type") # type: ignore - """The object type, which is always 'bing_grounding'. Required. Default value is - \"bing_grounding\".""" - bing_grounding: Dict[str, str] = rest_field() - """Reserved for future use. Required.""" - - @overload - def __init__( - self, - *, - id: str, # pylint: disable=redefined-builtin - bing_grounding: 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="bing_grounding", **kwargs) - - -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.projects.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 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.projects.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.projects.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 RunStepDelta(_model_base.Model): - """Represents the delta payload in a streaming run step delta chunk. - - :ivar step_details: The details of the run step. - :vartype step_details: ~azure.ai.projects.models.RunStepDeltaDetail - """ - - step_details: Optional["_models.RunStepDeltaDetail"] = rest_field() - """The details of the run step.""" - - @overload - def __init__( - self, - *, - step_details: Optional["_models.RunStepDeltaDetail"] = 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 RunStepDeltaChunk(_model_base.Model): - """Represents a run step delta i.e. any changed fields on a run step during streaming. - - Readonly variables are only populated by the server, and will be ignored when sending a request. - - - :ivar id: The identifier of the run step, which can be referenced in API endpoints. Required. - :vartype id: str - :ivar object: The object type, which is always ``thread.run.step.delta``. Required. Default - value is "thread.run.step.delta". - :vartype object: str - :ivar delta: The delta containing the fields that have changed on the run step. Required. - :vartype delta: ~azure.ai.projects.models.RunStepDelta - """ - - id: str = rest_field() - """The identifier of the run step, which can be referenced in API endpoints. Required.""" - object: Literal["thread.run.step.delta"] = rest_field() - """The object type, which is always ``thread.run.step.delta``. Required. Default value is - \"thread.run.step.delta\".""" - delta: "_models.RunStepDelta" = rest_field() - """The delta containing the fields that have changed on the run step. Required.""" - - @overload - def __init__( - self, - *, - id: str, # pylint: disable=redefined-builtin - delta: "_models.RunStepDelta", - ) -> 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.delta"] = "thread.run.step.delta" - - -class RunStepDeltaCodeInterpreterDetailItemObject(_model_base.Model): # pylint: disable=name-too-long - """Represents the Code Interpreter tool call data in a streaming run step's tool calls. - - :ivar input: The input into the Code Interpreter tool call. - :vartype input: str - :ivar outputs: The outputs from the Code Interpreter tool call. Code Interpreter can output one - or more - items, including text (\\ ``logs``\\ ) or images (\\ ``image``\\ ). Each of these are - represented by a - different object type. - :vartype outputs: list[~azure.ai.projects.models.RunStepDeltaCodeInterpreterOutput] - """ - - input: Optional[str] = rest_field() - """The input into the Code Interpreter tool call.""" - outputs: Optional[List["_models.RunStepDeltaCodeInterpreterOutput"]] = rest_field() - """The outputs from the Code Interpreter tool call. Code Interpreter can output one or more - items, including text (``logs``) or images (``image``). Each of these are represented - by a - different object type.""" - - @overload - def __init__( - self, - *, - input: Optional[str] = None, - outputs: Optional[List["_models.RunStepDeltaCodeInterpreterOutput"]] = 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 RunStepDeltaCodeInterpreterOutput(_model_base.Model): - """The abstract base representation of a streaming run step tool call's Code Interpreter tool - output. - - You probably want to use the sub-classes and not this class directly. Known sub-classes are: - RunStepDeltaCodeInterpreterImageOutput, RunStepDeltaCodeInterpreterLogOutput - - - :ivar index: The index of the output in the streaming run step tool call's Code Interpreter - outputs array. Required. - :vartype index: int - :ivar type: The type of the streaming run step tool call's Code Interpreter output. Required. - Default value is None. - :vartype type: str - """ - - __mapping__: Dict[str, _model_base.Model] = {} - index: int = rest_field() - """The index of the output in the streaming run step tool call's Code Interpreter outputs array. - Required.""" - type: str = rest_discriminator(name="type") - """The type of the streaming run step tool call's Code Interpreter output. Required. Default value - is None.""" - - @overload - def __init__( - self, - *, - index: int, - 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 RunStepDeltaCodeInterpreterImageOutput(RunStepDeltaCodeInterpreterOutput, discriminator="image"): - """Represents an image output as produced the Code interpreter tool and as represented in a - streaming run step's delta tool calls collection. - - - :ivar index: The index of the output in the streaming run step tool call's Code Interpreter - outputs array. Required. - :vartype index: int - :ivar type: The object type, which is always "image.". Required. Default value is "image". - :vartype type: str - :ivar image: The image data for the Code Interpreter tool call output. - :vartype image: ~azure.ai.projects.models.RunStepDeltaCodeInterpreterImageOutputObject - """ - - type: Literal["image"] = rest_discriminator(name="type") # type: ignore - """The object type, which is always \"image.\". Required. Default value is \"image\".""" - image: Optional["_models.RunStepDeltaCodeInterpreterImageOutputObject"] = rest_field() - """The image data for the Code Interpreter tool call output.""" - - @overload - def __init__( - self, - *, - index: int, - image: Optional["_models.RunStepDeltaCodeInterpreterImageOutputObject"] = 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="image", **kwargs) - - -class RunStepDeltaCodeInterpreterImageOutputObject(_model_base.Model): # pylint: disable=name-too-long - """Represents the data for a streaming run step's Code Interpreter tool call image output. - - :ivar file_id: The file ID for the image. - :vartype file_id: str - """ - - file_id: Optional[str] = rest_field() - """The file ID for the image.""" - - @overload - def __init__( - self, - *, - file_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 RunStepDeltaCodeInterpreterLogOutput(RunStepDeltaCodeInterpreterOutput, discriminator="logs"): - """Represents a log output as produced by the Code Interpreter tool and as represented in a - streaming run step's delta tool calls collection. - - - :ivar index: The index of the output in the streaming run step tool call's Code Interpreter - outputs array. Required. - :vartype index: int - :ivar type: The type of the object, which is always "logs.". Required. Default value is "logs". - :vartype type: str - :ivar logs: The text output from the Code Interpreter tool call. - :vartype logs: str - """ - - type: Literal["logs"] = rest_discriminator(name="type") # type: ignore - """The type of the object, which is always \"logs.\". Required. Default value is \"logs\".""" - logs: Optional[str] = rest_field() - """The text output from the Code Interpreter tool call.""" - - @overload - def __init__( - self, - *, - index: int, - logs: 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="logs", **kwargs) - - -class RunStepDeltaToolCall(_model_base.Model): - """The abstract base representation of a single tool call within a streaming run step's delta tool - call details. - - You probably want to use the sub-classes and not this class directly. Known sub-classes are: - RunStepDeltaCodeInterpreterToolCall, RunStepDeltaFileSearchToolCall, - RunStepDeltaFunctionToolCall - - - :ivar index: The index of the tool call detail in the run step's tool_calls array. Required. - :vartype index: int - :ivar id: The ID of the tool call, used when submitting outputs to the run. Required. - :vartype id: str - :ivar type: The type of the tool call detail item in a streaming run step's details. Required. - Default value is None. - :vartype type: str - """ - - __mapping__: Dict[str, _model_base.Model] = {} - index: int = rest_field() - """The index of the tool call detail in the run step's tool_calls array. Required.""" - id: str = rest_field() - """The ID of the tool call, used when submitting outputs to the run. Required.""" - type: str = rest_discriminator(name="type") - """The type of the tool call detail item in a streaming run step's details. Required. Default - value is None.""" - - @overload - def __init__( - self, - *, - index: int, - id: str, # pylint: disable=redefined-builtin - 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 RunStepDeltaCodeInterpreterToolCall(RunStepDeltaToolCall, discriminator="code_interpreter"): - """Represents a Code Interpreter tool call within a streaming run step's tool call details. - - - :ivar index: The index of the tool call detail in the run step's tool_calls array. Required. - :vartype index: int - :ivar id: The ID of the tool call, used when submitting outputs to the run. 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 Code Interpreter data for the tool call. - :vartype code_interpreter: - ~azure.ai.projects.models.RunStepDeltaCodeInterpreterDetailItemObject - """ - - 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: Optional["_models.RunStepDeltaCodeInterpreterDetailItemObject"] = rest_field() - """The Code Interpreter data for the tool call.""" - - @overload - def __init__( - self, - *, - index: int, - id: str, # pylint: disable=redefined-builtin - code_interpreter: Optional["_models.RunStepDeltaCodeInterpreterDetailItemObject"] = 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="code_interpreter", **kwargs) - - -class RunStepDeltaDetail(_model_base.Model): - """Represents a single run step detail item in a streaming run step's delta payload. - - You probably want to use the sub-classes and not this class directly. Known sub-classes are: - RunStepDeltaMessageCreation, RunStepDeltaToolCallObject - - - :ivar type: The object type for the run step detail object. Required. Default value is None. - :vartype type: str - """ - - __mapping__: Dict[str, _model_base.Model] = {} - type: str = rest_discriminator(name="type") - """The object type for the run step detail object. 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 RunStepDeltaFileSearchToolCall(RunStepDeltaToolCall, discriminator="file_search"): - """Represents a file search tool call within a streaming run step's tool call details. - - - :ivar index: The index of the tool call detail in the run step's tool_calls array. Required. - :vartype index: int - :ivar id: The ID of the tool call, used when submitting outputs to the run. 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. - :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: Optional[Dict[str, str]] = rest_field() - """Reserved for future use.""" - - @overload - def __init__( - self, - *, - index: int, - id: str, # pylint: disable=redefined-builtin - file_search: 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, type="file_search", **kwargs) - - -class RunStepDeltaFunction(_model_base.Model): - """Represents the function data in a streaming run step delta's function tool call. - - :ivar name: The name of the function. - :vartype name: str - :ivar arguments: The arguments passed to the function as input. - :vartype arguments: str - :ivar output: The output of the function, null if outputs have not yet been submitted. - :vartype output: str - """ - - name: Optional[str] = rest_field() - """The name of the function.""" - arguments: Optional[str] = rest_field() - """The arguments passed to the function as input.""" - output: Optional[str] = rest_field() - """The output of the function, null if outputs have not yet been submitted.""" - - @overload - def __init__( - self, - *, - name: Optional[str] = None, - arguments: 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 RunStepDeltaFunctionToolCall(RunStepDeltaToolCall, discriminator="function"): - """Represents a function tool call within a streaming run step's tool call details. - - - :ivar index: The index of the tool call detail in the run step's tool_calls array. Required. - :vartype index: int - :ivar id: The ID of the tool call, used when submitting outputs to the run. Required. - :vartype id: str - :ivar type: The object type, which is always "function.". Required. Default value is - "function". - :vartype type: str - :ivar function: The function data for the tool call. - :vartype function: ~azure.ai.projects.models.RunStepDeltaFunction - """ - - type: Literal["function"] = rest_discriminator(name="type") # type: ignore - """The object type, which is always \"function.\". Required. Default value is \"function\".""" - function: Optional["_models.RunStepDeltaFunction"] = rest_field() - """The function data for the tool call.""" - - @overload - def __init__( - self, - *, - index: int, - id: str, # pylint: disable=redefined-builtin - function: Optional["_models.RunStepDeltaFunction"] = 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="function", **kwargs) - - -class RunStepDeltaMessageCreation(RunStepDeltaDetail, discriminator="message_creation"): - """Represents a message creation within a streaming run step delta. - - - :ivar type: The object type, which is always "message_creation.". Required. Default value is - "message_creation". - :vartype type: str - :ivar message_creation: The message creation data. - :vartype message_creation: ~azure.ai.projects.models.RunStepDeltaMessageCreationObject - """ - - type: Literal["message_creation"] = rest_discriminator(name="type") # type: ignore - """The object type, which is always \"message_creation.\". Required. Default value is - \"message_creation\".""" - message_creation: Optional["_models.RunStepDeltaMessageCreationObject"] = rest_field() - """The message creation data.""" - - @overload - def __init__( - self, - *, - message_creation: Optional["_models.RunStepDeltaMessageCreationObject"] = 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="message_creation", **kwargs) - - -class RunStepDeltaMessageCreationObject(_model_base.Model): - """Represents the data within a streaming run step message creation response object. - - :ivar message_id: The ID of the newly-created message. - :vartype message_id: str - """ - - message_id: Optional[str] = rest_field() - """The ID of the newly-created message.""" - - @overload - def __init__( - self, - *, - message_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 RunStepDeltaToolCallObject(RunStepDeltaDetail, discriminator="tool_calls"): - """Represents an invocation of tool calls as part of a streaming run step. - - - :ivar type: The object type, which is always "tool_calls.". Required. Default value is - "tool_calls". - :vartype type: str - :ivar tool_calls: The collection of tool calls for the tool call detail item. - :vartype tool_calls: list[~azure.ai.projects.models.RunStepDeltaToolCall] - """ - - type: Literal["tool_calls"] = rest_discriminator(name="type") # type: ignore - """The object type, which is always \"tool_calls.\". Required. Default value is \"tool_calls\".""" - tool_calls: Optional[List["_models.RunStepDeltaToolCall"]] = rest_field() - """The collection of tool calls for the tool call detail item.""" - - @overload - def __init__( - self, - *, - tool_calls: Optional[List["_models.RunStepDeltaToolCall"]] = 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="tool_calls", **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.projects.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.projects.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 type: The object type, which is always 'file_search'. Required. Default value is - "file_search". - :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 - :ivar file_search: For now, this is always going to be an empty object. Required. - :vartype file_search: ~azure.ai.projects.models.RunStepFileSearchToolCallResults - """ - - 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: "_models.RunStepFileSearchToolCallResults" = rest_field() - """For now, this is always going to be an empty object. Required.""" - - @overload - def __init__( - self, - *, - id: str, # pylint: disable=redefined-builtin - file_search: "_models.RunStepFileSearchToolCallResults", - ) -> 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 RunStepFileSearchToolCallResult(_model_base.Model): - """File search tool call result. - - - :ivar file_id: The ID of the file that result was found in. Required. - :vartype file_id: str - :ivar file_name: The name of the file that result was found in. Required. - :vartype file_name: str - :ivar score: The score of the result. All values must be a floating point number between 0 and - 1. Required. - :vartype score: float - :ivar content: The content of the result that was found. The content is only included if - requested via the include query parameter. - :vartype content: list[~azure.ai.projects.models.FileSearchToolCallContent] - """ - - file_id: str = rest_field() - """The ID of the file that result was found in. Required.""" - file_name: str = rest_field() - """The name of the file that result was found in. Required.""" - score: float = rest_field() - """The score of the result. All values must be a floating point number between 0 and 1. Required.""" - content: Optional[List["_models.FileSearchToolCallContent"]] = rest_field() - """The content of the result that was found. The content is only included if requested via the - include query parameter.""" - - @overload - def __init__( - self, - *, - file_id: str, - file_name: str, - score: float, - content: Optional[List["_models.FileSearchToolCallContent"]] = 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 RunStepFileSearchToolCallResults(_model_base.Model): - """The results of the file search. - - - :ivar ranking_options: Ranking options for file search. - :vartype ranking_options: ~azure.ai.projects.models.FileSearchRankingOptions - :ivar results: The array of a file search results. Required. - :vartype results: list[~azure.ai.projects.models.RunStepFileSearchToolCallResult] - """ - - ranking_options: Optional["_models.FileSearchRankingOptions"] = rest_field() - """Ranking options for file search.""" - results: List["_models.RunStepFileSearchToolCallResult"] = rest_field() - """The array of a file search results. Required.""" - - @overload - def __init__( - self, - *, - results: List["_models.RunStepFileSearchToolCallResult"], - ranking_options: Optional["_models.FileSearchRankingOptions"] = 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 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.projects.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.projects.models.MESSAGE_CREATION - :ivar message_creation: Information about the message creation associated with this run step. - Required. - :vartype message_creation: ~azure.ai.projects.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 RunStepMicrosoftFabricToolCall(RunStepToolCall, discriminator="fabric_aiskill"): - """A record of a call to a Microsoft Fabric tool, issued by the model in evaluation of a defined - tool, that represents - executed Microsoft Fabric operations. - - - :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 'fabric_aiskill'. Required. Default value is - "fabric_aiskill". - :vartype type: str - :ivar microsoft_fabric: Reserved for future use. Required. - :vartype microsoft_fabric: dict[str, str] - """ - - type: Literal["fabric_aiskill"] = rest_discriminator(name="type") # type: ignore - """The object type, which is always 'fabric_aiskill'. Required. Default value is - \"fabric_aiskill\".""" - microsoft_fabric: Dict[str, str] = rest_field(name="fabric_aiskill") - """Reserved for future use. Required.""" - - @overload - def __init__( - self, - *, - id: str, # pylint: disable=redefined-builtin - microsoft_fabric: 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="fabric_aiskill", **kwargs) - - -class RunStepSharepointToolCall(RunStepToolCall, discriminator="sharepoint_grounding"): - """A record of a call to a SharePoint tool, issued by the model in evaluation of a defined tool, - that represents - executed SharePoint actions. - - - :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 'sharepoint_grounding'. Required. Default value is - "sharepoint_grounding". - :vartype type: str - :ivar share_point: Reserved for future use. Required. - :vartype share_point: dict[str, str] - """ - - type: Literal["sharepoint_grounding"] = rest_discriminator(name="type") # type: ignore - """The object type, which is always 'sharepoint_grounding'. Required. Default value is - \"sharepoint_grounding\".""" - share_point: Dict[str, str] = rest_field(name="sharepoint_grounding") - """Reserved for future use. Required.""" - - @overload - def __init__( - self, - *, - id: str, # pylint: disable=redefined-builtin - share_point: 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="sharepoint_grounding", **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.projects.models.TOOL_CALLS - :ivar tool_calls: A list of tool call details for this run step. Required. - :vartype tool_calls: list[~azure.ai.projects.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 SharepointToolDefinition(ToolDefinition, discriminator="sharepoint_grounding"): - """The input definition information for a sharepoint tool as used to configure an agent. - - - :ivar type: The object type, which is always 'sharepoint_grounding'. Required. Default value is - "sharepoint_grounding". - :vartype type: str - :ivar sharepoint_grounding: The list of connections used by the SharePoint tool. Required. - :vartype sharepoint_grounding: ~azure.ai.projects.models.ToolConnectionList - """ - - type: Literal["sharepoint_grounding"] = rest_discriminator(name="type") # type: ignore - """The object type, which is always 'sharepoint_grounding'. Required. Default value is - \"sharepoint_grounding\".""" - sharepoint_grounding: "_models.ToolConnectionList" = rest_field() - """The list of connections used by the SharePoint tool. Required.""" - - @overload - def __init__( - self, - *, - sharepoint_grounding: "_models.ToolConnectionList", - ) -> 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="sharepoint_grounding", **kwargs) - - -class SubmitToolOutputsAction(RequiredAction, discriminator="submit_tool_outputs"): - """The details for required tool calls that must be submitted for an agent 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.projects.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 agent thread run to - continue. Required. - :vartype tool_calls: list[~azure.ai.projects.models.RequiredToolCall] - """ - - tool_calls: List["_models.RequiredToolCall"] = rest_field() - """The list of tool calls that must be resolved for the agent 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 agent 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.projects.models.MessageStatus - :ivar incomplete_details: On an incomplete message, details about why the message is - incomplete. Required. - :vartype incomplete_details: ~azure.ai.projects.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 agent thread message. Required. Known values are: - "user" and "assistant". - :vartype role: str or ~azure.ai.projects.models.MessageRole - :ivar content: The list of content items associated with the agent thread message. Required. - :vartype content: list[~azure.ai.projects.models.MessageContent] - :ivar assistant_id: If applicable, the ID of the agent 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.projects.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 agent thread message. Required. Known values are: \"user\" and - \"assistant\".""" - content: List["_models.MessageContent"] = rest_field() - """The list of content items associated with the agent thread message. Required.""" - assistant_id: str = rest_field() - """If applicable, the ID of the agent 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 agent 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 agent. Use this value to insert - messages from the agent into the - conversation. Required. Known values are: "user" and "assistant". - :vartype role: str or ~azure.ai.projects.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.projects.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 agent. Use this value to insert - messages from the agent 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 agent 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 agent associated with the thread this run was performed - against. Required. - :vartype assistant_id: str - :ivar status: The status of the agent thread run. Required. Known values are: "queued", - "in_progress", "requires_action", "cancelling", "cancelled", "failed", "completed", and - "expired". - :vartype status: str or ~azure.ai.projects.models.RunStatus - :ivar required_action: The details of the action required for the agent thread run to continue. - :vartype required_action: ~azure.ai.projects.models.RequiredAction - :ivar last_error: The last error, if any, encountered by this agent thread run. Required. - :vartype last_error: ~azure.ai.projects.models.RunError - :ivar model: The ID of the model to use. Required. - :vartype model: str - :ivar instructions: The overridden system instructions used for this agent thread run. - Required. - :vartype instructions: str - :ivar tools: The overridden enabled tools used for this agent thread run. Required. - :vartype tools: list[~azure.ai.projects.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. - :vartype incomplete_details: ~azure.ai.projects.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.projects.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.projects.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.AgentsApiToolChoiceOptionMode"], - AgentsNamedToolChoice - :vartype tool_choice: str or str or ~azure.ai.projects.models.AgentsApiToolChoiceOptionMode or - ~azure.ai.projects.models.AgentsNamedToolChoice - :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.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat, ResponseFormatJsonSchemaType - :vartype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat or - ~azure.ai.projects.models.ResponseFormatJsonSchemaType - :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] - :ivar tool_resources: Override the tools the agent can use for this run. This is useful for - modifying the behavior on a per-run basis. - :vartype tool_resources: ~azure.ai.projects.models.UpdateToolResourcesOptions - :ivar parallel_tool_calls: Determines if tools can be executed in parallel within the run. - Required. - :vartype parallel_tool_calls: bool - """ - - 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 agent associated with the thread this run was performed against. Required.""" - status: Union[str, "_models.RunStatus"] = rest_field() - """The status of the agent 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 agent thread run to continue.""" - last_error: "_models.RunError" = rest_field() - """The last error, if any, encountered by this agent 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 agent thread run. Required.""" - tools: List["_models.ToolDefinition"] = rest_field() - """The overridden enabled tools used for this agent 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: "_models.IncompleteRunDetails" = rest_field() - """Details on why the run is incomplete. Will be ``null`` if the run is not incomplete. Required.""" - 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.AgentsApiToolChoiceOption" = 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.AgentsApiToolChoiceOptionMode\"], - AgentsNamedToolChoice""" - response_format: "_types.AgentsApiResponseFormatOption" = rest_field() - """The response format of the tool calls used in this run. Required. Is one of the following - types: str, Union[str, \"_models.AgentsApiResponseFormatMode\"], AgentsApiResponseFormat, - ResponseFormatJsonSchemaType""" - 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.""" - tool_resources: Optional["_models.UpdateToolResourcesOptions"] = rest_field() - """Override the tools the agent can use for this run. This is useful for modifying the behavior on - a per-run basis.""" - parallel_tool_calls: bool = rest_field() - """Determines if tools can be executed in parallel within the run. 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: "_models.IncompleteRunDetails", - usage: "_models.RunCompletionUsage", - max_prompt_tokens: int, - max_completion_tokens: int, - truncation_strategy: "_models.TruncationObject", - tool_choice: "_types.AgentsApiToolChoiceOption", - response_format: "_types.AgentsApiResponseFormatOption", - metadata: Dict[str, str], - parallel_tool_calls: bool, - required_action: Optional["_models.RequiredAction"] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - tool_resources: Optional["_models.UpdateToolResourcesOptions"] = 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 ToolConnection(_model_base.Model): - """A connection resource. - - - :ivar connection_id: A connection in a ToolConnectionList attached to this tool. Required. - :vartype connection_id: str - """ - - connection_id: str = rest_field() - """A connection in a ToolConnectionList attached to this tool. Required.""" - - @overload - def __init__( - self, - *, - connection_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 ToolConnectionList(_model_base.Model): - """A set of connection resources currently used by either the ``bing_grounding``\\ , - ``fabric_aiskill``\\ , or ``sharepoint_grounding`` tools. - - :ivar connection_list: The connections attached to this tool. There can be a maximum of 1 - connection - resource attached to the tool. - :vartype connection_list: list[~azure.ai.projects.models.ToolConnection] - """ - - connection_list: Optional[List["_models.ToolConnection"]] = rest_field(name="connections") - """The connections attached to this tool. There can be a maximum of 1 connection - resource attached to the tool.""" - - @overload - def __init__( - self, - *, - connection_list: Optional[List["_models.ToolConnection"]] = 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 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 agent'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.projects.models.CodeInterpreterToolResource - :ivar file_search: Resources to be used by the ``file_search`` tool consisting of vector store - IDs. - :vartype file_search: ~azure.ai.projects.models.FileSearchToolResource - :ivar azure_ai_search: Resources to be used by the ``azure_ai_search`` tool consisting of index - IDs and names. - :vartype azure_ai_search: ~azure.ai.projects.models.AzureAISearchResource - """ - - 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.""" - azure_ai_search: Optional["_models.AzureAISearchResource"] = rest_field() - """Resources to be used by the ``azure_ai_search`` tool consisting of index IDs and names.""" - - @overload - def __init__( - self, - *, - code_interpreter: Optional["_models.CodeInterpreterToolResource"] = None, - file_search: Optional["_models.FileSearchToolResource"] = None, - azure_ai_search: Optional["_models.AzureAISearchResource"] = 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.projects.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 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 agent. - :vartype file_ids: list[str] - """ - - file_ids: Optional[List[str]] = rest_field() - """A list of file IDs to override the current list of the agent.""" - - @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 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 agent. - :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 agent.""" - - @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 agent'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.projects.models.UpdateCodeInterpreterToolResourceOptions - :ivar file_search: Overrides the vector store attached to this agent. There can be a maximum of - 1 vector store attached to the agent. - :vartype file_search: ~azure.ai.projects.models.UpdateFileSearchToolResourceOptions - :ivar azure_ai_search: Overrides the resources to be used by the ``azure_ai_search`` tool - consisting of index IDs and names. - :vartype azure_ai_search: ~azure.ai.projects.models.AzureAISearchResource - """ - - 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 agent. There can be a maximum of 1 vector store - attached to the agent.""" - azure_ai_search: Optional["_models.AzureAISearchResource"] = rest_field() - """Overrides the resources to be used by the ``azure_ai_search`` tool consisting of index IDs and - names.""" - - @overload - def __init__( - self, - *, - code_interpreter: Optional["_models.UpdateCodeInterpreterToolResourceOptions"] = None, - file_search: Optional["_models.UpdateFileSearchToolResourceOptions"] = None, - azure_ai_search: Optional["_models.AzureAISearchResource"] = 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 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.projects.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.projects.models.VectorStoreStatus - :ivar expires_after: Details on when this vector store expires. - :vartype expires_after: ~azure.ai.projects.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.projects.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.projects.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.projects.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.projects.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 VectorStoreConfiguration(_model_base.Model): - """Vector storage configuration is the list of data sources, used when multiple - files can be used for the enterprise file search. - - - :ivar data_sources: Data sources. Required. - :vartype data_sources: list[~azure.ai.projects.models.VectorStoreDataSource] - """ - - data_sources: List["_models.VectorStoreDataSource"] = rest_field() - """Data sources. Required.""" + """Evaluation's properties. Unlike tags, properties are add-only. Once added, a property cannot be + removed.""" + is_enabled: Optional[str] = rest_field(name="isEnabled", visibility=["read"]) + """Enabled status of the evaluation. It is set by service and is read-only.""" + evaluators: Dict[str, "_models.EvaluatorConfiguration"] = rest_field(visibility=["read", "create"]) + """Evaluators to be used for the evaluation. Required.""" + trigger: "_models.Trigger" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Trigger for the evaluation. Required.""" @overload def __init__( self, *, - data_sources: List["_models.VectorStoreDataSource"], + data: "_models.ApplicationInsightsConfiguration", + evaluators: Dict[str, "_models.EvaluatorConfiguration"], + trigger: "_models.Trigger", + description: Optional[str] = None, + tags: Optional[Dict[str, str]] = None, + properties: Optional[Dict[str, str]] = None, ) -> None: ... @overload @@ -6593,28 +453,36 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) -class VectorStoreConfigurations(_model_base.Model): - """The structure, containing the list of vector storage configurations i.e. the list of azure - asset IDs. +class EvaluatorConfiguration(_model_base.Model): + """Evaluator Configuration. - :ivar store_name: Name. Required. - :vartype store_name: str - :ivar store_configuration: Configurations. Required. - :vartype store_configuration: ~azure.ai.projects.models.VectorStoreConfiguration + :ivar id: Identifier of the evaluator. Required. + :vartype id: str + :ivar init_params: Initialization parameters of the evaluator. + :vartype init_params: dict[str, any] + :ivar data_mapping: Data parameters of the evaluator. + :vartype data_mapping: dict[str, str] """ - store_name: str = rest_field(name="name") - """Name. Required.""" - store_configuration: "_models.VectorStoreConfiguration" = rest_field(name="configuration") - """Configurations. Required.""" + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Identifier of the evaluator. Required.""" + init_params: Optional[Dict[str, Any]] = rest_field( + name="initParams", visibility=["read", "create", "update", "delete", "query"] + ) + """Initialization parameters of the evaluator.""" + data_mapping: Optional[Dict[str, str]] = rest_field( + name="dataMapping", visibility=["read", "create", "update", "delete", "query"] + ) + """Data parameters of the evaluator.""" @overload def __init__( self, *, - store_name: str, - store_configuration: "_models.VectorStoreConfiguration", + id: str, # pylint: disable=redefined-builtin + init_params: Optional[Dict[str, Any]] = None, + data_mapping: Optional[Dict[str, str]] = None, ) -> None: ... @overload @@ -6628,29 +496,34 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) -class VectorStoreDataSource(_model_base.Model): - """The structure, containing Azure asset URI path and the asset type of the file used as a data - source - for the enterprise file search. +class GetAppInsightsResponse(_model_base.Model): + """Response from getting properties of the Application Insights resource. - :ivar asset_identifier: Asset URI. Required. - :vartype asset_identifier: str - :ivar asset_type: The asset type. Required. Known values are: "uri_asset" and "id_asset". - :vartype asset_type: str or ~azure.ai.projects.models.VectorStoreDataSourceAssetType + :ivar id: A unique identifier for the resource. Required. + :vartype id: str + :ivar name: The name of the resource. Required. + :vartype name: str + :ivar properties: The properties of the resource. Required. + :vartype properties: ~azure.ai.projects.models._models.AppInsightsProperties """ - asset_identifier: str = rest_field(name="uri") - """Asset URI. Required.""" - asset_type: Union[str, "_models.VectorStoreDataSourceAssetType"] = rest_field(name="type") - """The asset type. Required. Known values are: \"uri_asset\" and \"id_asset\".""" + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A unique identifier for the resource. Required.""" + name: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The name of the resource. Required.""" + properties: "_models._models.AppInsightsProperties" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The properties of the resource. Required.""" @overload def __init__( self, *, - asset_identifier: str, - asset_type: Union[str, "_models.VectorStoreDataSourceAssetType"], + id: str, # pylint: disable=redefined-builtin + name: str, + properties: "_models._models.AppInsightsProperties", ) -> None: ... @overload @@ -6664,35 +537,34 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **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. +class GetConnectionResponse(_model_base.Model): + """Response from the listSecrets operation. - :ivar id: The ID of the resource specified for deletion. Required. + :ivar id: A unique identifier for the connection. 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 + :ivar name: The name of the resource. Required. + :vartype name: str + :ivar properties: The properties of the resource. Required. + :vartype properties: ~azure.ai.projects.models._models.InternalConnectionProperties """ - 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\".""" + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A unique identifier for the connection. Required.""" + name: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The name of the resource. Required.""" + properties: "_models._models.InternalConnectionProperties" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The properties of the resource. Required.""" @overload def __init__( self, *, id: str, # pylint: disable=redefined-builtin - deleted: bool, + name: str, + properties: "_models._models.InternalConnectionProperties", ) -> None: ... @overload @@ -6704,32 +576,36 @@ def __init__(self, mapping: Mapping[str, Any]) -> None: 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. +class GetWorkspaceResponse(_model_base.Model): + """Response from the Workspace - Get operation. - :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.projects.models.VectorStoreExpirationPolicyAnchor - :ivar days: The anchor timestamp after which the expiration policy applies. Required. - :vartype days: int + :ivar id: A unique identifier for the resource. Required. + :vartype id: str + :ivar name: The name of the resource. Required. + :vartype name: str + :ivar properties: The properties of the resource. Required. + :vartype properties: ~azure.ai.projects.models._models.WorkspaceProperties """ - 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.""" + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A unique identifier for the resource. Required.""" + name: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The name of the resource. Required.""" + properties: "_models._models.WorkspaceProperties" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The properties of the resource. Required.""" @overload def __init__( self, *, - anchor: Union[str, "_models.VectorStoreExpirationPolicyAnchor"], - days: int, + id: str, # pylint: disable=redefined-builtin + name: str, + properties: "_models._models.WorkspaceProperties", ) -> None: ... @overload @@ -6743,72 +619,43 @@ 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. +class InternalConnectionProperties(_model_base.Model): + """Connection properties. - Readonly variables are only populated by the server, and will be ignored when sending a request. + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + InternalConnectionPropertiesAADAuth, InternalConnectionPropertiesApiKeyAuth, + InternalConnectionPropertiesNoAuth, InternalConnectionPropertiesSASAuth - :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.projects.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.projects.models.VectorStoreFileError - :ivar chunking_strategy: The strategy used to chunk the file. Required. - :vartype chunking_strategy: ~azure.ai.projects.models.VectorStoreChunkingStrategyResponse + :ivar auth_type: Authentication type of the connection target. Required. Known values are: + "ApiKey", "AAD", "SAS", and "None". + :vartype auth_type: str or ~azure.ai.projects.models.AuthenticationType + :ivar category: Category of the connection. Required. Known values are: "AzureOpenAI", + "Serverless", "AzureBlob", "AIServices", "CognitiveSearch", and "ApiKey". + :vartype category: str or ~azure.ai.projects.models.ConnectionType + :ivar target: The connection URL to be used for this service. Required. + :vartype target: str """ - 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.""" + __mapping__: Dict[str, _model_base.Model] = {} + auth_type: str = rest_discriminator(name="authType", visibility=["read", "create", "update", "delete", "query"]) + """Authentication type of the connection target. Required. Known values are: \"ApiKey\", \"AAD\", + \"SAS\", and \"None\".""" + category: Union[str, "_models.ConnectionType"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Category of the connection. Required. Known values are: \"AzureOpenAI\", \"Serverless\", + \"AzureBlob\", \"AIServices\", \"CognitiveSearch\", and \"ApiKey\".""" + target: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The connection URL to be used for this service. 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", + auth_type: str, + category: Union[str, "_models.ConnectionType"], + target: str, ) -> None: ... @overload @@ -6820,59 +667,33 @@ def __init__(self, mapping: Mapping[str, Any]) -> None: 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. +class InternalConnectionPropertiesAADAuth(InternalConnectionProperties, discriminator="AAD"): + """Connection properties for connections with AAD authentication (aka ``Entra ID passthrough``\\ + ). - :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.projects.models.VectorStoreFileBatchStatus - :ivar file_counts: Files count grouped by status processed or being processed by this vector - store. Required. - :vartype file_counts: ~azure.ai.projects.models.VectorStoreFileCount + :ivar category: Category of the connection. Required. Known values are: "AzureOpenAI", + "Serverless", "AzureBlob", "AIServices", "CognitiveSearch", and "ApiKey". + :vartype category: str or ~azure.ai.projects.models.ConnectionType + :ivar target: The connection URL to be used for this service. Required. + :vartype target: str + :ivar auth_type: Authentication type of the connection target. Required. Entra ID + authentication (formerly known as AAD) + :vartype auth_type: str or ~azure.ai.projects.models.ENTRA_ID """ - 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.""" + auth_type: Literal[AuthenticationType.ENTRA_ID] = rest_discriminator(name="authType", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Authentication type of the connection target. Required. Entra ID authentication (formerly known + as AAD)""" @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", + category: Union[str, "_models.ConnectionType"], + target: str, ) -> None: ... @overload @@ -6883,46 +704,38 @@ def __init__(self, mapping: Mapping[str, Any]) -> None: """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) - self.object: Literal["vector_store.files_batch"] = "vector_store.files_batch" + super().__init__(*args, auth_type=AuthenticationType.ENTRA_ID, **kwargs) -class VectorStoreFileCount(_model_base.Model): - """Counts of files processed or being processed by this vector store grouped by status. +class InternalConnectionPropertiesApiKeyAuth(InternalConnectionProperties, discriminator="ApiKey"): + """Connection properties for connections with API key authentication. - :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 + :ivar category: Category of the connection. Required. Known values are: "AzureOpenAI", + "Serverless", "AzureBlob", "AIServices", "CognitiveSearch", and "ApiKey". + :vartype category: str or ~azure.ai.projects.models.ConnectionType + :ivar target: The connection URL to be used for this service. Required. + :vartype target: str + :ivar auth_type: Authentication type of the connection target. Required. API Key authentication + :vartype auth_type: str or ~azure.ai.projects.models.API_KEY + :ivar credentials: Credentials will only be present for authType=ApiKey. Required. + :vartype credentials: ~azure.ai.projects.models._models.CredentialsApiKeyAuth """ - 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.""" + auth_type: Literal[AuthenticationType.API_KEY] = rest_discriminator(name="authType", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Authentication type of the connection target. Required. API Key authentication""" + credentials: "_models._models.CredentialsApiKeyAuth" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Credentials will only be present for authType=ApiKey. Required.""" @overload def __init__( self, *, - in_progress: int, - completed: int, - failed: int, - cancelled: int, - total: int, + category: Union[str, "_models.ConnectionType"], + target: str, + credentials: "_models._models.CredentialsApiKeyAuth", ) -> None: ... @overload @@ -6933,38 +746,31 @@ def __init__(self, mapping: Mapping[str, Any]) -> None: """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) - + super().__init__(*args, auth_type=AuthenticationType.API_KEY, **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. +class InternalConnectionPropertiesNoAuth(InternalConnectionProperties, discriminator="None"): + """Connection properties for connections with no authentication. - :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 + :ivar category: Category of the connection. Required. Known values are: "AzureOpenAI", + "Serverless", "AzureBlob", "AIServices", "CognitiveSearch", and "ApiKey". + :vartype category: str or ~azure.ai.projects.models.ConnectionType + :ivar target: The connection URL to be used for this service. Required. + :vartype target: str + :ivar auth_type: Authentication type of the connection target. Required. No authentication + :vartype auth_type: str or ~azure.ai.projects.models.NONE """ - 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\".""" + auth_type: Literal[AuthenticationType.NONE] = rest_discriminator(name="authType", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Authentication type of the connection target. Required. No authentication""" @overload def __init__( self, *, - id: str, # pylint: disable=redefined-builtin - deleted: bool, + category: Union[str, "_models.ConnectionType"], + target: str, ) -> None: ... @overload @@ -6975,33 +781,40 @@ def __init__(self, mapping: Mapping[str, Any]) -> None: """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) - self.object: Literal["vector_store.file.deleted"] = "vector_store.file.deleted" + super().__init__(*args, auth_type=AuthenticationType.NONE, **kwargs) -class VectorStoreFileError(_model_base.Model): - """Details on the error that may have occurred while processing a file for this vector store. +class InternalConnectionPropertiesSASAuth(InternalConnectionProperties, discriminator="SAS"): + """Connection properties for connections with SAS authentication. - :ivar code: One of ``server_error`` or ``rate_limit_exceeded``. Required. Known values are: - "server_error", "invalid_file", and "unsupported_file". - :vartype code: str or ~azure.ai.projects.models.VectorStoreFileErrorCode - :ivar message: A human-readable description of the error. Required. - :vartype message: str + :ivar category: Category of the connection. Required. Known values are: "AzureOpenAI", + "Serverless", "AzureBlob", "AIServices", "CognitiveSearch", and "ApiKey". + :vartype category: str or ~azure.ai.projects.models.ConnectionType + :ivar target: The connection URL to be used for this service. Required. + :vartype target: str + :ivar auth_type: Authentication type of the connection target. Required. Shared Access + Signature (SAS) authentication + :vartype auth_type: str or ~azure.ai.projects.models.SAS + :ivar credentials: Credentials will only be present for authType=ApiKey. Required. + :vartype credentials: ~azure.ai.projects.models._models.CredentialsSASAuth """ - code: Union[str, "_models.VectorStoreFileErrorCode"] = rest_field() - """One of ``server_error`` or ``rate_limit_exceeded``. Required. Known values are: - \"server_error\", \"invalid_file\", and \"unsupported_file\".""" - message: str = rest_field() - """A human-readable description of the error. Required.""" + auth_type: Literal[AuthenticationType.SAS] = rest_discriminator(name="authType", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Authentication type of the connection target. Required. Shared Access Signature (SAS) + authentication""" + credentials: "_models._models.CredentialsSASAuth" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Credentials will only be present for authType=ApiKey. Required.""" @overload def __init__( self, *, - code: Union[str, "_models.VectorStoreFileErrorCode"], - message: str, + category: Union[str, "_models.ConnectionType"], + target: str, + credentials: "_models._models.CredentialsSASAuth", ) -> None: ... @overload @@ -7012,35 +825,27 @@ def __init__(self, mapping: Mapping[str, Any]) -> None: """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) + super().__init__(*args, auth_type=AuthenticationType.SAS, **kwargs) -class VectorStoreStaticChunkingStrategyOptions(_model_base.Model): - """Options to configure a vector store static chunking strategy. +class ListConnectionsResponse(_model_base.Model): + """Response from the list operation. - :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 + :ivar value: A list of connection list secrets. Required. + :vartype value: list[~azure.ai.projects.models._models.GetConnectionResponse] """ - 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.""" + value: List["_models._models.GetConnectionResponse"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """A list of connection list secrets. Required.""" @overload def __init__( self, *, - max_chunk_size_tokens: int, - chunk_overlap_tokens: int, + value: List["_models._models.GetConnectionResponse"], ) -> None: ... @overload @@ -7054,27 +859,41 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) -class VectorStoreStaticChunkingStrategyRequest(VectorStoreChunkingStrategyRequest, discriminator="static"): - """A statically configured chunking strategy. +class RecurrenceSchedule(_model_base.Model): + """RecurrenceSchedule Definition. - 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.projects.models.STATIC - :ivar static: The options for the static chunking strategy. Required. - :vartype static: ~azure.ai.projects.models.VectorStoreStaticChunkingStrategyOptions + :ivar hours: List of hours for the schedule. Required. + :vartype hours: list[int] + :ivar minutes: List of minutes for the schedule. Required. + :vartype minutes: list[int] + :ivar week_days: List of days for the schedule. + :vartype week_days: list[str or ~azure.ai.projects.models.WeekDays] + :ivar month_days: List of month days for the schedule. + :vartype month_days: list[int] """ - 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.""" + hours: List[int] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """List of hours for the schedule. Required.""" + minutes: List[int] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """List of minutes for the schedule. Required.""" + week_days: Optional[List[Union[str, "_models.WeekDays"]]] = rest_field( + name="weekDays", visibility=["read", "create", "update", "delete", "query"] + ) + """List of days for the schedule.""" + month_days: Optional[List[int]] = rest_field( + name="monthDays", visibility=["read", "create", "update", "delete", "query"] + ) + """List of month days for the schedule.""" @overload def __init__( self, *, - static: "_models.VectorStoreStaticChunkingStrategyOptions", + hours: List[int], + minutes: List[int], + week_days: Optional[List[Union[str, "_models.WeekDays"]]] = None, + month_days: Optional[List[int]] = None, ) -> None: ... @overload @@ -7085,31 +904,45 @@ def __init__(self, mapping: Mapping[str, Any]) -> None: """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, type=VectorStoreChunkingStrategyRequestType.STATIC, **kwargs) + super().__init__(*args, **kwargs) -class VectorStoreStaticChunkingStrategyResponse( - VectorStoreChunkingStrategyResponse, discriminator="static" -): # pylint: disable=name-too-long - """A statically configured chunking strategy. +class RecurrenceTrigger(Trigger, discriminator="Recurrence"): + """Recurrence Trigger Definition. + + Readonly variables are only populated by the server, and will be ignored when sending a request. - :ivar type: The object type, which is always 'static'. Required. - :vartype type: str or ~azure.ai.projects.models.STATIC - :ivar static: The options for the static chunking strategy. Required. - :vartype static: ~azure.ai.projects.models.VectorStoreStaticChunkingStrategyOptions + :ivar type: Required. Default value is "Recurrence". + :vartype type: str + :ivar frequency: The frequency to trigger schedule. Required. Known values are: "Month", + "Week", "Day", "Hour", and "Minute". + :vartype frequency: str or ~azure.ai.projects.models.Frequency + :ivar interval: Specifies schedule interval in conjunction with frequency. Required. + :vartype interval: int + :ivar schedule: The recurrence schedule. + :vartype schedule: ~azure.ai.projects.models.RecurrenceSchedule """ - 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.""" + type: Literal["Recurrence"] = rest_discriminator(name="type", visibility=["read"]) # type: ignore + """Required. Default value is \"Recurrence\".""" + frequency: Union[str, "_models.Frequency"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The frequency to trigger schedule. Required. Known values are: \"Month\", \"Week\", \"Day\", + \"Hour\", and \"Minute\".""" + interval: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Specifies schedule interval in conjunction with frequency. Required.""" + schedule: Optional["_models.RecurrenceSchedule"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The recurrence schedule.""" @overload def __init__( self, *, - static: "_models.VectorStoreStaticChunkingStrategyOptions", + frequency: Union[str, "_models.Frequency"], + interval: int, + schedule: Optional["_models.RecurrenceSchedule"] = None, ) -> None: ... @overload @@ -7120,7 +953,34 @@ def __init__(self, mapping: Mapping[str, Any]) -> None: """ def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, type=VectorStoreChunkingStrategyResponseType.STATIC, **kwargs) + super().__init__(*args, type="Recurrence", **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 WorkspaceProperties(_model_base.Model): @@ -7131,7 +991,9 @@ class WorkspaceProperties(_model_base.Model): :vartype application_insights: str """ - application_insights: str = rest_field(name="applicationInsights") + application_insights: str = rest_field( + name="applicationInsights", visibility=["read", "create", "update", "delete", "query"] + ) """Authentication type of the connection target. Required.""" @overload diff --git a/sdk/ai/azure-ai-projects/azure/ai/projects/models/_patch.py b/sdk/ai/azure-ai-projects/azure/ai/projects/models/_patch.py index d6875e448d65..f7dd32510333 100644 --- a/sdk/ai/azure-ai-projects/azure/ai/projects/models/_patch.py +++ b/sdk/ai/azure-ai-projects/azure/ai/projects/models/_patch.py @@ -1,4 +1,3 @@ -# pylint: disable=too-many-lines # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. @@ -7,1807 +6,9 @@ Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize """ -import asyncio -import base64 -import datetime -import inspect -import itertools -import json -import logging -import math -import re -from abc import ABC, abstractmethod -from typing import ( - Any, - AsyncIterator, - Awaitable, - Callable, - Dict, - Generic, - Iterator, - List, - Mapping, - Optional, - Set, - Tuple, - Type, - TypeVar, - Union, - cast, - get_args, - get_origin, - overload, -) +from typing import List -from azure.core.credentials import AccessToken, TokenCredential -from azure.core.credentials_async import AsyncTokenCredential - -from ._enums import AgentStreamEvent, ConnectionType, MessageRole -from ._models import ( - AzureAISearchResource, - AzureAISearchToolDefinition, - AzureFunctionDefinition, - AzureFunctionStorageQueue, - AzureFunctionToolDefinition, - AzureFunctionBinding, - BingGroundingToolDefinition, - CodeInterpreterToolDefinition, - CodeInterpreterToolResource, - FileSearchToolDefinition, - FileSearchToolResource, - FunctionDefinition, - FunctionToolDefinition, - GetConnectionResponse, - IndexResource, - MessageImageFileContent, - MessageTextContent, - MessageTextFileCitationAnnotation, - MessageTextFilePathAnnotation, - MicrosoftFabricToolDefinition, - OpenApiAuthDetails, - OpenApiToolDefinition, - OpenApiFunctionDefinition, - RequiredFunctionToolCall, - RunStep, - RunStepDeltaChunk, - SharepointToolDefinition, - SubmitToolOutputsAction, - ThreadRun, - ToolConnection, - ToolConnectionList, - ToolDefinition, - ToolResources, - MessageDeltaTextContent, - VectorStoreDataSource, -) - -from ._models import MessageDeltaChunk as MessageDeltaChunkGenerated -from ._models import ThreadMessage as ThreadMessageGenerated -from ._models import OpenAIPageableListOfThreadMessage as OpenAIPageableListOfThreadMessageGenerated -from ._models import MessageAttachment as MessageAttachmentGenerated - -from .. import _types - - -logger = logging.getLogger(__name__) - -StreamEventData = Union["MessageDeltaChunk", "ThreadMessage", ThreadRun, RunStep, str] - - -def _filter_parameters(model_class: Type, parameters: Dict[str, Any]) -> Dict[str, Any]: - """ - Remove the parameters, non present in class public fields; return shallow copy of a dictionary. - - **Note:** Classes inherited from the model check that the parameters are present - in the list of attributes and if they are not, the error is being raised. This check may not - be relevant for classes, not inherited from azure.ai.projects._model_base.Model. - :param Type model_class: The class of model to be used. - :param parameters: The parsed dictionary with parameters. - :type parameters: Union[str, Dict[str, Any]] - :return: The dictionary with all invalid parameters removed. - :rtype: Dict[str, Any] - """ - new_params = {} - valid_parameters = set( - filter( - lambda x: not x.startswith("_") and hasattr(model_class.__dict__[x], "_type"), model_class.__dict__.keys() - ) - ) - for k in filter(lambda x: x in valid_parameters, parameters.keys()): - new_params[k] = parameters[k] - return new_params - - -def _safe_instantiate( - model_class: Type, parameters: Union[str, Dict[str, Any]], *, generated_class: Optional[Type] = None -) -> StreamEventData: - """ - Instantiate class with the set of parameters from the server. - - :param Type model_class: The class of model to be used. - :param parameters: The parsed dictionary with parameters. - :type parameters: Union[str, Dict[str, Any]] - :keyword Optional[Type] generated_class: The optional generated type. - :return: The class of model_class type if parameters is a dictionary, or the parameters themselves otherwise. - :rtype: Any - """ - if not generated_class: - generated_class = model_class - if not isinstance(parameters, dict): - return parameters - return cast(StreamEventData, model_class(**_filter_parameters(generated_class, parameters))) - - -def _parse_event(event_data_str: str) -> Tuple[str, StreamEventData]: - event_lines = event_data_str.strip().split("\n") - event_type: Optional[str] = None - event_data = "" - event_obj: StreamEventData - for line in event_lines: - if line.startswith("event:"): - event_type = line.split(":", 1)[1].strip() - elif line.startswith("data:"): - event_data = line.split(":", 1)[1].strip() - - if not event_type: - raise ValueError("Event type not specified in the event data.") - - try: - parsed_data: Union[str, Dict[str, StreamEventData]] = cast(Dict[str, StreamEventData], json.loads(event_data)) - except json.JSONDecodeError: - parsed_data = event_data - - # Workaround for service bug: Rename 'expires_at' to 'expired_at' - if event_type.startswith("thread.run.step") and isinstance(parsed_data, dict) and "expires_at" in parsed_data: - parsed_data["expired_at"] = parsed_data.pop("expires_at") - - # Map to the appropriate class instance - if event_type in { - AgentStreamEvent.THREAD_RUN_CREATED.value, - AgentStreamEvent.THREAD_RUN_QUEUED.value, - AgentStreamEvent.THREAD_RUN_INCOMPLETE.value, - AgentStreamEvent.THREAD_RUN_IN_PROGRESS.value, - AgentStreamEvent.THREAD_RUN_REQUIRES_ACTION.value, - AgentStreamEvent.THREAD_RUN_COMPLETED.value, - AgentStreamEvent.THREAD_RUN_FAILED.value, - AgentStreamEvent.THREAD_RUN_CANCELLING.value, - AgentStreamEvent.THREAD_RUN_CANCELLED.value, - AgentStreamEvent.THREAD_RUN_EXPIRED.value, - }: - event_obj = _safe_instantiate(ThreadRun, parsed_data) - elif event_type in { - AgentStreamEvent.THREAD_RUN_STEP_CREATED.value, - AgentStreamEvent.THREAD_RUN_STEP_IN_PROGRESS.value, - AgentStreamEvent.THREAD_RUN_STEP_COMPLETED.value, - AgentStreamEvent.THREAD_RUN_STEP_FAILED.value, - AgentStreamEvent.THREAD_RUN_STEP_CANCELLED.value, - AgentStreamEvent.THREAD_RUN_STEP_EXPIRED.value, - }: - event_obj = _safe_instantiate(RunStep, parsed_data) - elif event_type in { - AgentStreamEvent.THREAD_MESSAGE_CREATED.value, - AgentStreamEvent.THREAD_MESSAGE_IN_PROGRESS.value, - AgentStreamEvent.THREAD_MESSAGE_COMPLETED.value, - AgentStreamEvent.THREAD_MESSAGE_INCOMPLETE.value, - }: - event_obj = _safe_instantiate(ThreadMessage, parsed_data, generated_class=ThreadMessageGenerated) - elif event_type == AgentStreamEvent.THREAD_MESSAGE_DELTA.value: - event_obj = _safe_instantiate(MessageDeltaChunk, parsed_data, generated_class=MessageDeltaChunkGenerated) - - elif event_type == AgentStreamEvent.THREAD_RUN_STEP_DELTA.value: - event_obj = _safe_instantiate(RunStepDeltaChunk, parsed_data) - else: - event_obj = str(parsed_data) - - return event_type, event_obj - - -class ConnectionProperties: - """The properties of a single connection. - - :ivar id: A unique identifier for the connection. - :vartype id: str - :ivar name: The friendly name of the connection. - :vartype name: str - :ivar authentication_type: The authentication type used by the connection. - :vartype authentication_type: ~azure.ai.projects.models._models.AuthenticationType - :ivar connection_type: The connection type . - :vartype connection_type: ~azure.ai.projects.models._models.ConnectionType - :ivar endpoint_url: The endpoint URL associated with this connection - :vartype endpoint_url: str - :ivar key: The api-key to be used when accessing the connection. - :vartype key: str - :ivar token_credential: The TokenCredential to be used when accessing the connection. - :vartype token_credential: ~azure.core.credentials.TokenCredential - """ - - def __init__( - self, - *, - connection: GetConnectionResponse, - token_credential: Union[TokenCredential, AsyncTokenCredential, None] = None, - ) -> None: - self.id = connection.id - self.name = connection.name - self.authentication_type = connection.properties.auth_type - self.connection_type = cast(ConnectionType, connection.properties.category) - self.endpoint_url = ( - connection.properties.target[:-1] - if connection.properties.target.endswith("/") - else connection.properties.target - ) - self.key: Optional[str] = None - if hasattr(connection.properties, "credentials"): - if hasattr(connection.properties.credentials, "key"): # type: ignore - self.key = connection.properties.credentials.key # type: ignore - self.token_credential = token_credential - - def to_evaluator_model_config( - self, deployment_name: str, api_version: str, *, include_credentials: bool = False - ) -> Dict[str, str]: - """Get model configuration to be used with evaluators, from connection. - - :param deployment_name: Deployment name to build model configuration. - :type deployment_name: str - :param api_version: API version used by model deployment. - :type api_version: str - :keyword include_credentials: Include credentials in the model configuration. If set to True, the model - configuration will have the key field set to the actual key value. - If set to False, the model configuration will have the key field set to the connection id. - To get the secret, connection.get method should be called with include_credentials set to True. - :paramtype include_credentials: bool - - :returns: Model configuration dictionary. - :rtype: Dict[str, str] - """ - connection_type = self.connection_type.value - if self.connection_type.value == ConnectionType.AZURE_OPEN_AI: - connection_type = "azure_openai" - - if self.authentication_type == "ApiKey": - model_config = { - "azure_deployment": deployment_name, - "azure_endpoint": self.endpoint_url, - "type": connection_type, - "api_version": api_version, - "api_key": self.key if include_credentials and self.key else f"{self.id}/credentials/key", - } - else: - model_config = { - "azure_deployment": deployment_name, - "azure_endpoint": self.endpoint_url, - "type": self.connection_type, - "api_version": api_version, - } - return model_config - - def __str__(self): - out = "{\n" - out += f' "name": "{self.name}",\n' - out += f' "id": "{self.id}",\n' - out += f' "authentication_type": "{self.authentication_type}",\n' - out += f' "connection_type": "{self.connection_type}",\n' - out += f' "endpoint_url": "{self.endpoint_url}",\n' - if self.key: - out += ' "key": "REDACTED"\n' - else: - out += ' "key": null\n' - if self.token_credential: - out += ' "token_credential": "REDACTED"\n' - else: - out += ' "token_credential": null\n' - out += "}\n" - return out - - -# TODO: Look into adding an async version of this class -class SASTokenCredential(TokenCredential): - def __init__( - self, - *, - sas_token: str, - credential: TokenCredential, - subscription_id: str, - resource_group_name: str, - project_name: str, - connection_name: str, - ): - self._sas_token = sas_token - self._credential = credential - self._subscription_id = subscription_id - self._resource_group_name = resource_group_name - self._project_name = project_name - self._connection_name = connection_name - self._expires_on = SASTokenCredential._get_expiration_date_from_token(self._sas_token) - logger.debug("[SASTokenCredential.__init__] Exit. Given token expires on %s.", self._expires_on) - - @classmethod - def _get_expiration_date_from_token(cls, jwt_token: str) -> datetime.datetime: - payload = jwt_token.split(".")[1] - padded_payload = payload + "=" * (4 - len(payload) % 4) # Add padding if necessary - decoded_bytes = base64.urlsafe_b64decode(padded_payload) - decoded_str = decoded_bytes.decode("utf-8") - decoded_payload = json.loads(decoded_str) - expiration_date = decoded_payload.get("exp") - return datetime.datetime.fromtimestamp(expiration_date, datetime.timezone.utc) - - def _refresh_token(self) -> None: - logger.debug("[SASTokenCredential._refresh_token] Enter") - from azure.ai.projects import AIProjectClient - - project_client = AIProjectClient( - credential=self._credential, - # Since we are only going to use the "connections" operations, we don't need to supply an endpoint. - # http://management.azure.com is hard coded in the SDK. - endpoint="not-needed", - subscription_id=self._subscription_id, - resource_group_name=self._resource_group_name, - project_name=self._project_name, - ) - - connection = project_client.connections.get(connection_name=self._connection_name, include_credentials=True) - - self._sas_token = "" - if connection is not None and connection.token_credential is not None: - sas_credential = cast(SASTokenCredential, connection.token_credential) - self._sas_token = sas_credential._sas_token # pylint: disable=protected-access - self._expires_on = SASTokenCredential._get_expiration_date_from_token(self._sas_token) - logger.debug("[SASTokenCredential._refresh_token] Exit. New token expires on %s.", self._expires_on) - - def get_token( - self, - *scopes: str, - claims: Optional[str] = None, - tenant_id: Optional[str] = None, - enable_cae: bool = False, - **kwargs: Any, - ) -> AccessToken: - """Request an access token for `scopes`. - - :param str scopes: The type of access needed. - - :keyword str claims: Additional claims required in the token, such as those returned in a resource - provider's claims challenge following an authorization failure. - :keyword str tenant_id: Optional tenant to include in the token request. - :keyword bool enable_cae: Indicates whether to enable Continuous Access Evaluation (CAE) for the requested - token. Defaults to False. - - :rtype: AccessToken - :return: An AccessToken instance containing the token string and its expiration time in Unix time. - """ - logger.debug("SASTokenCredential.get_token] Enter") - if self._expires_on < datetime.datetime.now(datetime.timezone.utc): - self._refresh_token() - return AccessToken(self._sas_token, math.floor(self._expires_on.timestamp())) - - -# Define type_map to translate Python type annotations to JSON Schema types -type_map = { - "str": "string", - "int": "integer", - "float": "number", - "bool": "boolean", - "NoneType": "null", - "list": "array", - "dict": "object", -} - - -def _map_type(annotation) -> Dict[str, Any]: # pylint: disable=too-many-return-statements - if annotation == inspect.Parameter.empty: - return {"type": "string"} # Default type if annotation is missing - - origin = get_origin(annotation) - - if origin in {list, List}: - args = get_args(annotation) - item_type = args[0] if args else str - return {"type": "array", "items": _map_type(item_type)} - if origin in {dict, Dict}: - return {"type": "object"} - if origin is Union: - args = get_args(annotation) - # If Union contains None, it is an optional parameter - if type(None) in args: - # If Union contains only one non-None type, it is a nullable parameter - non_none_args = [arg for arg in args if arg is not type(None)] - if len(non_none_args) == 1: - schema = _map_type(non_none_args[0]) - if "type" in schema: - if isinstance(schema["type"], str): - schema["type"] = [schema["type"], "null"] - elif "null" not in schema["type"]: - schema["type"].append("null") - else: - schema["type"] = ["null"] - return schema - # If Union contains multiple types, it is a oneOf parameter - return {"oneOf": [_map_type(arg) for arg in args]} - if isinstance(annotation, type): - schema_type = type_map.get(annotation.__name__, "string") - return {"type": schema_type} - - return {"type": "string"} # Fallback to "string" if type is unrecognized - - -def is_optional(annotation) -> bool: - origin = get_origin(annotation) - if origin is Union: - args = get_args(annotation) - return type(None) in args - return False - - -class MessageDeltaChunk(MessageDeltaChunkGenerated): - @property - def text(self) -> str: - """Get the text content of the delta chunk. - - :rtype: str - """ - if not self.delta or not self.delta.content: - return "" - return "".join( - content_part.text.value or "" - for content_part in self.delta.content - if isinstance(content_part, MessageDeltaTextContent) and content_part.text - ) - - -class ThreadMessage(ThreadMessageGenerated): - @property - def text_messages(self) -> List[MessageTextContent]: - """Returns all text message contents in the messages. - - :rtype: List[MessageTextContent] - """ - if not self.content: - return [] - return [content for content in self.content if isinstance(content, MessageTextContent)] - - @property - def image_contents(self) -> List[MessageImageFileContent]: - """Returns all image file contents from image message contents in the messages. - - :rtype: List[MessageImageFileContent] - """ - if not self.content: - return [] - return [content for content in self.content if isinstance(content, MessageImageFileContent)] - - @property - def file_citation_annotations(self) -> List[MessageTextFileCitationAnnotation]: - """Returns all file citation annotations from text message annotations in the messages. - - :rtype: List[MessageTextFileCitationAnnotation] - """ - if not self.content: - return [] - - return [ - annotation - for content in self.content - if isinstance(content, MessageTextContent) - for annotation in content.text.annotations - if isinstance(annotation, MessageTextFileCitationAnnotation) - ] - - @property - def file_path_annotations(self) -> List[MessageTextFilePathAnnotation]: - """Returns all file path annotations from text message annotations in the messages. - - :rtype: List[MessageTextFilePathAnnotation] - """ - if not self.content: - return [] - return [ - annotation - for content in self.content - if isinstance(content, MessageTextContent) - for annotation in content.text.annotations - if isinstance(annotation, MessageTextFilePathAnnotation) - ] - - -class MessageAttachment(MessageAttachmentGenerated): - @overload - def __init__( - self, - *, - tools: List["FileSearchToolDefinition"], - file_id: Optional[str] = None, - data_source: Optional["VectorStoreDataSource"] = None, - ) -> None: ... - @overload - def __init__( - self, - *, - tools: List["CodeInterpreterToolDefinition"], - file_id: Optional[str] = None, - data_source: Optional["VectorStoreDataSource"] = None, - ) -> None: ... - @overload - def __init__( - self, - *, - tools: List["_types.MessageAttachmentToolDefinition"], - file_id: Optional[str] = None, - data_source: Optional["VectorStoreDataSource"] = 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) - - -ToolDefinitionT = TypeVar("ToolDefinitionT", bound=ToolDefinition) -ToolT = TypeVar("ToolT", bound="Tool") - - -class Tool(ABC, Generic[ToolDefinitionT]): - """ - An abstract class representing a tool that can be used by an agent. - """ - - @property - @abstractmethod - def definitions(self) -> List[ToolDefinitionT]: - """Get the tool definitions.""" - - @property - @abstractmethod - def resources(self) -> ToolResources: - """Get the tool resources.""" - - @abstractmethod - def execute(self, tool_call: Any) -> Any: - """ - Execute the tool with the provided tool call. - - :param Any tool_call: The tool call to execute. - :return: The output of the tool operations. - """ - - -class BaseFunctionTool(Tool[FunctionToolDefinition]): - """ - A tool that executes user-defined functions. - """ - - def __init__(self, functions: Set[Callable[..., Any]]): - """ - Initialize FunctionTool with a set of functions. - - :param functions: A set of function objects. - """ - self._functions = self._create_function_dict(functions) - self._definitions = self._build_function_definitions(self._functions) - - def add_functions(self, extra_functions: Set[Callable[..., Any]]) -> None: - """ - Add more functions into this FunctionTool’s existing function set. - If a function with the same name already exists, it is overwritten. - - :param extra_functions: A set of additional functions to be added to - the existing function set. Functions are defined as callables and - may have any number of arguments and return types. - :type extra_functions: Set[Callable[..., Any]] - """ - # Convert the existing dictionary of { name: function } back into a set - existing_functions = set(self._functions.values()) - # Merge old + new - combined = existing_functions.union(extra_functions) - # Rebuild state - self._functions = self._create_function_dict(combined) - self._definitions = self._build_function_definitions(self._functions) - - def _create_function_dict(self, functions: Set[Callable[..., Any]]) -> Dict[str, Callable[..., Any]]: - return {func.__name__: func for func in functions} - - def _build_function_definitions(self, functions: Dict[str, Any]) -> List[FunctionToolDefinition]: - specs: List[FunctionToolDefinition] = [] - # Flexible regex to capture ':param : ' - param_pattern = re.compile( - r""" - ^\s* # Optional leading whitespace - :param # Literal ':param' - \s+ # At least one whitespace character - (?P[^:\s\(\)]+) # Parameter name (no spaces, colons, or parentheses) - (?:\s*\(\s*(?P[^)]+?)\s*\))? # Optional type in parentheses, allowing internal spaces - \s*:\s* # Colon ':' surrounded by optional whitespace - (?P.+) # Description (rest of the line) - """, - re.VERBOSE, - ) - - for name, func in functions.items(): - sig = inspect.signature(func) - params = sig.parameters - docstring = inspect.getdoc(func) or "" - description = docstring.split("\n", maxsplit=1)[0] if docstring else "No description" - - param_descriptions = {} - for line in docstring.splitlines(): - line = line.strip() - match = param_pattern.match(line) - if match: - groups = match.groupdict() - param_name = groups.get("name") - param_desc = groups.get("description") - param_desc = param_desc.strip() if param_desc else "No description" - param_descriptions[param_name] = param_desc.strip() - - properties = {} - required = [] - for param_name, param in params.items(): - param_type_info = _map_type(param.annotation) - param_description = param_descriptions.get(param_name, "No description") - - properties[param_name] = {**param_type_info, "description": param_description} - - # If the parameter has no default value and is not optional, add it to the required list - if param.default is inspect.Parameter.empty and not is_optional(param.annotation): - required.append(param_name) - - function_def = FunctionDefinition( - name=name, - description=description, - parameters={"type": "object", "properties": properties, "required": required}, - ) - tool_def = FunctionToolDefinition(function=function_def) - specs.append(tool_def) - - return specs - - def _get_func_and_args(self, tool_call: RequiredFunctionToolCall) -> Tuple[Any, Dict[str, Any]]: - function_name = tool_call.function.name - arguments = tool_call.function.arguments - - if function_name not in self._functions: - logging.error("Function '%s' not found.", function_name) - raise ValueError(f"Function '{function_name}' not found.") - - function = self._functions[function_name] - - try: - parsed_arguments = json.loads(arguments) - except json.JSONDecodeError as e: - logging.error("Invalid JSON arguments for function '%s': %s", function_name, e) - raise ValueError(f"Invalid JSON arguments: {e}") from e - - if not isinstance(parsed_arguments, dict): - logging.error("Arguments must be a JSON object for function '%s'.", function_name) - raise TypeError("Arguments must be a JSON object.") - - return function, parsed_arguments - - @property - def definitions(self) -> List[FunctionToolDefinition]: - """ - Get the function definitions. - - :return: A list of function definitions. - :rtype: List[ToolDefinition] - """ - return self._definitions - - @property - def resources(self) -> ToolResources: - """ - Get the tool resources for the agent. - - :return: An empty ToolResources as FunctionTool doesn't have specific resources. - :rtype: ToolResources - """ - return ToolResources() - - -class FunctionTool(BaseFunctionTool): - - def execute(self, tool_call: RequiredFunctionToolCall) -> Any: - function, parsed_arguments = self._get_func_and_args(tool_call) - - try: - return function(**parsed_arguments) if parsed_arguments else function() - except TypeError as e: - error_message = f"Error executing function '{tool_call.function.name}': {e}" - logging.error(error_message) - # Return error message as JSON string back to agent in order to make possible self - # correction to the function call - return json.dumps({"error": error_message}) - - -class AsyncFunctionTool(BaseFunctionTool): - - async def execute(self, tool_call: RequiredFunctionToolCall) -> Any: # pylint: disable=invalid-overridden-method - function, parsed_arguments = self._get_func_and_args(tool_call) - - try: - if inspect.iscoroutinefunction(function): - return await function(**parsed_arguments) if parsed_arguments else await function() - return function(**parsed_arguments) if parsed_arguments else function() - except TypeError as e: - error_message = f"Error executing function '{tool_call.function.name}': {e}" - logging.error(error_message) - # Return error message as JSON string back to agent in order to make possible self correction - # to the function call - return json.dumps({"error": error_message}) - - -class AzureAISearchTool(Tool[AzureAISearchToolDefinition]): - """ - A tool that searches for information using Azure AI Search. - """ - - def __init__(self, index_connection_id: str, index_name: str): - self.index_list = [IndexResource(index_connection_id=index_connection_id, index_name=index_name)] - - @property - def definitions(self) -> List[AzureAISearchToolDefinition]: - """ - Get the Azure AI search tool definitions. - - :return: A list of tool definitions. - :rtype: List[ToolDefinition] - """ - return [AzureAISearchToolDefinition()] - - @property - def resources(self) -> ToolResources: - """ - Get the Azure AI search resources. - - :return: ToolResources populated with azure_ai_search associated resources. - :rtype: ToolResources - """ - return ToolResources(azure_ai_search=AzureAISearchResource(index_list=self.index_list)) - - def execute(self, tool_call: Any): - """ - AI Search tool does not execute client-side. - - :param Any tool_call: The tool call to execute. - """ - - -class OpenApiTool(Tool[OpenApiToolDefinition]): - """ - A tool that retrieves information using OpenAPI specs. - Initialized with an initial API definition (name, description, spec, auth), - this class also supports adding and removing additional API definitions dynamically. - """ - - def __init__(self, name: str, description: str, spec: Any, auth: OpenApiAuthDetails): - """ - Constructor initializes the tool with a primary API definition. - - :param name: The name of the API. - :param description: The API description. - :param spec: The API specification. - :param auth: Authentication details for the API. - :type auth: OpenApiAuthDetails - """ - self._default_auth = auth - self._definitions: List[OpenApiToolDefinition] = [ - OpenApiToolDefinition( - openapi=OpenApiFunctionDefinition(name=name, description=description, spec=spec, auth=auth) - ) - ] - - @property - def definitions(self) -> List[OpenApiToolDefinition]: - """ - Get the list of all API definitions for the tool. - - :return: A list of OpenAPI tool definitions. - :rtype: List[ToolDefinition] - """ - return self._definitions - - def add_definition(self, name: str, description: str, spec: Any, auth: Optional[OpenApiAuthDetails] = None) -> None: - """ - Adds a new API definition dynamically. - Raises a ValueError if a definition with the same name already exists. - - :param name: The name of the API. - :type name: str - :param description: The description of the API. - :type description: str - :param spec: The API specification. - :type spec: Any - :param auth: Optional authentication details for this particular API definition. - If not provided, the tool's default authentication details will be used. - :type auth: Optional[OpenApiAuthDetails] - :raises ValueError: If a definition with the same name exists. - """ - # Check if a definition with the same name exists. - if any(definition.openapi.name == name for definition in self._definitions): - raise ValueError(f"Definition '{name}' already exists and cannot be added again.") - - # Use provided auth if specified, otherwise use default - auth_to_use = auth if auth is not None else self._default_auth - - new_definition = OpenApiToolDefinition( - openapi=OpenApiFunctionDefinition(name=name, description=description, spec=spec, auth=auth_to_use) - ) - self._definitions.append(new_definition) - - def remove_definition(self, name: str) -> None: - """ - Removes an API definition based on its name. - - :param name: The name of the API definition to remove. - :type name: str - :raises ValueError: If the definition with the specified name does not exist. - """ - for definition in self._definitions: - if definition.openapi.name == name: - self._definitions.remove(definition) - logging.info("Definition '%s' removed. Total definitions: %d.", name, len(self._definitions)) - return - raise ValueError(f"Definition with the name '{name}' does not exist.") - - @property - def resources(self) -> ToolResources: - """ - Get the tool resources for the agent. - - :return: An empty ToolResources as OpenApiTool doesn't have specific resources. - :rtype: ToolResources - """ - return ToolResources() - - def execute(self, tool_call: Any) -> None: - """ - OpenApiTool does not execute client-side. - - :param Any tool_call: The tool call to execute. - :type tool_call: Any - """ - - -class AzureFunctionTool(Tool[AzureFunctionToolDefinition]): - """ - A tool that is used to inform agent about available the Azure function. - - :param name: The azure function name. - :param description: The azure function description. - :param parameters: The description of function parameters. - :param input_queue: Input queue used, by azure function. - :param output_queue: Output queue used, by azure function. - """ - - def __init__( - self, - name: str, - description: str, - parameters: Dict[str, Any], - input_queue: AzureFunctionStorageQueue, - output_queue: AzureFunctionStorageQueue, - ) -> None: - self._definitions = [ - AzureFunctionToolDefinition( - azure_function=AzureFunctionDefinition( - function=FunctionDefinition( - name=name, - description=description, - parameters=parameters, - ), - input_binding=AzureFunctionBinding(storage_queue=input_queue), - output_binding=AzureFunctionBinding(storage_queue=output_queue), - ) - ) - ] - - @property - def definitions(self) -> List[AzureFunctionToolDefinition]: - """ - Get the Azure AI search tool definitions. - - :rtype: List[ToolDefinition] - """ - return self._definitions - - @property - def resources(self) -> ToolResources: - """ - Get the Azure AI search resources. - - :rtype: ToolResources - """ - return ToolResources() - - def execute(self, tool_call: Any) -> Any: - pass - - -class ConnectionTool(Tool[ToolDefinitionT]): - """ - A tool that requires connection ids. - Used as base class for Bing Grounding, Sharepoint, and Microsoft Fabric - """ - - def __init__(self, connection_id: str): - """ - Initialize ConnectionTool with a connection_id. - - :param connection_id: Connection ID used by tool. All connection tools allow only one connection. - """ - self.connection_ids = [ToolConnection(connection_id=connection_id)] - - @property - def resources(self) -> ToolResources: - """ - Get the connection tool resources. - - :rtype: ToolResources - """ - return ToolResources() - - def execute(self, tool_call: Any) -> Any: - pass - - -class BingGroundingTool(ConnectionTool[BingGroundingToolDefinition]): - """ - A tool that searches for information using Bing. - """ - - @property - def definitions(self) -> List[BingGroundingToolDefinition]: - """ - Get the Bing grounding tool definitions. - - :rtype: List[ToolDefinition] - """ - return [BingGroundingToolDefinition(bing_grounding=ToolConnectionList(connection_list=self.connection_ids))] - - -class FabricTool(ConnectionTool[MicrosoftFabricToolDefinition]): - """ - A tool that searches for information using Microsoft Fabric. - """ - - @property - def definitions(self) -> List[MicrosoftFabricToolDefinition]: - """ - Get the Microsoft Fabric tool definitions. - - :rtype: List[ToolDefinition] - """ - return [MicrosoftFabricToolDefinition(fabric_aiskill=ToolConnectionList(connection_list=self.connection_ids))] - - -class SharepointTool(ConnectionTool[SharepointToolDefinition]): - """ - A tool that searches for information using Sharepoint. - """ - - @property - def definitions(self) -> List[SharepointToolDefinition]: - """ - Get the Sharepoint tool definitions. - - :rtype: List[ToolDefinition] - """ - return [SharepointToolDefinition(sharepoint_grounding=ToolConnectionList(connection_list=self.connection_ids))] - - -class FileSearchTool(Tool[FileSearchToolDefinition]): - """ - A tool that searches for uploaded file information from the created vector stores. - - :param vector_store_ids: A list of vector store IDs to search for files. - :type vector_store_ids: list[str] - """ - - def __init__(self, vector_store_ids: Optional[List[str]] = None): - if vector_store_ids is None: - self.vector_store_ids = set() - else: - self.vector_store_ids = set(vector_store_ids) - - def add_vector_store(self, store_id: str) -> None: - """ - Add a vector store ID to the list of vector stores to search for files. - - :param store_id: The ID of the vector store to search for files. - :type store_id: str - - """ - self.vector_store_ids.add(store_id) - - def remove_vector_store(self, store_id: str) -> None: - """ - Remove a vector store ID from the list of vector stores to search for files. - - :param store_id: The ID of the vector store to remove. - :type store_id: str - - """ - self.vector_store_ids.remove(store_id) - - @property - def definitions(self) -> List[FileSearchToolDefinition]: - """ - Get the file search tool definitions. - - :rtype: List[ToolDefinition] - """ - return [FileSearchToolDefinition()] - - @property - def resources(self) -> ToolResources: - """ - Get the file search resources. - - :rtype: ToolResources - """ - return ToolResources(file_search=FileSearchToolResource(vector_store_ids=list(self.vector_store_ids))) - - def execute(self, tool_call: Any) -> Any: - pass - - -class CodeInterpreterTool(Tool[CodeInterpreterToolDefinition]): - """ - A tool that interprets code files uploaded to the agent. - - :param file_ids: A list of file IDs to interpret. - :type file_ids: list[str] - """ - - def __init__(self, file_ids: Optional[List[str]] = None): - if file_ids is None: - self.file_ids = set() - else: - self.file_ids = set(file_ids) - - def add_file(self, file_id: str) -> None: - """ - Add a file ID to the list of files to interpret. - - :param file_id: The ID of the file to interpret. - :type file_id: str - """ - self.file_ids.add(file_id) - - def remove_file(self, file_id: str) -> None: - """ - Remove a file ID from the list of files to interpret. - - :param file_id: The ID of the file to remove. - :type file_id: str - """ - self.file_ids.remove(file_id) - - @property - def definitions(self) -> List[CodeInterpreterToolDefinition]: - """ - Get the code interpreter tool definitions. - - :rtype: List[ToolDefinition] - """ - return [CodeInterpreterToolDefinition()] - - @property - def resources(self) -> ToolResources: - """ - Get the code interpreter resources. - - :rtype: ToolResources - """ - if not self.file_ids: - return ToolResources() - return ToolResources(code_interpreter=CodeInterpreterToolResource(file_ids=list(self.file_ids))) - - def execute(self, tool_call: Any) -> Any: - pass - - -class BaseToolSet: - """ - Abstract class for a collection of tools that can be used by an agent. - """ - - def __init__(self) -> None: - self._tools: List[Tool] = [] - - def validate_tool_type(self, tool: Tool) -> None: - pass - - def add(self, tool: Tool): - """ - Add a tool to the tool set. - - :param Tool tool: The tool to add. - :raises ValueError: If a tool of the same type already exists. - """ - self.validate_tool_type(tool) - - if any(isinstance(existing_tool, type(tool)) for existing_tool in self._tools): - raise ValueError("Tool of type {type(tool).__name__} already exists in the ToolSet.") - self._tools.append(tool) - - def remove(self, tool_type: Type[Tool]) -> None: - """ - Remove a tool of the specified type from the tool set. - - :param Type[Tool] tool_type: The type of tool to remove. - :raises ValueError: If a tool of the specified type is not found. - """ - for i, tool in enumerate(self._tools): - if isinstance(tool, tool_type): - del self._tools[i] - logging.info("Tool of type %s removed from the ToolSet.", tool_type.__name__) - return - raise ValueError(f"Tool of type {tool_type.__name__} not found in the ToolSet.") - - @property - def definitions(self) -> List[ToolDefinition]: - """ - Get the definitions for all tools in the tool set. - - :rtype: List[ToolDefinition] - """ - tools = [] - for tool in self._tools: - tools.extend(tool.definitions) - return tools - - @property - def resources(self) -> ToolResources: - """ - Get the resources for all tools in the tool set. - - :rtype: ToolResources - """ - tool_resources: Dict[str, Any] = {} - for tool in self._tools: - resources = tool.resources - for key, value in resources.items(): - if key in tool_resources: - if isinstance(tool_resources[key], dict) and isinstance(value, dict): - tool_resources[key].update(value) - else: - tool_resources[key] = value - return self._create_tool_resources_from_dict(tool_resources) - - def _create_tool_resources_from_dict(self, resources: Dict[str, Any]) -> ToolResources: - """ - Safely converts a dictionary into a ToolResources instance. - - :param resources: A dictionary of tool resources. Should be a mapping - accepted by ~azure.ai.projects.models.AzureAISearchResource - :type resources: Dict[str, Any] - :return: A ToolResources instance. - :rtype: ToolResources - """ - try: - return ToolResources(**resources) - except TypeError as e: - logging.error("Error creating ToolResources: %s", e) - raise ValueError("Invalid resources for ToolResources.") from e - - def get_definitions_and_resources(self) -> Dict[str, Any]: - """ - Get the definitions and resources for all tools in the tool set. - - :return: A dictionary containing the tool resources and definitions. - :rtype: Dict[str, Any] - """ - return { - "tool_resources": self.resources, - "tools": self.definitions, - } - - def get_tool(self, tool_type: Type[ToolT]) -> ToolT: - """ - Get a tool of the specified type from the tool set. - - :param Type[Tool] tool_type: The type of tool to get. - :return: The tool of the specified type. - :rtype: Tool - :raises ValueError: If a tool of the specified type is not found. - """ - for tool in self._tools: - if isinstance(tool, tool_type): - return cast(ToolT, tool) - raise ValueError(f"Tool of type {tool_type.__name__} not found in the ToolSet.") - - -class ToolSet(BaseToolSet): - """ - A collection of tools that can be used by an synchronize agent. - """ - - def validate_tool_type(self, tool: Tool) -> None: - """ - Validate the type of the tool. - - :param Tool tool: The type of the tool to validate. - :raises ValueError: If the tool type is not a subclass of Tool. - """ - if isinstance(tool, AsyncFunctionTool): - raise ValueError( - "AsyncFunctionTool is not supported in ToolSet. " - + "To use async functions, use AsyncToolSet and agents operations in azure.ai.projects.aio." - ) - - def execute_tool_calls(self, tool_calls: List[Any]) -> Any: - """ - Execute a tool of the specified type with the provided tool calls. - - :param List[Any] tool_calls: A list of tool calls to execute. - :return: The output of the tool operations. - :rtype: Any - """ - tool_outputs = [] - - for tool_call in tool_calls: - try: - if tool_call.type == "function": - tool = self.get_tool(FunctionTool) - output = tool.execute(tool_call) - tool_output = { - "tool_call_id": tool_call.id, - "output": output, - } - tool_outputs.append(tool_output) - except Exception as e: # pylint: disable=broad-exception-caught - logging.error("Failed to execute tool call %s: %s", tool_call, e) - - return tool_outputs - - -class AsyncToolSet(BaseToolSet): - """ - A collection of tools that can be used by an asynchronous agent. - """ - - def validate_tool_type(self, tool: Tool) -> None: - """ - Validate the type of the tool. - - :param Tool tool: The type of the tool to validate. - :raises ValueError: If the tool type is not a subclass of Tool. - """ - if isinstance(tool, FunctionTool): - raise ValueError( - "FunctionTool is not supported in AsyncToolSet. " - + "Please use AsyncFunctionTool instead and provide sync and/or async function(s)." - ) - - async def execute_tool_calls(self, tool_calls: List[Any]) -> Any: - """ - Execute a tool of the specified type with the provided tool calls. - - :param List[Any] tool_calls: A list of tool calls to execute. - :return: The output of the tool operations. - :rtype: Any - """ - tool_outputs = [] - - for tool_call in tool_calls: - try: - if tool_call.type == "function": - tool = self.get_tool(AsyncFunctionTool) - output = await tool.execute(tool_call) - tool_output = { - "tool_call_id": tool_call.id, - "output": output, - } - tool_outputs.append(tool_output) - except Exception as e: # pylint: disable=broad-exception-caught - logging.error("Failed to execute tool call %s: %s", tool_call, e) - - return tool_outputs - - -EventFunctionReturnT = TypeVar("EventFunctionReturnT") -T = TypeVar("T") -BaseAsyncAgentEventHandlerT = TypeVar("BaseAsyncAgentEventHandlerT", bound="BaseAsyncAgentEventHandler") -BaseAgentEventHandlerT = TypeVar("BaseAgentEventHandlerT", bound="BaseAgentEventHandler") - - -async def async_chain(*iterators: AsyncIterator[T]) -> AsyncIterator[T]: - for iterator in iterators: - async for item in iterator: - yield item - - -class BaseAsyncAgentEventHandler(AsyncIterator[T]): - - def __init__(self) -> None: - self.response_iterator: Optional[AsyncIterator[bytes]] = None - self.submit_tool_outputs: Optional[Callable[[ThreadRun, "BaseAsyncAgentEventHandler[T]"], Awaitable[None]]] = ( - None - ) - self.buffer: Optional[str] = None - - def initialize( - self, - response_iterator: AsyncIterator[bytes], - submit_tool_outputs: Callable[[ThreadRun, "BaseAsyncAgentEventHandler[T]"], Awaitable[None]], - ): - self.response_iterator = ( - async_chain(self.response_iterator, response_iterator) if self.response_iterator else response_iterator - ) - self.submit_tool_outputs = submit_tool_outputs - - async def __anext__(self) -> T: - self.buffer = "" if self.buffer is None else self.buffer - if self.response_iterator is None: - raise ValueError("The response handler was not initialized.") - - if not "\n\n" in self.buffer: - async for chunk in self.response_iterator: - self.buffer += chunk.decode("utf-8") - if "\n\n" in self.buffer: - break - - if self.buffer == "": - raise StopAsyncIteration() - - event_str = "" - if "\n\n" in self.buffer: - event_end_index = self.buffer.index("\n\n") - event_str = self.buffer[:event_end_index] - self.buffer = self.buffer[event_end_index:].lstrip() - else: - event_str = self.buffer - self.buffer = "" - - return await self._process_event(event_str) - - async def _process_event(self, event_data_str: str) -> T: - raise NotImplementedError("This method needs to be implemented.") - - async def until_done(self) -> None: - """ - Iterates through all events until the stream is marked as done. - Calls the provided callback function with each event data. - """ - try: - async for _ in self: - pass - except StopAsyncIteration: - pass - - -class BaseAgentEventHandler(Iterator[T]): - - def __init__(self) -> None: - self.response_iterator: Optional[Iterator[bytes]] = None - self.submit_tool_outputs: Optional[Callable[[ThreadRun, "BaseAgentEventHandler[T]"], None]] = None - self.buffer: Optional[str] = None - - def initialize( - self, - response_iterator: Iterator[bytes], - submit_tool_outputs: Callable[[ThreadRun, "BaseAgentEventHandler[T]"], None], - ) -> None: - self.response_iterator = ( - itertools.chain(self.response_iterator, response_iterator) if self.response_iterator else response_iterator - ) - self.submit_tool_outputs = submit_tool_outputs - - def __next__(self) -> T: - self.buffer = "" if self.buffer is None else self.buffer - if self.response_iterator is None: - raise ValueError("The response handler was not initialized.") - - if not "\n\n" in self.buffer: - for chunk in self.response_iterator: - self.buffer += chunk.decode("utf-8") - if "\n\n" in self.buffer: - break - - if self.buffer == "": - raise StopIteration() - - event_str = "" - if "\n\n" in self.buffer: - event_end_index = self.buffer.index("\n\n") - event_str = self.buffer[:event_end_index] - self.buffer = self.buffer[event_end_index:].lstrip() - else: - event_str = self.buffer - self.buffer = "" - - return self._process_event(event_str) - - def _process_event(self, event_data_str: str) -> T: - raise NotImplementedError("This method needs to be implemented.") - - def until_done(self) -> None: - """ - Iterates through all events until the stream is marked as done. - Calls the provided callback function with each event data. - """ - try: - for _ in self: - pass - except StopIteration: - pass - - -class AsyncAgentEventHandler(BaseAsyncAgentEventHandler[Tuple[str, StreamEventData, Optional[EventFunctionReturnT]]]): - - async def _process_event(self, event_data_str: str) -> Tuple[str, StreamEventData, Optional[EventFunctionReturnT]]: - event_type, event_data_obj = _parse_event(event_data_str) - if ( - isinstance(event_data_obj, ThreadRun) - and event_data_obj.status == "requires_action" - and isinstance(event_data_obj.required_action, SubmitToolOutputsAction) - ): - await cast(Callable[[ThreadRun, "BaseAsyncAgentEventHandler"], Awaitable[None]], self.submit_tool_outputs)( - event_data_obj, self - ) - - func_rt: Optional[EventFunctionReturnT] = None - try: - if isinstance(event_data_obj, MessageDeltaChunk): - func_rt = await self.on_message_delta(event_data_obj) - elif isinstance(event_data_obj, ThreadMessage): - func_rt = await self.on_thread_message(event_data_obj) - elif isinstance(event_data_obj, ThreadRun): - func_rt = await self.on_thread_run(event_data_obj) - elif isinstance(event_data_obj, RunStep): - func_rt = await self.on_run_step(event_data_obj) - elif isinstance(event_data_obj, RunStepDeltaChunk): - func_rt = await self.on_run_step_delta(event_data_obj) - elif event_type == AgentStreamEvent.ERROR: - func_rt = await self.on_error(event_data_obj) - elif event_type == AgentStreamEvent.DONE: - func_rt = await self.on_done() - else: - func_rt = await self.on_unhandled_event( - event_type, event_data_obj - ) # pylint: disable=assignment-from-none - except Exception as e: # pylint: disable=broad-exception-caught - logging.error("Error in event handler for event '%s': %s", event_type, e) - return event_type, event_data_obj, func_rt - - async def on_message_delta( - self, delta: "MessageDeltaChunk" # pylint: disable=unused-argument - ) -> Optional[EventFunctionReturnT]: - """Handle message delta events. - - :param MessageDeltaChunk delta: The message delta. - :rtype: Optional[EventFunctionReturnT] - """ - return None - - async def on_thread_message( - self, message: "ThreadMessage" # pylint: disable=unused-argument - ) -> Optional[EventFunctionReturnT]: - """Handle thread message events. - - :param ThreadMessage message: The thread message. - :rtype: Optional[EventFunctionReturnT] - """ - return None - - async def on_thread_run( - self, run: "ThreadRun" # pylint: disable=unused-argument - ) -> Optional[EventFunctionReturnT]: - """Handle thread run events. - - :param ThreadRun run: The thread run. - :rtype: Optional[EventFunctionReturnT] - """ - return None - - async def on_run_step(self, step: "RunStep") -> Optional[EventFunctionReturnT]: # pylint: disable=unused-argument - """Handle run step events. - - :param RunStep step: The run step. - :rtype: Optional[EventFunctionReturnT] - """ - return None - - async def on_run_step_delta( - self, delta: "RunStepDeltaChunk" # pylint: disable=unused-argument - ) -> Optional[EventFunctionReturnT]: - """Handle run step delta events. - - :param RunStepDeltaChunk delta: The run step delta. - :rtype: Optional[EventFunctionReturnT] - """ - return None - - async def on_error(self, data: str) -> Optional[EventFunctionReturnT]: # pylint: disable=unused-argument - """Handle error events. - - :param str data: The error event's data. - :rtype: Optional[EventFunctionReturnT] - """ - return None - - async def on_done( - self, - ) -> Optional[EventFunctionReturnT]: - """Handle the completion of the stream. - :rtype: Optional[EventFunctionReturnT] - """ - return None - - async def on_unhandled_event( - self, event_type: str, event_data: str # pylint: disable=unused-argument - ) -> Optional[EventFunctionReturnT]: - """Handle any unhandled event types. - - :param str event_type: The event type. - :param Any event_data: The event's data. - :rtype: Optional[EventFunctionReturnT] - """ - return None - - -class AgentEventHandler(BaseAgentEventHandler[Tuple[str, StreamEventData, Optional[EventFunctionReturnT]]]): - - def _process_event(self, event_data_str: str) -> Tuple[str, StreamEventData, Optional[EventFunctionReturnT]]: - - event_type, event_data_obj = _parse_event(event_data_str) - if ( - isinstance(event_data_obj, ThreadRun) - and event_data_obj.status == "requires_action" - and isinstance(event_data_obj.required_action, SubmitToolOutputsAction) - ): - cast(Callable[[ThreadRun, "BaseAgentEventHandler"], Awaitable[None]], self.submit_tool_outputs)( - event_data_obj, self - ) - - func_rt: Optional[EventFunctionReturnT] = None - try: - if isinstance(event_data_obj, MessageDeltaChunk): - func_rt = self.on_message_delta(event_data_obj) # pylint: disable=assignment-from-none - elif isinstance(event_data_obj, ThreadMessage): - func_rt = self.on_thread_message(event_data_obj) # pylint: disable=assignment-from-none - elif isinstance(event_data_obj, ThreadRun): - func_rt = self.on_thread_run(event_data_obj) # pylint: disable=assignment-from-none - elif isinstance(event_data_obj, RunStep): - func_rt = self.on_run_step(event_data_obj) # pylint: disable=assignment-from-none - elif isinstance(event_data_obj, RunStepDeltaChunk): - func_rt = self.on_run_step_delta(event_data_obj) # pylint: disable=assignment-from-none - elif event_type == AgentStreamEvent.ERROR: - func_rt = self.on_error(event_data_obj) # pylint: disable=assignment-from-none - elif event_type == AgentStreamEvent.DONE: - func_rt = self.on_done() # pylint: disable=assignment-from-none - else: - func_rt = self.on_unhandled_event(event_type, event_data_obj) # pylint: disable=assignment-from-none - except Exception as e: # pylint: disable=broad-exception-caught - logging.error("Error in event handler for event '%s': %s", event_type, e) - return event_type, event_data_obj, func_rt - - def on_message_delta( - self, delta: "MessageDeltaChunk" # pylint: disable=unused-argument - ) -> Optional[EventFunctionReturnT]: - """Handle message delta events. - - :param MessageDeltaChunk delta: The message delta. - :rtype: Optional[EventFunctionReturnT] - """ - return None - - def on_thread_message( - self, message: "ThreadMessage" # pylint: disable=unused-argument - ) -> Optional[EventFunctionReturnT]: - """Handle thread message events. - - :param ThreadMessage message: The thread message. - :rtype: Optional[EventFunctionReturnT] - """ - return None - - def on_thread_run(self, run: "ThreadRun") -> Optional[EventFunctionReturnT]: # pylint: disable=unused-argument - """Handle thread run events. - - :param ThreadRun run: The thread run. - :rtype: Optional[EventFunctionReturnT] - """ - return None - - def on_run_step(self, step: "RunStep") -> Optional[EventFunctionReturnT]: # pylint: disable=unused-argument - """Handle run step events. - - :param RunStep step: The run step. - :rtype: Optional[EventFunctionReturnT] - """ - return None - - def on_run_step_delta( - self, delta: "RunStepDeltaChunk" # pylint: disable=unused-argument - ) -> Optional[EventFunctionReturnT]: - """Handle run step delta events. - - :param RunStepDeltaChunk delta: The run step delta. - :rtype: Optional[EventFunctionReturnT] - """ - return None - - def on_error(self, data: str) -> Optional[EventFunctionReturnT]: # pylint: disable=unused-argument - """Handle error events. - - :param str data: The error event's data. - :rtype: Optional[EventFunctionReturnT] - """ - return None - - def on_done( - self, - ) -> Optional[EventFunctionReturnT]: - """Handle the completion of the stream.""" - return None - - def on_unhandled_event( - self, event_type: str, event_data: str # pylint: disable=unused-argument - ) -> Optional[EventFunctionReturnT]: - """Handle any unhandled event types. - - :param str event_type: The event type. - :param Any event_data: The event's data. - """ - return None - - -class AsyncAgentRunStream(Generic[BaseAsyncAgentEventHandlerT]): - def __init__( - self, - response_iterator: AsyncIterator[bytes], - submit_tool_outputs: Callable[[ThreadRun, BaseAsyncAgentEventHandlerT], Awaitable[None]], - event_handler: BaseAsyncAgentEventHandlerT, - ): - self.response_iterator = response_iterator - self.event_handler = event_handler - self.submit_tool_outputs = submit_tool_outputs - self.event_handler.initialize( - self.response_iterator, - cast(Callable[[ThreadRun, BaseAsyncAgentEventHandler], Awaitable[None]], submit_tool_outputs), - ) - - async def __aenter__(self): - return self.event_handler - - async def __aexit__(self, exc_type, exc_val, exc_tb): - close_method = getattr(self.response_iterator, "close", None) - if callable(close_method): - result = close_method() - if asyncio.iscoroutine(result): - await result - - -class AgentRunStream(Generic[BaseAgentEventHandlerT]): - def __init__( - self, - response_iterator: Iterator[bytes], - submit_tool_outputs: Callable[[ThreadRun, BaseAgentEventHandlerT], None], - event_handler: BaseAgentEventHandlerT, - ): - self.response_iterator = response_iterator - self.event_handler = event_handler - self.submit_tool_outputs = submit_tool_outputs - self.event_handler.initialize( - self.response_iterator, - cast(Callable[[ThreadRun, BaseAgentEventHandler], None], submit_tool_outputs), - ) - - def __enter__(self): - return self.event_handler - - def __exit__(self, exc_type, exc_val, exc_tb): - close_method = getattr(self.response_iterator, "close", None) - if callable(close_method): - close_method() - - -class OpenAIPageableListOfThreadMessage(OpenAIPageableListOfThreadMessageGenerated): - - @property - def text_messages(self) -> List[MessageTextContent]: - """Returns all text message contents in the messages. - - :rtype: List[MessageTextContent] - """ - texts = [content for msg in self.data for content in msg.text_messages] - return texts - - @property - def image_contents(self) -> List[MessageImageFileContent]: - """Returns all image file contents from image message contents in the messages. - - :rtype: List[MessageImageFileContent] - """ - return [content for msg in self.data for content in msg.image_contents] - - @property - def file_citation_annotations(self) -> List[MessageTextFileCitationAnnotation]: - """Returns all file citation annotations from text message annotations in the messages. - - :rtype: List[MessageTextFileCitationAnnotation] - """ - annotations = [annotation for msg in self.data for annotation in msg.file_citation_annotations] - return annotations - - @property - def file_path_annotations(self) -> List[MessageTextFilePathAnnotation]: - """Returns all file path annotations from text message annotations in the messages. - - :rtype: List[MessageTextFilePathAnnotation] - """ - annotations = [annotation for msg in self.data for annotation in msg.file_path_annotations] - return annotations - - def get_last_message_by_role(self, role: MessageRole) -> Optional[ThreadMessage]: - """Returns the last message from a sender in the specified role. - - :param role: The role of the sender. - :type role: MessageRole - - :return: The last message from a sender in the specified role. - :rtype: ~azure.ai.projects.models.ThreadMessage - """ - for msg in self.data: - if msg.role == role: - return msg - return None - - def get_last_text_message_by_role(self, role: MessageRole) -> Optional[MessageTextContent]: - """Returns the last text message from a sender in the specified role. - - :param role: The role of the sender. - :type role: MessageRole - - :return: The last text message from a sender in the specified role. - :rtype: ~azure.ai.projects.models.MessageTextContent - """ - for msg in self.data: - if msg.role == role: - for content in msg.content: - if isinstance(content, MessageTextContent): - return content - return None - - -__all__: List[str] = [ - "AgentEventHandler", - "AgentRunStream", - "AsyncAgentRunStream", - "AsyncFunctionTool", - "AsyncToolSet", - "AzureAISearchTool", - "AzureFunctionTool", - "BaseAsyncAgentEventHandler", - "BaseAgentEventHandler", - "CodeInterpreterTool", - "ConnectionProperties", - "AsyncAgentEventHandler", - "OpenAIPageableListOfThreadMessage", - "FileSearchTool", - "FunctionTool", - "OpenApiTool", - "BingGroundingTool", - "StreamEventData", - "SharepointTool", - "FabricTool", - "AzureAISearchTool", - "SASTokenCredential", - "Tool", - "ToolSet", - "BaseAsyncAgentEventHandlerT", - "BaseAgentEventHandlerT", - "ThreadMessage", - "MessageTextFileCitationAnnotation", - "MessageDeltaChunk", - "MessageAttachment", -] # Add all objects you want publicly available to users at this package level +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level def patch_sdk(): diff --git a/sdk/ai/azure-ai-projects/azure/ai/projects/operations/__init__.py b/sdk/ai/azure-ai-projects/azure/ai/projects/operations/__init__.py index 64c4031e2bb6..f1eb231e4404 100644 --- a/sdk/ai/azure-ai-projects/azure/ai/projects/operations/__init__.py +++ b/sdk/ai/azure-ai-projects/azure/ai/projects/operations/__init__.py @@ -12,7 +12,6 @@ if TYPE_CHECKING: from ._patch import * # pylint: disable=unused-wildcard-import -from ._operations import AgentsOperations # type: ignore from ._operations import ConnectionsOperations # type: ignore from ._operations import TelemetryOperations # type: ignore from ._operations import EvaluationsOperations # type: ignore @@ -22,7 +21,6 @@ from ._patch import patch_sdk as _patch_sdk __all__ = [ - "AgentsOperations", "ConnectionsOperations", "TelemetryOperations", "EvaluationsOperations", diff --git a/sdk/ai/azure-ai-projects/azure/ai/projects/operations/_operations.py b/sdk/ai/azure-ai-projects/azure/ai/projects/operations/_operations.py index ef27ce1eca4c..94d22d8937cd 100644 --- a/sdk/ai/azure-ai-projects/azure/ai/projects/operations/_operations.py +++ b/sdk/ai/azure-ai-projects/azure/ai/projects/operations/_operations.py @@ -1,4 +1,4 @@ -# pylint: disable=too-many-lines +# pylint: disable=line-too-long,useless-suppression,too-many-lines # coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. @@ -9,7 +9,7 @@ from io import IOBase import json import sys -from typing import Any, Callable, Dict, IO, Iterable, List, Optional, TYPE_CHECKING, TypeVar, Union, overload +from typing import Any, Callable, Dict, IO, Iterable, List, Optional, TypeVar, Union, overload import urllib.parse from azure.core import PipelineClient @@ -29,19 +29,15 @@ from azure.core.tracing.decorator import distributed_trace from azure.core.utils import case_insensitive_dict -from .. import _model_base, models as _models +from .. import models as _models from .._configuration import AIProjectClientConfiguration from .._model_base import SdkJSONEncoder, _deserialize from .._serialization import Deserializer, Serializer -from .._vendor import FileType, prepare_multipart_form_data if sys.version_info >= (3, 9): from collections.abc import MutableMapping else: from typing import MutableMapping # type: ignore - -if TYPE_CHECKING: - from .. import _types JSON = MutableMapping[str, Any] # pylint: disable=unsubscriptable-object _Unset: Any = object() T = TypeVar("T") @@ -51,34 +47,30 @@ _SERIALIZER.client_side_validation = False -def build_agents_create_agent_request(**kwargs: Any) -> HttpRequest: +def build_connections_get_workspace_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", "2024-07-01-preview")) accept = _headers.pop("Accept", "application/json") # Construct URL - _url = "/assistants" + _url = "/" # 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) + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) -def build_agents_list_agents_request( +def build_connections_list_connections_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, + category: Optional[Union[str, _models.ConnectionType]] = None, + include_all: Optional[bool] = None, + target: Optional[str] = None, **kwargs: Any ) -> HttpRequest: _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) @@ -88,18 +80,16 @@ def build_agents_list_agents_request( accept = _headers.pop("Accept", "application/json") # Construct URL - _url = "/assistants" + _url = "/connections" # Construct parameters _params["api-version"] = _SERIALIZER.query("api_version", api_version, "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") + if category is not None: + _params["category"] = _SERIALIZER.query("category", category, "str") + if include_all is not None: + _params["includeAll"] = _SERIALIZER.query("include_all", include_all, "bool") + if target is not None: + _params["target"] = _SERIALIZER.query("target", target, "str") # Construct headers _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") @@ -107,7 +97,7 @@ def build_agents_list_agents_request( return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) -def build_agents_get_agent_request(assistant_id: str, **kwargs: Any) -> HttpRequest: +def build_connections_get_connection_request(connection_name: str, **kwargs: Any) -> HttpRequest: _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) @@ -115,9 +105,9 @@ def build_agents_get_agent_request(assistant_id: str, **kwargs: Any) -> HttpRequ accept = _headers.pop("Accept", "application/json") # Construct URL - _url = "/assistants/{assistantId}" + _url = "/connections/{connectionName}" path_format_arguments = { - "assistantId": _SERIALIZER.url("assistant_id", assistant_id, "str"), + "connectionName": _SERIALIZER.url("connection_name", connection_name, "str"), } _url: str = _url.format(**path_format_arguments) # type: ignore @@ -131,7 +121,9 @@ def build_agents_get_agent_request(assistant_id: str, **kwargs: Any) -> HttpRequ return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) -def build_agents_update_agent_request(assistant_id: str, **kwargs: Any) -> HttpRequest: +def build_connections_get_connection_with_secrets_request( # pylint: disable=name-too-long + connection_name: str, **kwargs: Any +) -> HttpRequest: _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) @@ -140,9 +132,9 @@ def build_agents_update_agent_request(assistant_id: str, **kwargs: Any) -> HttpR accept = _headers.pop("Accept", "application/json") # Construct URL - _url = "/assistants/{assistantId}" + _url = "/connections/{connectionName}/listsecrets" path_format_arguments = { - "assistantId": _SERIALIZER.url("assistant_id", assistant_id, "str"), + "connectionName": _SERIALIZER.url("connection_name", connection_name, "str"), } _url: str = _url.format(**path_format_arguments) # type: ignore @@ -158,7 +150,7 @@ def build_agents_update_agent_request(assistant_id: str, **kwargs: Any) -> HttpR return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) -def build_agents_delete_agent_request(assistant_id: str, **kwargs: Any) -> HttpRequest: +def build_telemetry_get_app_insights_request(app_insights_resource_url: str, **kwargs: Any) -> HttpRequest: _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) @@ -166,9 +158,9 @@ def build_agents_delete_agent_request(assistant_id: str, **kwargs: Any) -> HttpR accept = _headers.pop("Accept", "application/json") # Construct URL - _url = "/assistants/{assistantId}" + _url = "/{appInsightsResourceUrl}" path_format_arguments = { - "assistantId": _SERIALIZER.url("assistant_id", assistant_id, "str"), + "appInsightsResourceUrl": _SERIALIZER.url("app_insights_resource_url", app_insights_resource_url, "str"), } _url: str = _url.format(**path_format_arguments) # type: ignore @@ -179,32 +171,10 @@ def build_agents_delete_agent_request(assistant_id: str, **kwargs: Any) -> HttpR # Construct headers _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") - return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs) - - -def build_agents_create_thread_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", "2024-07-01-preview")) - accept = _headers.pop("Accept", "application/json") - - # Construct URL - _url = "/threads" - - # 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) + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) -def build_agents_get_thread_request(thread_id: str, **kwargs: Any) -> HttpRequest: +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 {}) @@ -212,9 +182,9 @@ def build_agents_get_thread_request(thread_id: str, **kwargs: Any) -> HttpReques accept = _headers.pop("Accept", "application/json") # Construct URL - _url = "/threads/{threadId}" + _url = "/evaluations/runs/{id}" path_format_arguments = { - "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + "id": _SERIALIZER.url("id", id, "str"), } _url: str = _url.format(**path_format_arguments) # type: ignore @@ -228,24 +198,19 @@ def build_agents_get_thread_request(thread_id: str, **kwargs: Any) -> HttpReques return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) -def build_agents_update_thread_request(thread_id: str, **kwargs: Any) -> HttpRequest: +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", "2024-07-01-preview")) + api_version: str = kwargs.pop("api_version", _params.pop("apiVersion", "2024-07-01-preview")) 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 + _url = "/evaluations/runs:run" # Construct parameters - _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + _params["apiVersion"] = _SERIALIZER.query("api_version", api_version, "str") # Construct headers if content_type is not None: @@ -255,7 +220,9 @@ def build_agents_update_thread_request(thread_id: str, **kwargs: Any) -> HttpReq return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) -def build_agents_delete_thread_request(thread_id: str, **kwargs: Any) -> HttpRequest: +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 {}) @@ -263,23 +230,24 @@ def build_agents_delete_thread_request(thread_id: str, **kwargs: Any) -> HttpReq 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 + _url = "/evaluations/runs" # 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="DELETE", url=_url, params=_params, headers=_headers, **kwargs) + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) -def build_agents_create_message_request(thread_id: str, **kwargs: Any) -> HttpRequest: +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 {}) @@ -288,9 +256,9 @@ def build_agents_create_message_request(thread_id: str, **kwargs: Any) -> HttpRe accept = _headers.pop("Accept", "application/json") # Construct URL - _url = "/threads/{threadId}/messages" + _url = "/evaluations/runs/{id}" path_format_arguments = { - "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + "id": _SERIALIZER.url("id", id, "str"), } _url: str = _url.format(**path_format_arguments) # type: ignore @@ -303,53 +271,10 @@ def build_agents_create_message_request(thread_id: str, **kwargs: Any) -> HttpRe _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_agents_list_messages_request( - 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 {}) - - api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-07-01-preview")) - 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 - _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") - 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) + return HttpRequest(method="PATCH", url=_url, params=_params, headers=_headers, **kwargs) -def build_agents_get_message_request(thread_id: str, message_id: str, **kwargs: Any) -> HttpRequest: +def build_evaluations_get_schedule_request(name: str, **kwargs: Any) -> HttpRequest: _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) @@ -357,10 +282,9 @@ def build_agents_get_message_request(thread_id: str, message_id: str, **kwargs: accept = _headers.pop("Accept", "application/json") # Construct URL - _url = "/threads/{threadId}/messages/{messageId}" + _url = "/evaluations/schedules/{name}" path_format_arguments = { - "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), - "messageId": _SERIALIZER.url("message_id", message_id, "str"), + "name": _SERIALIZER.url("name", name, "str"), } _url: str = _url.format(**path_format_arguments) # type: ignore @@ -374,7 +298,9 @@ def build_agents_get_message_request(thread_id: str, message_id: str, **kwargs: return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) -def build_agents_update_message_request(thread_id: str, message_id: str, **kwargs: Any) -> HttpRequest: +def build_evaluations_create_or_replace_schedule_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 {}) @@ -383,10 +309,9 @@ def build_agents_update_message_request(thread_id: str, message_id: str, **kwarg accept = _headers.pop("Accept", "application/json") # Construct URL - _url = "/threads/{threadId}/messages/{messageId}" + _url = "/evaluations/schedules/{name}" path_format_arguments = { - "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), - "messageId": _SERIALIZER.url("message_id", message_id, "str"), + "name": _SERIALIZER.url("name", name, "str"), } _url: str = _url.format(**path_format_arguments) # type: ignore @@ -399,6161 +324,157 @@ def build_agents_update_message_request(thread_id: str, message_id: str, **kwarg _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) + return HttpRequest(method="PUT", url=_url, params=_params, headers=_headers, **kwargs) -def build_agents_create_run_request( - thread_id: str, *, include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, **kwargs: Any +def build_evaluations_list_schedule_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 {}) - content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-07-01-preview")) 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 + _url = "/evaluations/schedules" # Construct parameters _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") - if include is not None: - _params["include[]"] = _SERIALIZER.query("include", include, "[str]", div=",") + 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 - 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) + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) -def build_agents_list_runs_request( - thread_id: str, - *, - limit: Optional[int] = None, - order: Optional[Union[str, _models.ListSortOrder]] = None, - after: Optional[str] = None, - before: Optional[str] = None, - **kwargs: Any +def build_evaluations_disable_schedule_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", "2024-07-01-preview")) + api_version: str = kwargs.pop("api_version", _params.pop("apiVersion", "2024-07-01-preview")) accept = _headers.pop("Accept", "application/json") # Construct URL - _url = "/threads/{threadId}/runs" + _url = "/evaluations/schedules/{name}/disable" path_format_arguments = { - "threadId": _SERIALIZER.url("thread_id", thread_id, "str"), + "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 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") + _params["apiVersion"] = _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) - + return HttpRequest(method="PATCH", url=_url, params=_params, headers=_headers, **kwargs) -def build_agents_get_run_request(thread_id: str, run_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", "2024-07-01-preview")) - accept = _headers.pop("Accept", "application/json") +class ConnectionsOperations: + """ + .. warning:: + **DO NOT** instantiate this class directly. - # 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"), - } + Instead, you should access the following operations through + :class:`~azure.ai.projects.AIProjectClient`'s + :attr:`connections` attribute. + """ - _url: str = _url.format(**path_format_arguments) # type: ignore + def __init__(self, *args, **kwargs): + input_args = list(args) + self._client: PipelineClient = input_args.pop(0) if input_args else kwargs.pop("client") + self._config: AIProjectClientConfiguration = input_args.pop(0) if input_args else kwargs.pop("config") + self._serialize: Serializer = input_args.pop(0) if input_args else kwargs.pop("serializer") + self._deserialize: Deserializer = input_args.pop(0) if input_args else kwargs.pop("deserializer") - # Construct parameters - _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + @distributed_trace + def _get_workspace(self, **kwargs: Any) -> _models._models.GetWorkspaceResponse: + """Gets the properties of the specified machine learning workspace. - # Construct headers - _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + :return: GetWorkspaceResponse. The GetWorkspaceResponse is compatible with MutableMapping + :rtype: ~azure.ai.projects.models._models.GetWorkspaceResponse + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) - return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + cls: ClsType[_models._models.GetWorkspaceResponse] = kwargs.pop("cls", None) -def build_agents_update_run_request(thread_id: str, run_id: str, **kwargs: Any) -> HttpRequest: - _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) - _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + _request = build_connections_get_workspace_request( + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), + "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), + "resourceGroupName": self._serialize.url( + "self._config.resource_group_name", self._config.resource_group_name, "str" + ), + "projectName": self._serialize.url("self._config.project_name", self._config.project_name, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) - content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) - api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-07-01-preview")) - accept = _headers.pop("Accept", "application/json") + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) - # 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"), - } + response = pipeline_response.http_response - _url: str = _url.format(**path_format_arguments) # type: ignore + 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) - # Construct parameters - _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize( + _models._models.GetWorkspaceResponse, response.json() # pylint: disable=protected-access + ) - # 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") + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore - return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + return deserialized # type: ignore - -def build_agents_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 {}) - _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", "2024-07-01-preview")) - 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 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_agents_cancel_run_request(thread_id: str, run_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", "2024-07-01-preview")) - 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 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_agents_create_thread_and_run_request(**kwargs: Any) -> HttpRequest: # pylint: disable=name-too-long - _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", "2024-07-01-preview")) - accept = _headers.pop("Accept", "application/json") - - # Construct URL - _url = "/threads/runs" - - # 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_agents_get_run_step_request( - thread_id: str, - run_id: str, - step_id: str, - *, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = 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", "2024-07-01-preview")) - 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 parameters - _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") - if include is not None: - _params["include[]"] = _SERIALIZER.query("include", include, "[str]", div=",") - - # Construct headers - _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") - - return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) - - -def build_agents_list_run_steps_request( - thread_id: str, - run_id: str, - *, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = 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 {}) - - api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-07-01-preview")) - 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 - _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") - if include is not None: - _params["include[]"] = _SERIALIZER.query("include", include, "[str]", div=",") - 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_agents_list_files_request( - *, 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 {}) - - api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-07-01-preview")) - accept = _headers.pop("Accept", "application/json") - - # Construct URL - _url = "/files" - - # Construct parameters - _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") - 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_agents_upload_file_request(**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", "2024-07-01-preview")) - accept = _headers.pop("Accept", "application/json") - - # Construct URL - _url = "/files" - - # 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_agents_delete_file_request(file_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", "2024-07-01-preview")) - 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 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_agents_get_file_request(file_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", "2024-07-01-preview")) - 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 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_agents_get_file_content_request(file_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", "2024-07-01-preview")) - 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 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_agents_list_vector_stores_request( - *, - 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 {}) - - api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-07-01-preview")) - accept = _headers.pop("Accept", "application/json") - - # Construct URL - _url = "/vector_stores" - - # Construct parameters - _params["api-version"] = _SERIALIZER.query("api_version", api_version, "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_agents_create_vector_store_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", "2024-07-01-preview")) - accept = _headers.pop("Accept", "application/json") - - # Construct URL - _url = "/vector_stores" - - # 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_agents_get_vector_store_request(vector_store_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", "2024-07-01-preview")) - 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 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_agents_modify_vector_store_request(vector_store_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", "2024-07-01-preview")) - 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 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_agents_delete_vector_store_request(vector_store_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", "2024-07-01-preview")) - 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 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_agents_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 {}) - - api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-07-01-preview")) - 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 - _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") - 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_agents_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 {}) - _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", "2024-07-01-preview")) - 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 - _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_agents_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 {}) - _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) - - api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-07-01-preview")) - 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 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_agents_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 {}) - _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) - - api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-07-01-preview")) - 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 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_agents_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 {}) - _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", "2024-07-01-preview")) - 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 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_agents_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 {}) - _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) - - api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-07-01-preview")) - 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 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_agents_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 {}) - _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) - - api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-07-01-preview")) - 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 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_agents_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 {}) - - api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2024-07-01-preview")) - 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 - _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") - 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_connections_get_workspace_request(**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", "2024-07-01-preview")) - accept = _headers.pop("Accept", "application/json") - - # Construct URL - _url = "/" - - # 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_list_connections_request( # pylint: disable=name-too-long - *, - category: Optional[Union[str, _models.ConnectionType]] = None, - include_all: Optional[bool] = None, - target: Optional[str] = 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", "2024-07-01-preview")) - accept = _headers.pop("Accept", "application/json") - - # Construct URL - _url = "/connections" - - # Construct parameters - _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") - if category is not None: - _params["category"] = _SERIALIZER.query("category", category, "str") - if include_all is not None: - _params["includeAll"] = _SERIALIZER.query("include_all", include_all, "bool") - if target is not None: - _params["target"] = _SERIALIZER.query("target", target, "str") - - # Construct headers - _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") - - return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) - - -def build_connections_get_connection_request(connection_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", "2024-07-01-preview")) - accept = _headers.pop("Accept", "application/json") - - # Construct URL - _url = "/connections/{connectionName}" - path_format_arguments = { - "connectionName": _SERIALIZER.url("connection_name", connection_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_get_connection_with_secrets_request( # pylint: disable=name-too-long - connection_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", "2024-07-01-preview")) - accept = _headers.pop("Accept", "application/json") - - # Construct URL - _url = "/connections/{connectionName}/listsecrets" - path_format_arguments = { - "connectionName": _SERIALIZER.url("connection_name", connection_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_telemetry_get_app_insights_request(app_insights_resource_url: 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", "2024-07-01-preview")) - accept = _headers.pop("Accept", "application/json") - - # Construct URL - _url = "/{appInsightsResourceUrl}" - path_format_arguments = { - "appInsightsResourceUrl": _SERIALIZER.url("app_insights_resource_url", app_insights_resource_url, "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_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", "2024-07-01-preview")) - accept = _headers.pop("Accept", "application/json") - - # Construct URL - _url = "/evaluations/runs/{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("apiVersion", "2024-07-01-preview")) - accept = _headers.pop("Accept", "application/json") - - # Construct URL - _url = "/evaluations/runs:run" - - # Construct parameters - _params["apiVersion"] = _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", "2024-07-01-preview")) - accept = _headers.pop("Accept", "application/json") - - # Construct URL - _url = "/evaluations/runs" - - # 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", "2024-07-01-preview")) - accept = _headers.pop("Accept", "application/json") - - # Construct URL - _url = "/evaluations/runs/{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_evaluations_get_schedule_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", "2024-07-01-preview")) - accept = _headers.pop("Accept", "application/json") - - # Construct URL - _url = "/evaluations/schedules/{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_evaluations_create_or_replace_schedule_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", "2024-07-01-preview")) - accept = _headers.pop("Accept", "application/json") - - # Construct URL - _url = "/evaluations/schedules/{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_evaluations_list_schedule_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", "2024-07-01-preview")) - accept = _headers.pop("Accept", "application/json") - - # Construct URL - _url = "/evaluations/schedules" - - # 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_disable_schedule_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("apiVersion", "2024-07-01-preview")) - accept = _headers.pop("Accept", "application/json") - - # Construct URL - _url = "/evaluations/schedules/{name}/disable" - path_format_arguments = { - "name": _SERIALIZER.url("name", name, "str"), - } - - _url: str = _url.format(**path_format_arguments) # type: ignore - - # Construct parameters - _params["apiVersion"] = _SERIALIZER.query("api_version", api_version, "str") - - # Construct headers - _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") - - return HttpRequest(method="PATCH", url=_url, params=_params, headers=_headers, **kwargs) - - -class AgentsOperations: # pylint: disable=too-many-public-methods - """ - .. warning:: - **DO NOT** instantiate this class directly. - - Instead, you should access the following operations through - :class:`~azure.ai.projects.AIProjectClient`'s - :attr:`agents` attribute. - """ - - def __init__(self, *args, **kwargs): - input_args = list(args) - self._client: PipelineClient = input_args.pop(0) if input_args else kwargs.pop("client") - self._config: AIProjectClientConfiguration = input_args.pop(0) if input_args else kwargs.pop("config") - self._serialize: Serializer = input_args.pop(0) if input_args else kwargs.pop("serializer") - self._deserialize: Deserializer = input_args.pop(0) if input_args else kwargs.pop("deserializer") - - @overload - def create_agent( - self, - *, - model: str, - content_type: str = "application/json", - name: Optional[str] = None, - description: Optional[str] = None, - instructions: Optional[str] = None, - tools: Optional[List[_models.ToolDefinition]] = None, - tool_resources: Optional[_models.ToolResources] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.Agent: - """Creates a new agent. - - :keyword model: The ID of the model to use. Required. - :paramtype model: str - :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. - Default value is "application/json". - :paramtype content_type: str - :keyword name: The name of the new agent. Default value is None. - :paramtype name: str - :keyword description: The description of the new agent. Default value is None. - :paramtype description: str - :keyword instructions: The system instructions for the new agent to use. Default value is None. - :paramtype instructions: str - :keyword tools: The collection of tools to enable for the new agent. Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword tool_resources: A set of resources that are used by the agent'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. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.ToolResources - :keyword 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. Default value is - None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword response_format: The response format of the tool calls used by this agent. Is one of - the following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat or - ~azure.ai.projects.models.ResponseFormatJsonSchemaType - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def create_agent(self, body: JSON, *, content_type: str = "application/json", **kwargs: Any) -> _models.Agent: - """Creates a new agent. - - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def create_agent(self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any) -> _models.Agent: - """Creates a new agent. - - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace - def create_agent( - self, - body: Union[JSON, IO[bytes]] = _Unset, - *, - model: str = _Unset, - name: Optional[str] = None, - description: Optional[str] = None, - instructions: Optional[str] = None, - tools: Optional[List[_models.ToolDefinition]] = None, - tool_resources: Optional[_models.ToolResources] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.Agent: - """Creates a new agent. - - :param body: Is either a JSON type or a IO[bytes] type. Required. - :type body: JSON or IO[bytes] - :keyword model: The ID of the model to use. Required. - :paramtype model: str - :keyword name: The name of the new agent. Default value is None. - :paramtype name: str - :keyword description: The description of the new agent. Default value is None. - :paramtype description: str - :keyword instructions: The system instructions for the new agent to use. Default value is None. - :paramtype instructions: str - :keyword tools: The collection of tools to enable for the new agent. Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword tool_resources: A set of resources that are used by the agent'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. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.ToolResources - :keyword 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. Default value is - None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword response_format: The response format of the tool calls used by this agent. Is one of - the following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat or - ~azure.ai.projects.models.ResponseFormatJsonSchemaType - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :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.Agent] = kwargs.pop("cls", None) - - if body is _Unset: - if model is _Unset: - raise TypeError("missing required argument: model") - body = { - "description": description, - "instructions": instructions, - "metadata": metadata, - "model": model, - "name": name, - "response_format": response_format, - "temperature": temperature, - "tool_resources": tool_resources, - "tools": tools, - "top_p": top_p, - } - 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_agents_create_agent_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.Agent, response.json()) - - if cls: - return cls(pipeline_response, deserialized, {}) # type: ignore - - return deserialized # type: ignore - - @distributed_trace - def list_agents( - self, - *, - limit: Optional[int] = None, - order: Optional[Union[str, _models.ListSortOrder]] = None, - after: Optional[str] = None, - before: Optional[str] = None, - **kwargs: Any - ) -> _models.OpenAIPageableListOfAgent: - """Gets a list of agents 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.projects.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: OpenAIPageableListOfAgent. The OpenAIPageableListOfAgent is compatible with - MutableMapping - :rtype: ~azure.ai.projects.models.OpenAIPageableListOfAgent - :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.OpenAIPageableListOfAgent] = kwargs.pop("cls", None) - - _request = build_agents_list_agents_request( - limit=limit, - order=order, - after=after, - before=before, - api_version=self._config.api_version, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.OpenAIPageableListOfAgent, response.json()) - - if cls: - return cls(pipeline_response, deserialized, {}) # type: ignore - - return deserialized # type: ignore - - @distributed_trace - def get_agent(self, assistant_id: str, **kwargs: Any) -> _models.Agent: - """Retrieves an existing agent. - - :param assistant_id: Identifier of the agent. Required. - :type assistant_id: str - :return: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :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.Agent] = kwargs.pop("cls", None) - - _request = build_agents_get_agent_request( - assistant_id=assistant_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.Agent, response.json()) - - if cls: - return cls(pipeline_response, deserialized, {}) # type: ignore - - return deserialized # type: ignore - - @overload - def update_agent( - self, - assistant_id: str, - *, - content_type: str = "application/json", - 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.ToolResources] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.Agent: - """Modifies an existing agent. - - :param assistant_id: The ID of the agent to modify. Required. - :type assistant_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 model: The ID of the model to use. Default value is None. - :paramtype model: str - :keyword name: The modified name for the agent to use. Default value is None. - :paramtype name: str - :keyword description: The modified description for the agent to use. Default value is None. - :paramtype description: str - :keyword instructions: The modified system instructions for the new agent to use. Default value - is None. - :paramtype instructions: str - :keyword tools: The modified collection of tools to enable for the agent. Default value is - None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword tool_resources: A set of resources that are used by the agent'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. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.ToolResources - :keyword 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. Default value is - None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword response_format: The response format of the tool calls used by this agent. Is one of - the following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat or - ~azure.ai.projects.models.ResponseFormatJsonSchemaType - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def update_agent( - self, assistant_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any - ) -> _models.Agent: - """Modifies an existing agent. - - :param assistant_id: The ID of the agent to modify. Required. - :type assistant_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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def update_agent( - self, assistant_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any - ) -> _models.Agent: - """Modifies an existing agent. - - :param assistant_id: The ID of the agent to modify. Required. - :type assistant_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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace - def update_agent( - self, - assistant_id: str, - body: Union[JSON, IO[bytes]] = _Unset, - *, - 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.ToolResources] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.Agent: - """Modifies an existing agent. - - :param assistant_id: The ID of the agent to modify. Required. - :type assistant_id: str - :param body: Is either a JSON type or a IO[bytes] type. Required. - :type body: JSON or IO[bytes] - :keyword model: The ID of the model to use. Default value is None. - :paramtype model: str - :keyword name: The modified name for the agent to use. Default value is None. - :paramtype name: str - :keyword description: The modified description for the agent to use. Default value is None. - :paramtype description: str - :keyword instructions: The modified system instructions for the new agent to use. Default value - is None. - :paramtype instructions: str - :keyword tools: The modified collection of tools to enable for the agent. Default value is - None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword tool_resources: A set of resources that are used by the agent'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. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.ToolResources - :keyword 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. Default value is - None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword response_format: The response format of the tool calls used by this agent. Is one of - the following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat or - ~azure.ai.projects.models.ResponseFormatJsonSchemaType - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :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.Agent] = kwargs.pop("cls", None) - - if body is _Unset: - body = { - "description": description, - "instructions": instructions, - "metadata": metadata, - "model": model, - "name": name, - "response_format": response_format, - "temperature": temperature, - "tool_resources": tool_resources, - "tools": tools, - "top_p": top_p, - } - 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_agents_update_agent_request( - assistant_id=assistant_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.Agent, response.json()) - - if cls: - return cls(pipeline_response, deserialized, {}) # type: ignore - - return deserialized # type: ignore - - @distributed_trace - def delete_agent(self, assistant_id: str, **kwargs: Any) -> _models.AgentDeletionStatus: - """Deletes an agent. - - :param assistant_id: Identifier of the agent. Required. - :type assistant_id: str - :return: AgentDeletionStatus. The AgentDeletionStatus is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.AgentDeletionStatus - :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.AgentDeletionStatus] = kwargs.pop("cls", None) - - _request = build_agents_delete_agent_request( - assistant_id=assistant_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.AgentDeletionStatus, response.json()) - - if cls: - return cls(pipeline_response, deserialized, {}) # type: ignore - - return deserialized # type: ignore - - @overload - def create_thread( - self, - *, - content_type: str = "application/json", - messages: Optional[List[_models.ThreadMessageOptions]] = None, - tool_resources: Optional[_models.ToolResources] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.AgentThread: - """Creates a new thread. Threads contain messages and can be run by agents. - - :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. - Default value is "application/json". - :paramtype content_type: str - :keyword messages: The initial messages to associate with the new thread. Default value is - None. - :paramtype messages: list[~azure.ai.projects.models.ThreadMessageOptions] - :keyword tool_resources: A set of resources that are made available to the agent'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. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.ToolResources - :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: AgentThread. The AgentThread is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.AgentThread - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def create_thread( - self, body: JSON, *, content_type: str = "application/json", **kwargs: Any - ) -> _models.AgentThread: - """Creates a new thread. Threads contain messages and can be run by agents. - - :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: AgentThread. The AgentThread is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.AgentThread - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def create_thread( - self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any - ) -> _models.AgentThread: - """Creates a new thread. Threads contain messages and can be run by agents. - - :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: AgentThread. The AgentThread is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.AgentThread - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace - def create_thread( - self, - body: Union[JSON, IO[bytes]] = _Unset, - *, - messages: Optional[List[_models.ThreadMessageOptions]] = None, - tool_resources: Optional[_models.ToolResources] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.AgentThread: - """Creates a new thread. Threads contain messages and can be run by agents. - - :param body: Is either a JSON type or a IO[bytes] type. Required. - :type body: JSON or IO[bytes] - :keyword messages: The initial messages to associate with the new thread. Default value is - None. - :paramtype messages: list[~azure.ai.projects.models.ThreadMessageOptions] - :keyword tool_resources: A set of resources that are made available to the agent'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. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.ToolResources - :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: AgentThread. The AgentThread is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.AgentThread - :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.AgentThread] = kwargs.pop("cls", None) - - if body is _Unset: - body = {"messages": messages, "metadata": metadata, "tool_resources": tool_resources} - 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_agents_create_thread_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.AgentThread, 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.AgentThread: - """Gets information about an existing thread. - - :param thread_id: Identifier of the thread. Required. - :type thread_id: str - :return: AgentThread. The AgentThread is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.AgentThread - :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.AgentThread] = kwargs.pop("cls", None) - - _request = build_agents_get_thread_request( - thread_id=thread_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.AgentThread, response.json()) - - if cls: - return cls(pipeline_response, deserialized, {}) # type: ignore - - return deserialized # type: ignore - - @overload - def update_thread( - self, - thread_id: str, - *, - content_type: str = "application/json", - tool_resources: Optional[_models.ToolResources] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.AgentThread: - """Modifies an existing thread. - - :param thread_id: The ID of the thread to modify. Required. - :type thread_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 tool_resources: A set of resources that are made available to the agent'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. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.ToolResources - :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: AgentThread. The AgentThread is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.AgentThread - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def update_thread( - self, thread_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any - ) -> _models.AgentThread: - """Modifies an existing thread. - - :param thread_id: The ID of the thread to modify. Required. - :type thread_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: AgentThread. The AgentThread is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.AgentThread - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def update_thread( - self, thread_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any - ) -> _models.AgentThread: - """Modifies an existing thread. - - :param thread_id: The ID of the thread to modify. Required. - :type thread_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: AgentThread. The AgentThread is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.AgentThread - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace - def update_thread( - self, - thread_id: str, - body: Union[JSON, IO[bytes]] = _Unset, - *, - tool_resources: Optional[_models.ToolResources] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.AgentThread: - """Modifies an existing thread. - - :param thread_id: The ID of the thread to modify. Required. - :type thread_id: str - :param body: Is either a JSON type or a IO[bytes] type. Required. - :type body: JSON or IO[bytes] - :keyword tool_resources: A set of resources that are made available to the agent'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. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.ToolResources - :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: AgentThread. The AgentThread is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.AgentThread - :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.AgentThread] = kwargs.pop("cls", None) - - if body is _Unset: - body = {"metadata": metadata, "tool_resources": tool_resources} - 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_agents_update_thread_request( - thread_id=thread_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.AgentThread, 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: Identifier of the thread. Required. - :type thread_id: str - :return: ThreadDeletionStatus. The ThreadDeletionStatus is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_delete_thread_request( - thread_id=thread_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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 - - @overload - def create_message( - self, - thread_id: str, - *, - role: Union[str, _models.MessageRole], - content: str, - content_type: str = "application/json", - attachments: Optional[List[_models.MessageAttachment]] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.ThreadMessage: - """Creates a new message on a specified thread. - - :param thread_id: Identifier of the thread. Required. - :type thread_id: str - :keyword 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 agent. Use this value to insert - messages from the agent into the - conversation. Known values are: "user" and "assistant". Required. - :paramtype role: str or ~azure.ai.projects.models.MessageRole - :keyword 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. - :paramtype content: str - :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. - Default value is "application/json". - :paramtype content_type: str - :keyword attachments: A list of files attached to the message, and the tools they should be - added to. Default value is None. - :paramtype attachments: list[~azure.ai.projects.models.MessageAttachment] - :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.projects.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: Identifier of the thread. Required. - :type thread_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.projects.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: Identifier of the thread. Required. - :type thread_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.projects.models.ThreadMessage - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace - def create_message( - self, - thread_id: str, - body: Union[JSON, IO[bytes]] = _Unset, - *, - role: Union[str, _models.MessageRole] = _Unset, - content: str = _Unset, - attachments: Optional[List[_models.MessageAttachment]] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.ThreadMessage: - """Creates a new message on a specified thread. - - :param thread_id: Identifier of the thread. Required. - :type thread_id: str - :param body: Is either a JSON type or a IO[bytes] type. Required. - :type body: JSON or IO[bytes] - :keyword 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 agent. Use this value to insert - messages from the agent into the - conversation. Known values are: "user" and "assistant". Required. - :paramtype role: str or ~azure.ai.projects.models.MessageRole - :keyword 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. - :paramtype content: str - :keyword attachments: A list of files attached to the message, and the tools they should be - added to. Default value is None. - :paramtype attachments: list[~azure.ai.projects.models.MessageAttachment] - :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.projects.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: - if role is _Unset: - raise TypeError("missing required argument: role") - if content is _Unset: - raise TypeError("missing required argument: content") - body = {"attachments": attachments, "content": content, "metadata": metadata, "role": role} - 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_agents_create_message_request( - thread_id=thread_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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: Identifier of the thread. 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.projects.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.projects.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_agents_list_messages_request( - thread_id=thread_id, - run_id=run_id, - limit=limit, - order=order, - after=after, - before=before, - api_version=self._config.api_version, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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: Identifier of the thread. Required. - :type thread_id: str - :param message_id: Identifier of the message. Required. - :type message_id: str - :return: ThreadMessage. The ThreadMessage is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_get_message_request( - thread_id=thread_id, - message_id=message_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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, - *, - 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: Identifier of the thread. Required. - :type thread_id: str - :param message_id: Identifier of the message. 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.projects.models.ThreadMessage - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @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: Identifier of the thread. Required. - :type thread_id: str - :param message_id: Identifier of the message. 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.projects.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: Identifier of the thread. Required. - :type thread_id: str - :param message_id: Identifier of the message. 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.projects.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: Identifier of the thread. Required. - :type thread_id: str - :param message_id: Identifier of the message. 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.projects.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_agents_update_message_request( - thread_id=thread_id, - message_id=message_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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 create_run( - self, - thread_id: str, - *, - assistant_id: str, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - content_type: str = "application/json", - model: Optional[str] = None, - instructions: Optional[str] = None, - additional_instructions: Optional[str] = None, - additional_messages: Optional[List[_models.ThreadMessageOptions]] = None, - tools: Optional[List[_models.ToolDefinition]] = None, - stream_parameter: 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.AgentsApiToolChoiceOption"] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - parallel_tool_calls: Optional[bool] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.ThreadRun: - """Creates a new run for an agent thread. - - :param thread_id: Identifier of the thread. Required. - :type thread_id: str - :keyword assistant_id: The ID of the agent that should run the thread. Required. - :paramtype assistant_id: str - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. - Default value is "application/json". - :paramtype content_type: str - :keyword model: The overridden model name that the agent should use to run the thread. Default - value is None. - :paramtype model: str - :keyword instructions: The overridden system instructions that the agent should use to run the - thread. Default value is None. - :paramtype instructions: str - :keyword 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. Default value is None. - :paramtype additional_instructions: str - :keyword additional_messages: Adds additional messages to the thread before creating the run. - Default value is None. - :paramtype additional_messages: list[~azure.ai.projects.models.ThreadMessageOptions] - :keyword tools: The overridden list of enabled tools that the agent should use to run the - thread. Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :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 - :keyword 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. Default - value is None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword 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. Default - value is None. - :paramtype max_prompt_tokens: int - :keyword 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. Default value is None. - :paramtype max_completion_tokens: int - :keyword truncation_strategy: The strategy to use for dropping messages as the context windows - moves forward. Default value is None. - :paramtype truncation_strategy: ~azure.ai.projects.models.TruncationObject - :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of - the following types: str, Union[str, "_models.AgentsApiToolChoiceOptionMode"], - AgentsNamedToolChoice Default value is None. - :paramtype tool_choice: str or str or ~azure.ai.projects.models.AgentsApiToolChoiceOptionMode - or ~azure.ai.projects.models.AgentsNamedToolChoice - :keyword response_format: Specifies the format that the model must output. Is one of the - following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat or - ~azure.ai.projects.models.ResponseFormatJsonSchemaType - :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. - Default value is None. - :paramtype parallel_tool_calls: bool - :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.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def create_run( - self, - thread_id: str, - body: JSON, - *, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - content_type: str = "application/json", - **kwargs: Any - ) -> _models.ThreadRun: - """Creates a new run for an agent thread. - - :param thread_id: Identifier of the thread. Required. - :type thread_id: str - :param body: Required. - :type body: JSON - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :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.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def create_run( - self, - thread_id: str, - body: IO[bytes], - *, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - content_type: str = "application/json", - **kwargs: Any - ) -> _models.ThreadRun: - """Creates a new run for an agent thread. - - :param thread_id: Identifier of the thread. Required. - :type thread_id: str - :param body: Required. - :type body: IO[bytes] - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :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.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace - def create_run( - self, - thread_id: str, - body: Union[JSON, IO[bytes]] = _Unset, - *, - assistant_id: str = _Unset, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - model: Optional[str] = None, - instructions: Optional[str] = None, - additional_instructions: Optional[str] = None, - additional_messages: Optional[List[_models.ThreadMessageOptions]] = None, - tools: Optional[List[_models.ToolDefinition]] = None, - stream_parameter: 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.AgentsApiToolChoiceOption"] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - parallel_tool_calls: Optional[bool] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.ThreadRun: - """Creates a new run for an agent thread. - - :param thread_id: Identifier of the thread. Required. - :type thread_id: str - :param body: Is either a JSON type or a IO[bytes] type. Required. - :type body: JSON or IO[bytes] - :keyword assistant_id: The ID of the agent that should run the thread. Required. - :paramtype assistant_id: str - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :keyword model: The overridden model name that the agent should use to run the thread. Default - value is None. - :paramtype model: str - :keyword instructions: The overridden system instructions that the agent should use to run the - thread. Default value is None. - :paramtype instructions: str - :keyword 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. Default value is None. - :paramtype additional_instructions: str - :keyword additional_messages: Adds additional messages to the thread before creating the run. - Default value is None. - :paramtype additional_messages: list[~azure.ai.projects.models.ThreadMessageOptions] - :keyword tools: The overridden list of enabled tools that the agent should use to run the - thread. Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :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 - :keyword 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. Default - value is None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword 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. Default - value is None. - :paramtype max_prompt_tokens: int - :keyword 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. Default value is None. - :paramtype max_completion_tokens: int - :keyword truncation_strategy: The strategy to use for dropping messages as the context windows - moves forward. Default value is None. - :paramtype truncation_strategy: ~azure.ai.projects.models.TruncationObject - :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of - the following types: str, Union[str, "_models.AgentsApiToolChoiceOptionMode"], - AgentsNamedToolChoice Default value is None. - :paramtype tool_choice: str or str or ~azure.ai.projects.models.AgentsApiToolChoiceOptionMode - or ~azure.ai.projects.models.AgentsNamedToolChoice - :keyword response_format: Specifies the format that the model must output. Is one of the - following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat or - ~azure.ai.projects.models.ResponseFormatJsonSchemaType - :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. - Default value is None. - :paramtype parallel_tool_calls: bool - :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.projects.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 assistant_id is _Unset: - raise TypeError("missing required argument: assistant_id") - body = { - "additional_instructions": additional_instructions, - "additional_messages": additional_messages, - "assistant_id": assistant_id, - "instructions": instructions, - "max_completion_tokens": max_completion_tokens, - "max_prompt_tokens": max_prompt_tokens, - "metadata": metadata, - "model": model, - "parallel_tool_calls": parallel_tool_calls, - "response_format": response_format, - "stream": stream_parameter, - "temperature": temperature, - "tool_choice": tool_choice, - "tools": tools, - "top_p": top_p, - "truncation_strategy": truncation_strategy, - } - 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_agents_create_run_request( - thread_id=thread_id, - include=include, - 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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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: Identifier of the thread. 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.projects.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.projects.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_agents_list_runs_request( - thread_id=thread_id, - limit=limit, - order=order, - after=after, - before=before, - api_version=self._config.api_version, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. Required. - :type run_id: str - :return: ThreadRun. The ThreadRun is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_get_run_request( - thread_id=thread_id, - run_id=run_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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, - *, - content_type: str = "application/json", - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.ThreadRun: - """Modifies an existing thread run. - - :param thread_id: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. 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.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @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: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. 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.projects.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: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. 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.projects.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: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. 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.projects.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_agents_update_run_request( - thread_id=thread_id, - run_id=run_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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, - *, - 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: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. 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.projects.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. Default value is None. - :paramtype stream_parameter: bool - :return: ThreadRun. The ThreadRun is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @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: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. 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.projects.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: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. 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.projects.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: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. 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.projects.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. Default value is None. - :paramtype stream_parameter: bool - :return: ThreadRun. The ThreadRun is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_submit_tool_outputs_to_run_request( - thread_id=thread_id, - run_id=run_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. Required. - :type run_id: str - :return: ThreadRun. The ThreadRun is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_cancel_run_request( - thread_id=thread_id, - run_id=run_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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, - *, - assistant_id: str, - content_type: str = "application/json", - thread: Optional[_models.AgentThreadCreationOptions] = None, - model: Optional[str] = None, - instructions: Optional[str] = None, - tools: Optional[List[_models.ToolDefinition]] = None, - tool_resources: Optional[_models.UpdateToolResourcesOptions] = None, - stream_parameter: 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.AgentsApiToolChoiceOption"] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - parallel_tool_calls: Optional[bool] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.ThreadRun: - """Creates a new agent thread and immediately starts a run using that new thread. - - :keyword assistant_id: The ID of the agent for which the thread should be created. Required. - :paramtype assistant_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 thread: The details used to create the new thread. If no thread is provided, an empty - one will be created. Default value is None. - :paramtype thread: ~azure.ai.projects.models.AgentThreadCreationOptions - :keyword model: The overridden model that the agent should use to run the thread. Default value - is None. - :paramtype model: str - :keyword instructions: The overridden system instructions the agent should use to run the - thread. Default value is None. - :paramtype instructions: str - :keyword tools: The overridden list of enabled tools the agent should use to run the thread. - Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword tool_resources: Override the tools the agent can use for this run. This is useful for - modifying the behavior on a per-run basis. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.UpdateToolResourcesOptions - :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 - :keyword 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. Default - value is None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword 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. Default - value is None. - :paramtype max_prompt_tokens: int - :keyword 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. Default value is None. - :paramtype max_completion_tokens: int - :keyword truncation_strategy: The strategy to use for dropping messages as the context windows - moves forward. Default value is None. - :paramtype truncation_strategy: ~azure.ai.projects.models.TruncationObject - :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of - the following types: str, Union[str, "_models.AgentsApiToolChoiceOptionMode"], - AgentsNamedToolChoice Default value is None. - :paramtype tool_choice: str or str or ~azure.ai.projects.models.AgentsApiToolChoiceOptionMode - or ~azure.ai.projects.models.AgentsNamedToolChoice - :keyword response_format: Specifies the format that the model must output. Is one of the - following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat or - ~azure.ai.projects.models.ResponseFormatJsonSchemaType - :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. - Default value is None. - :paramtype parallel_tool_calls: bool - :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.projects.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 agent thread and immediately starts a run using that new thread. - - :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.projects.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 agent thread and immediately starts a run using that new thread. - - :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.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace - def create_thread_and_run( - self, - body: Union[JSON, IO[bytes]] = _Unset, - *, - assistant_id: str = _Unset, - thread: Optional[_models.AgentThreadCreationOptions] = None, - model: Optional[str] = None, - instructions: Optional[str] = None, - tools: Optional[List[_models.ToolDefinition]] = None, - tool_resources: Optional[_models.UpdateToolResourcesOptions] = None, - stream_parameter: 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.AgentsApiToolChoiceOption"] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - parallel_tool_calls: Optional[bool] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.ThreadRun: - """Creates a new agent thread and immediately starts a run using that new thread. - - :param body: Is either a JSON type or a IO[bytes] type. Required. - :type body: JSON or IO[bytes] - :keyword assistant_id: The ID of the agent for which the thread should be created. Required. - :paramtype assistant_id: str - :keyword thread: The details used to create the new thread. If no thread is provided, an empty - one will be created. Default value is None. - :paramtype thread: ~azure.ai.projects.models.AgentThreadCreationOptions - :keyword model: The overridden model that the agent should use to run the thread. Default value - is None. - :paramtype model: str - :keyword instructions: The overridden system instructions the agent should use to run the - thread. Default value is None. - :paramtype instructions: str - :keyword tools: The overridden list of enabled tools the agent should use to run the thread. - Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword tool_resources: Override the tools the agent can use for this run. This is useful for - modifying the behavior on a per-run basis. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.UpdateToolResourcesOptions - :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 - :keyword 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. Default - value is None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword 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. Default - value is None. - :paramtype max_prompt_tokens: int - :keyword 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. Default value is None. - :paramtype max_completion_tokens: int - :keyword truncation_strategy: The strategy to use for dropping messages as the context windows - moves forward. Default value is None. - :paramtype truncation_strategy: ~azure.ai.projects.models.TruncationObject - :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of - the following types: str, Union[str, "_models.AgentsApiToolChoiceOptionMode"], - AgentsNamedToolChoice Default value is None. - :paramtype tool_choice: str or str or ~azure.ai.projects.models.AgentsApiToolChoiceOptionMode - or ~azure.ai.projects.models.AgentsNamedToolChoice - :keyword response_format: Specifies the format that the model must output. Is one of the - following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat, ResponseFormatJsonSchemaType Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat or - ~azure.ai.projects.models.ResponseFormatJsonSchemaType - :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. - Default value is None. - :paramtype parallel_tool_calls: bool - :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.projects.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 assistant_id is _Unset: - raise TypeError("missing required argument: assistant_id") - body = { - "assistant_id": assistant_id, - "instructions": instructions, - "max_completion_tokens": max_completion_tokens, - "max_prompt_tokens": max_prompt_tokens, - "metadata": metadata, - "model": model, - "parallel_tool_calls": parallel_tool_calls, - "response_format": response_format, - "stream": stream_parameter, - "temperature": temperature, - "thread": thread, - "tool_choice": tool_choice, - "tool_resources": tool_resources, - "tools": tools, - "top_p": top_p, - "truncation_strategy": truncation_strategy, - } - 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_agents_create_thread_and_run_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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 get_run_step( - self, - thread_id: str, - run_id: str, - step_id: str, - *, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - **kwargs: Any - ) -> _models.RunStep: - """Gets a single run step from a thread run. - - :param thread_id: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. Required. - :type run_id: str - :param step_id: Identifier of the run step. Required. - :type step_id: str - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :return: RunStep. The RunStep is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_get_run_step_request( - thread_id=thread_id, - run_id=run_id, - step_id=step_id, - include=include, - api_version=self._config.api_version, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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, - *, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - 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: Identifier of the thread. Required. - :type thread_id: str - :param run_id: Identifier of the run. Required. - :type run_id: str - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :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.projects.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.projects.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_agents_list_run_steps_request( - thread_id=thread_id, - run_id=run_id, - include=include, - limit=limit, - order=order, - after=after, - before=before, - api_version=self._config.api_version, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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 - - @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: The purpose of the file. 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.projects.models.FilePurpose - :return: FileListResponse. The FileListResponse is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_list_files_request( - purpose=purpose, - api_version=self._config.api_version, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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, *, 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, in bytes. Required. - :paramtype file: ~azure.ai.projects._vendor.FileType - :keyword purpose: The intended purpose of the uploaded file. Use ``assistants`` for Agents and - Message files, ``vision`` for Agents image file inputs, ``batch`` for Batch API, and - ``fine-tune`` for Fine-tuning. Known values are: "fine-tune", "fine-tune-results", - "assistants", "assistants_output", "batch", "batch_output", and "vision". Required. - :paramtype purpose: str or ~azure.ai.projects.models.FilePurpose - :keyword filename: The name of the file. Default value is None. - :paramtype filename: str - :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.OpenAIFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @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.projects.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, in bytes. Required. - :paramtype file: ~azure.ai.projects._vendor.FileType - :keyword purpose: The intended purpose of the uploaded file. Use ``assistants`` for Agents and - Message files, ``vision`` for Agents image file inputs, ``batch`` for Batch API, and - ``fine-tune`` for Fine-tuning. Known values are: "fine-tune", "fine-tune-results", - "assistants", "assistants_output", "batch", "batch_output", and "vision". Required. - :paramtype purpose: str or ~azure.ai.projects.models.FilePurpose - :keyword filename: The name of the file. Default value is None. - :paramtype filename: str - :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_upload_file_request( - api_version=self._config.api_version, - files=_files, - data=_data, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.projects.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_agents_delete_file_request( - file_id=file_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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.projects.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_agents_get_file_request( - file_id=file_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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: - """Retrieves the raw content of a specific file. - - :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_agents_get_file_content_request( - file_id=file_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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 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.projects.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.projects.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_agents_list_vector_stores_request( - limit=limit, - order=order, - after=after, - before=before, - api_version=self._config.api_version, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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, - *, - content_type: str = "application/json", - file_ids: Optional[List[str]] = None, - name: Optional[str] = None, - store_configuration: Optional[_models.VectorStoreConfiguration] = None, - expires_after: Optional[_models.VectorStoreExpirationPolicy] = None, - chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.VectorStore: - """Creates a vector store. - - :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. - Default value is "application/json". - :paramtype content_type: 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. Default value is None. - :paramtype file_ids: list[str] - :keyword name: The name of the vector store. Default value is None. - :paramtype name: str - :keyword store_configuration: The vector store configuration, used when vector store is created - from Azure asset URIs. Default value is None. - :paramtype store_configuration: ~azure.ai.projects.models.VectorStoreConfiguration - :keyword expires_after: Details on when this vector store expires. Default value is None. - :paramtype expires_after: ~azure.ai.projects.models.VectorStoreExpirationPolicy - :keyword 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. Default value is None. - :paramtype chunking_strategy: ~azure.ai.projects.models.VectorStoreChunkingStrategyRequest - :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: VectorStore. The VectorStore is compatible with MutableMapping - :rtype: ~azure.ai.projects.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: 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.projects.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: 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.projects.models.VectorStore - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace - def create_vector_store( - self, - body: Union[JSON, IO[bytes]] = _Unset, - *, - file_ids: Optional[List[str]] = None, - name: Optional[str] = None, - store_configuration: Optional[_models.VectorStoreConfiguration] = None, - expires_after: Optional[_models.VectorStoreExpirationPolicy] = None, - chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.VectorStore: - """Creates a vector store. - - :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. Default value is None. - :paramtype file_ids: list[str] - :keyword name: The name of the vector store. Default value is None. - :paramtype name: str - :keyword store_configuration: The vector store configuration, used when vector store is created - from Azure asset URIs. Default value is None. - :paramtype store_configuration: ~azure.ai.projects.models.VectorStoreConfiguration - :keyword expires_after: Details on when this vector store expires. Default value is None. - :paramtype expires_after: ~azure.ai.projects.models.VectorStoreExpirationPolicy - :keyword 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. Default value is None. - :paramtype chunking_strategy: ~azure.ai.projects.models.VectorStoreChunkingStrategyRequest - :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: VectorStore. The VectorStore is compatible with MutableMapping - :rtype: ~azure.ai.projects.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) - - if body is _Unset: - body = { - "chunking_strategy": chunking_strategy, - "configuration": store_configuration, - "expires_after": expires_after, - "file_ids": file_ids, - "metadata": metadata, - "name": name, - } - 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_agents_create_vector_store_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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: Identifier of the vector store. Required. - :type vector_store_id: str - :return: VectorStore. The VectorStore is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_get_vector_store_request( - vector_store_id=vector_store_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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, - *, - content_type: str = "application/json", - name: Optional[str] = None, - expires_after: Optional[_models.VectorStoreExpirationPolicy] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.VectorStore: - """The ID of the vector store to modify. - - :param vector_store_id: Identifier of the vector store. Required. - :type vector_store_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 name: The name of the vector store. Default value is None. - :paramtype name: str - :keyword expires_after: Details on when this vector store expires. Default value is None. - :paramtype expires_after: ~azure.ai.projects.models.VectorStoreExpirationPolicy - :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: VectorStore. The VectorStore is compatible with MutableMapping - :rtype: ~azure.ai.projects.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: Identifier of the vector store. 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: VectorStore. The VectorStore is compatible with MutableMapping - :rtype: ~azure.ai.projects.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: Identifier of the vector store. 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: VectorStore. The VectorStore is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStore - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace - def modify_vector_store( - self, - vector_store_id: str, - body: Union[JSON, IO[bytes]] = _Unset, - *, - name: Optional[str] = None, - expires_after: Optional[_models.VectorStoreExpirationPolicy] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any - ) -> _models.VectorStore: - """The ID of the vector store to modify. - - :param vector_store_id: Identifier of the vector store. 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 name: The name of the vector store. Default value is None. - :paramtype name: str - :keyword expires_after: Details on when this vector store expires. Default value is None. - :paramtype expires_after: ~azure.ai.projects.models.VectorStoreExpirationPolicy - :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: VectorStore. The VectorStore is compatible with MutableMapping - :rtype: ~azure.ai.projects.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) - - if body is _Unset: - body = {"expires_after": expires_after, "metadata": metadata, "name": name} - 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_agents_modify_vector_store_request( - vector_store_id=vector_store_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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: Identifier of the vector store. Required. - :type vector_store_id: str - :return: VectorStoreDeletionStatus. The VectorStoreDeletionStatus is compatible with - MutableMapping - :rtype: ~azure.ai.projects.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_agents_delete_vector_store_request( - vector_store_id=vector_store_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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: Identifier of the vector store. 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.projects.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.projects.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.projects.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_agents_list_vector_store_files_request( - vector_store_id=vector_store_id, - filter=filter, - limit=limit, - order=order, - after=after, - before=before, - api_version=self._config.api_version, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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, - *, - content_type: str = "application/json", - file_id: Optional[str] = None, - data_source: Optional[_models.VectorStoreDataSource] = None, - 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: Identifier of the vector store. Required. - :type vector_store_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 file_id: Identifier of the file. Default value is None. - :paramtype file_id: str - :keyword data_source: Azure asset ID. Default value is None. - :paramtype data_source: ~azure.ai.projects.models.VectorStoreDataSource - :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.projects.models.VectorStoreChunkingStrategyRequest - :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStoreFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @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: Identifier of the vector store. 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.projects.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: Identifier of the vector store. 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.projects.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: Optional[str] = None, - data_source: Optional[_models.VectorStoreDataSource] = None, - 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: Identifier of the vector store. 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: Identifier of the file. Default value is None. - :paramtype file_id: str - :keyword data_source: Azure asset ID. Default value is None. - :paramtype data_source: ~azure.ai.projects.models.VectorStoreDataSource - :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.projects.models.VectorStoreChunkingStrategyRequest - :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.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: - body = {"chunking_strategy": chunking_strategy, "data_source": data_source, "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_agents_create_vector_store_file_request( - vector_store_id=vector_store_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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: Identifier of the vector store. Required. - :type vector_store_id: str - :param file_id: Identifier of the file. Required. - :type file_id: str - :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_get_vector_store_file_request( - vector_store_id=vector_store_id, - file_id=file_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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: Identifier of the vector store. Required. - :type vector_store_id: str - :param file_id: Identifier of the file. Required. - :type file_id: str - :return: VectorStoreFileDeletionStatus. The VectorStoreFileDeletionStatus is compatible with - MutableMapping - :rtype: ~azure.ai.projects.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_agents_delete_vector_store_file_request( - vector_store_id=vector_store_id, - file_id=file_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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, - *, - content_type: str = "application/json", - file_ids: Optional[List[str]] = None, - data_sources: Optional[List[_models.VectorStoreDataSource]] = None, - chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, - **kwargs: Any - ) -> _models.VectorStoreFileBatch: - """Create a vector store file batch. - - :param vector_store_id: Identifier of the vector store. Required. - :type vector_store_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 file_ids: List of file identifiers. Default value is None. - :paramtype file_ids: list[str] - :keyword data_sources: List of Azure assets. Default value is None. - :paramtype data_sources: list[~azure.ai.projects.models.VectorStoreDataSource] - :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.projects.models.VectorStoreChunkingStrategyRequest - :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStoreFileBatch - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @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: Identifier of the vector store. 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.projects.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: Identifier of the vector store. 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.projects.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: Optional[List[str]] = None, - data_sources: Optional[List[_models.VectorStoreDataSource]] = None, - chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, - **kwargs: Any - ) -> _models.VectorStoreFileBatch: - """Create a vector store file batch. - - :param vector_store_id: Identifier of the vector store. 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: List of file identifiers. Default value is None. - :paramtype file_ids: list[str] - :keyword data_sources: List of Azure assets. Default value is None. - :paramtype data_sources: list[~azure.ai.projects.models.VectorStoreDataSource] - :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.projects.models.VectorStoreChunkingStrategyRequest - :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping - :rtype: ~azure.ai.projects.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: - body = {"chunking_strategy": chunking_strategy, "data_sources": data_sources, "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_agents_create_vector_store_file_batch_request( - vector_store_id=vector_store_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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: Identifier of the vector store. Required. - :type vector_store_id: str - :param batch_id: Identifier of the file batch. Required. - :type batch_id: str - :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_get_vector_store_file_batch_request( - vector_store_id=vector_store_id, - batch_id=batch_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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: Identifier of the vector store. Required. - :type vector_store_id: str - :param batch_id: Identifier of the file batch. Required. - :type batch_id: str - :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping - :rtype: ~azure.ai.projects.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_agents_cancel_vector_store_file_batch_request( - vector_store_id=vector_store_id, - batch_id=batch_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"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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: Identifier of the vector store. Required. - :type vector_store_id: str - :param batch_id: Identifier of the file batch. 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.projects.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.projects.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.projects.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_agents_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, - api_version=self._config.api_version, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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 - - -class ConnectionsOperations: - """ - .. warning:: - **DO NOT** instantiate this class directly. - - Instead, you should access the following operations through - :class:`~azure.ai.projects.AIProjectClient`'s - :attr:`connections` attribute. - """ - - def __init__(self, *args, **kwargs): - input_args = list(args) - self._client: PipelineClient = input_args.pop(0) if input_args else kwargs.pop("client") - self._config: AIProjectClientConfiguration = input_args.pop(0) if input_args else kwargs.pop("config") - self._serialize: Serializer = input_args.pop(0) if input_args else kwargs.pop("serializer") - self._deserialize: Deserializer = input_args.pop(0) if input_args else kwargs.pop("deserializer") - - @distributed_trace - def _get_workspace(self, **kwargs: Any) -> _models._models.GetWorkspaceResponse: - """Gets the properties of the specified machine learning workspace. - - :return: GetWorkspaceResponse. The GetWorkspaceResponse is compatible with MutableMapping - :rtype: ~azure.ai.projects.models._models.GetWorkspaceResponse - :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._models.GetWorkspaceResponse] = kwargs.pop("cls", None) - - _request = build_connections_get_workspace_request( - api_version=self._config.api_version, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), - "subscriptionId": self._serialize.url("self._config.subscription_id", self._config.subscription_id, "str"), - "resourceGroupName": self._serialize.url( - "self._config.resource_group_name", self._config.resource_group_name, "str" - ), - "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._models.GetWorkspaceResponse, response.json() # pylint: disable=protected-access - ) - - if cls: - return cls(pipeline_response, deserialized, {}) # type: ignore - - return deserialized # type: ignore - - @distributed_trace - def _list_connections( - self, - *, - category: Optional[Union[str, _models.ConnectionType]] = None, - include_all: Optional[bool] = None, - target: Optional[str] = None, - **kwargs: Any - ) -> _models._models.ListConnectionsResponse: - """List the details of all the connections (not including their credentials). + @distributed_trace + def _list_connections( + self, + *, + category: Optional[Union[str, _models.ConnectionType]] = None, + include_all: Optional[bool] = None, + target: Optional[str] = None, + **kwargs: Any + ) -> _models._models.ListConnectionsResponse: + """List the details of all the connections (not including their credentials). :keyword category: Category of the workspace connection. Known values are: "AzureOpenAI", - "Serverless", "AzureBlob", "AIServices", and "CognitiveSearch". Default value is None. + "Serverless", "AzureBlob", "AIServices", "CognitiveSearch", and "ApiKey". Default value is + None. :paramtype category: str or ~azure.ai.projects.models.ConnectionType :keyword include_all: Indicates whether to list datastores. Service default: do not list datastores. Default value is None. @@ -6813,7 +734,6 @@ def __init__(self, *args, **kwargs): def _get_app_insights( self, app_insights_resource_url: str, **kwargs: Any ) -> _models._models.GetAppInsightsResponse: - # pylint: disable=line-too-long """Gets the properties of the specified Application Insights resource. :param app_insights_resource_url: The AppInsights Azure resource Url. It should have the diff --git a/sdk/ai/azure-ai-projects/azure/ai/projects/operations/_patch.py b/sdk/ai/azure-ai-projects/azure/ai/projects/operations/_patch.py index a7692690bcde..f7dd32510333 100644 --- a/sdk/ai/azure-ai-projects/azure/ai/projects/operations/_patch.py +++ b/sdk/ai/azure-ai-projects/azure/ai/projects/operations/_patch.py @@ -1,4 +1,3 @@ -# pylint: disable=too-many-lines # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. @@ -7,3314 +6,9 @@ Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize """ -import io -import logging -import os -import sys -import time -from pathlib import Path -from typing import ( - IO, - TYPE_CHECKING, - Any, - Dict, - Iterator, - List, - Optional, - Sequence, - TextIO, - Union, - cast, - overload, -) +from typing import List -from azure.core.exceptions import ResourceNotFoundError -from azure.core.tracing.decorator import distributed_trace - -from .. import models as _models -from .._vendor import FileType -from ..models._enums import AuthenticationType, ConnectionType, FilePurpose, RunStatus -from ..models._models import ( - GetAppInsightsResponse, - GetConnectionResponse, - GetWorkspaceResponse, - InternalConnectionPropertiesSASAuth, - ListConnectionsResponse, -) -from ..models._patch import ConnectionProperties -from ._operations import AgentsOperations as AgentsOperationsGenerated -from ._operations import ConnectionsOperations as ConnectionsOperationsGenerated -from ._operations import TelemetryOperations as TelemetryOperationsGenerated - -if sys.version_info >= (3, 9): - from collections.abc import MutableMapping -else: - from typing import MutableMapping # type: ignore # pylint: disable=ungrouped-imports - -if TYPE_CHECKING: - # pylint: disable=unused-import,ungrouped-imports - from openai import AzureOpenAI - - from azure.ai.inference import ChatCompletionsClient, EmbeddingsClient, ImageEmbeddingsClient - - from .. import _types - -JSON = MutableMapping[str, Any] # pylint: disable=unsubscriptable-object -_Unset: Any = object() - -logger = logging.getLogger(__name__) - - -class InferenceOperations: - - def __init__(self, outer_instance): - - # All returned inference clients will have this application id set on their user-agent. - # For more info on user-agent HTTP header, see: - # https://azure.github.io/azure-sdk/general_azurecore.html#telemetry-policy - USER_AGENT_APP_ID = "AIProjectClient" - - if hasattr(outer_instance, "_user_agent") and outer_instance._user_agent: - # If the calling application has set "user_agent" when constructing the AIProjectClient, - # take that value and prepend it to USER_AGENT_APP_ID. - self._user_agent = f"{outer_instance._user_agent}-{USER_AGENT_APP_ID}" - else: - self._user_agent = USER_AGENT_APP_ID - - self._outer_instance = outer_instance - - @distributed_trace - def get_chat_completions_client( - self, *, connection_name: Optional[str] = None, **kwargs - ) -> "ChatCompletionsClient": - """Get an authenticated ChatCompletionsClient (from the package azure-ai-inference) for the default - Azure AI Services connected resource (if `connection_name` is not specificed), or from the Azure AI - Services resource given by its connection name. Keyword arguments are passed to the constructor of - ChatCompletionsClient. - - At least one AI model that supports chat completions must be deployed in this resource. - - .. note:: The package `azure-ai-inference` must be installed prior to calling this method. - - :keyword connection_name: The name of a connection to an Azure AI Services resource in your AI Foundry project. - resource. Optional. If not provided, the default Azure AI Services connection will be used. - :type connection_name: str - - :return: An authenticated chat completions client. - :rtype: ~azure.ai.inference.ChatCompletionsClient - - :raises ~azure.core.exceptions.ResourceNotFoundError: if an Azure AI Services connection - does not exist. - :raises ~azure.core.exceptions.ModuleNotFoundError: if the `azure-ai-inference` package - is not installed. - :raises ValueError: if the connection name is an empty string. - :raises ~azure.core.exceptions.HttpResponseError: - """ - kwargs.setdefault("merge_span", True) - - if connection_name is not None and not connection_name: - raise ValueError("Connection name cannot be empty") - - # Back-door way to access the old behavior where each AI model (non-OpenAI) was hosted on - # a separate "Serverless" connection. This is now deprecated. - use_serverless_connection: bool = os.getenv("USE_SERVERLESS_CONNECTION", None) == "true" - - if connection_name: - connection = self._outer_instance.connections.get(connection_name=connection_name, include_credentials=True) - else: - if use_serverless_connection: - connection = self._outer_instance.connections.get_default( - connection_type=ConnectionType.SERVERLESS, include_credentials=True - ) - else: - connection = self._outer_instance.connections.get_default( - connection_type=ConnectionType.AZURE_AI_SERVICES, include_credentials=True - ) - - logger.debug("[InferenceOperations.get_chat_completions_client] connection = %s", str(connection)) - - try: - from azure.ai.inference import ChatCompletionsClient - except ModuleNotFoundError as e: - raise ModuleNotFoundError( - "Azure AI Inference SDK is not installed. Please install it using 'pip install azure-ai-inference'" - ) from e - - if use_serverless_connection: - endpoint = connection.endpoint_url - credential_scopes = ["https://ml.azure.com/.default"] - else: - endpoint = f"{connection.endpoint_url}/models" - credential_scopes = ["https://cognitiveservices.azure.com/.default"] - - if connection.authentication_type == AuthenticationType.API_KEY: - logger.debug( - "[InferenceOperations.get_chat_completions_client] " - + "Creating ChatCompletionsClient using API key authentication" - ) - from azure.core.credentials import AzureKeyCredential - - client = ChatCompletionsClient( - endpoint=endpoint, - credential=AzureKeyCredential(connection.key), - user_agent=kwargs.pop("user_agent", self._user_agent), - **kwargs, - ) - elif connection.authentication_type == AuthenticationType.ENTRA_ID: - logger.debug( - "[InferenceOperations.get_chat_completions_client] " - + "Creating ChatCompletionsClient using Entra ID authentication" - ) - client = ChatCompletionsClient( - endpoint=endpoint, - credential=connection.token_credential, - credential_scopes=credential_scopes, - user_agent=kwargs.pop("user_agent", self._user_agent), - **kwargs, - ) - elif connection.authentication_type == AuthenticationType.SAS: - logger.debug( - "[InferenceOperations.get_chat_completions_client] " - + "Creating ChatCompletionsClient using SAS authentication" - ) - raise ValueError( - "Getting chat completions client from a connection with SAS authentication is not yet supported" - ) - else: - raise ValueError("Unknown authentication type") - - return client - - @distributed_trace - def get_embeddings_client(self, *, connection_name: Optional[str] = None, **kwargs) -> "EmbeddingsClient": - """Get an authenticated EmbeddingsClient (from the package azure-ai-inference) for the default - Azure AI Services connected resource (if `connection_name` is not specificed), or from the Azure AI - Services resource given by its connection name. Keyword arguments are passed to the constructor of - EmbeddingsClient. - - At least one AI model that supports text embeddings must be deployed in this resource. - - .. note:: The package `azure-ai-inference` must be installed prior to calling this method. - - :keyword connection_name: The name of a connection to an Azure AI Services resource in your AI Foundry project. - resource. Optional. If not provided, the default Azure AI Services connection will be used. - :type connection_name: str - - :return: An authenticated text embeddings client - :rtype: ~azure.ai.inference.EmbeddingsClient - - :raises ~azure.core.exceptions.ResourceNotFoundError: if an Azure AI Services connection - does not exist. - :raises ~azure.core.exceptions.ModuleNotFoundError: if the `azure-ai-inference` package - is not installed. - :raises ValueError: if the connection name is an empty string. - :raises ~azure.core.exceptions.HttpResponseError: - """ - kwargs.setdefault("merge_span", True) - - if connection_name is not None and not connection_name: - raise ValueError("Connection name cannot be empty") - - # Back-door way to access the old behavior where each AI model (non-OpenAI) was hosted on - # a separate "Serverless" connection. This is now deprecated. - use_serverless_connection: bool = os.getenv("USE_SERVERLESS_CONNECTION", None) == "true" - - if connection_name: - connection = self._outer_instance.connections.get(connection_name=connection_name, include_credentials=True) - else: - if use_serverless_connection: - connection = self._outer_instance.connections.get_default( - connection_type=ConnectionType.SERVERLESS, include_credentials=True - ) - else: - connection = self._outer_instance.connections.get_default( - connection_type=ConnectionType.AZURE_AI_SERVICES, include_credentials=True - ) - - logger.debug("[InferenceOperations.get_embeddings_client] connection = %s", str(connection)) - - try: - from azure.ai.inference import EmbeddingsClient - except ModuleNotFoundError as e: - raise ModuleNotFoundError( - "Azure AI Inference SDK is not installed. Please install it using 'pip install azure-ai-inference'" - ) from e - - if use_serverless_connection: - endpoint = connection.endpoint_url - credential_scopes = ["https://ml.azure.com/.default"] - else: - endpoint = f"{connection.endpoint_url}/models" - credential_scopes = ["https://cognitiveservices.azure.com/.default"] - - if connection.authentication_type == AuthenticationType.API_KEY: - logger.debug( - "[InferenceOperations.get_embeddings_client] Creating EmbeddingsClient using API key authentication" - ) - from azure.core.credentials import AzureKeyCredential - - client = EmbeddingsClient( - endpoint=endpoint, - credential=AzureKeyCredential(connection.key), - user_agent=kwargs.pop("user_agent", self._user_agent), - **kwargs, - ) - elif connection.authentication_type == AuthenticationType.ENTRA_ID: - logger.debug( - "[InferenceOperations.get_embeddings_client] Creating EmbeddingsClient using Entra ID authentication" - ) - client = EmbeddingsClient( - endpoint=endpoint, - credential=connection.token_credential, - credential_scopes=credential_scopes, - user_agent=kwargs.pop("user_agent", self._user_agent), - **kwargs, - ) - elif connection.authentication_type == AuthenticationType.SAS: - logger.debug( - "[InferenceOperations.get_embeddings_client] Creating EmbeddingsClient using SAS authentication" - ) - raise ValueError("Getting embeddings client from a connection with SAS authentication is not yet supported") - else: - raise ValueError("Unknown authentication type") - - return client - - @distributed_trace - def get_image_embeddings_client( - self, *, connection_name: Optional[str] = None, **kwargs - ) -> "ImageEmbeddingsClient": - """Get an authenticated ImageEmbeddingsClient (from the package azure-ai-inference) for the default - Azure AI Services connected resource (if `connection_name` is not specificed), or from the Azure AI - Services resource given by its connection name. Keyword arguments are passed to the constructor of - ImageEmbeddingsClient. - - At least one AI model that supports image embeddings must be deployed in this resource. - - .. note:: The package `azure-ai-inference` must be installed prior to calling this method. - - :keyword connection_name: The name of a connection to an Azure AI Services resource in your AI Foundry project. - resource. Optional. If not provided, the default Azure AI Services connection will be used. - :type connection_name: str - - :return: An authenticated image embeddings client - :rtype: ~azure.ai.inference.ImageEmbeddingsClient - - :raises ~azure.core.exceptions.ResourceNotFoundError: if an Azure AI Services connection - does not exist. - :raises ~azure.core.exceptions.ModuleNotFoundError: if the `azure-ai-inference` package - is not installed. - :raises ValueError: if the connection name is an empty string. - :raises ~azure.core.exceptions.HttpResponseError: - """ - kwargs.setdefault("merge_span", True) - - if connection_name is not None and not connection_name: - raise ValueError("Connection name cannot be empty") - - # Back-door way to access the old behavior where each AI model (non-OpenAI) was hosted on - # a separate "Serverless" connection. This is now deprecated. - use_serverless_connection: bool = os.getenv("USE_SERVERLESS_CONNECTION", None) == "true" - - if connection_name: - connection = self._outer_instance.connections.get(connection_name=connection_name, include_credentials=True) - else: - if use_serverless_connection: - connection = self._outer_instance.connections.get_default( - connection_type=ConnectionType.SERVERLESS, include_credentials=True - ) - else: - connection = self._outer_instance.connections.get_default( - connection_type=ConnectionType.AZURE_AI_SERVICES, include_credentials=True - ) - - logger.debug("[InferenceOperations.get_embeddings_client] connection = %s", str(connection)) - - try: - from azure.ai.inference import ImageEmbeddingsClient - except ModuleNotFoundError as e: - raise ModuleNotFoundError( - "Azure AI Inference SDK is not installed. Please install it using 'pip install azure-ai-inference'" - ) from e - - if use_serverless_connection: - endpoint = connection.endpoint_url - credential_scopes = ["https://ml.azure.com/.default"] - else: - endpoint = f"{connection.endpoint_url}/models" - credential_scopes = ["https://cognitiveservices.azure.com/.default"] - - if connection.authentication_type == AuthenticationType.API_KEY: - logger.debug( - "[InferenceOperations.get_image_embeddings_client] " - "Creating ImageEmbeddingsClient using API key authentication" - ) - from azure.core.credentials import AzureKeyCredential - - client = ImageEmbeddingsClient( - endpoint=endpoint, - credential=AzureKeyCredential(connection.key), - user_agent=kwargs.pop("user_agent", self._user_agent), - **kwargs, - ) - elif connection.authentication_type == AuthenticationType.ENTRA_ID: - logger.debug( - "[InferenceOperations.get_image_embeddings_client] " - "Creating ImageEmbeddingsClient using Entra ID authentication" - ) - client = ImageEmbeddingsClient( - endpoint=endpoint, - credential=connection.token_credential, - credential_scopes=credential_scopes, - user_agent=kwargs.pop("user_agent", self._user_agent), - **kwargs, - ) - elif connection.authentication_type == AuthenticationType.SAS: - logger.debug( - "[InferenceOperations.get_image_embeddings_client] " - "Creating ImageEmbeddingsClient using SAS authentication" - ) - raise ValueError( - "Getting image embeddings client from a connection with SAS authentication is not yet supported" - ) - else: - raise ValueError("Unknown authentication type") - - return client - - @distributed_trace - def get_azure_openai_client( - self, *, api_version: Optional[str] = None, connection_name: Optional[str] = None, **kwargs - ) -> "AzureOpenAI": - """Get an authenticated AzureOpenAI client (from the `openai` package) for the default - Azure OpenAI connection (if `connection_name` is not specificed), or from the Azure OpenAI - resource given by its connection name. - - .. note:: The package `openai` must be installed prior to calling this method. - - :keyword api_version: The Azure OpenAI api-version to use when creating the client. Optional. - See "Data plane - Inference" row in the table at - https://learn.microsoft.com/azure/ai-services/openai/reference#api-specs. If this keyword - is not specified, you must set the environment variable `OPENAI_API_VERSION` instead. - :paramtype api_version: str - :keyword connection_name: The name of a connection to an Azure OpenAI resource in your AI Foundry project. - resource. Optional. If not provided, the default Azure OpenAI connection will be used. - :type connection_name: str - - :return: An authenticated AzureOpenAI client - :rtype: ~openai.AzureOpenAI - - :raises ~azure.core.exceptions.ResourceNotFoundError: if an Azure OpenAI connection - does not exist. - :raises ~azure.core.exceptions.ModuleNotFoundError: if the `openai` package - is not installed. - :raises ValueError: if the connection name is an empty string. - :raises ~azure.core.exceptions.HttpResponseError: - """ - kwargs.setdefault("merge_span", True) - - if connection_name is not None and not connection_name: - raise ValueError("Connection name cannot be empty") - - if connection_name: - connection = self._outer_instance.connections.get( - connection_name=connection_name, include_credentials=True, **kwargs - ) - else: - connection = self._outer_instance.connections.get_default( - connection_type=ConnectionType.AZURE_OPEN_AI, include_credentials=True, **kwargs - ) - - logger.debug("[InferenceOperations.get_azure_openai_client] connection = %s", str(connection)) - - try: - from openai import AzureOpenAI - except ModuleNotFoundError as e: - raise ModuleNotFoundError( - "OpenAI SDK is not installed. Please install it using 'pip install openai'" - ) from e - - if connection.authentication_type == AuthenticationType.API_KEY: - logger.debug( - "[InferenceOperations.get_azure_openai_client] Creating AzureOpenAI using API key authentication" - ) - client = AzureOpenAI( - api_key=connection.key, azure_endpoint=connection.endpoint_url, api_version=api_version - ) - elif connection.authentication_type == AuthenticationType.ENTRA_ID: - logger.debug( - "[InferenceOperations.get_azure_openai_client] " + "Creating AzureOpenAI using Entra ID authentication" - ) - try: - from azure.identity import get_bearer_token_provider - except ModuleNotFoundError as e: - raise ModuleNotFoundError( - "azure.identity package not installed. Please install it using 'pip install azure.identity'" - ) from e - client = AzureOpenAI( - # See https://learn.microsoft.com/python/api/azure-identity/azure.identity?view=azure-python#azure-identity-get-bearer-token-provider # pylint: disable=line-too-long - azure_ad_token_provider=get_bearer_token_provider( - connection.token_credential, "https://cognitiveservices.azure.com/.default" - ), - azure_endpoint=connection.endpoint_url, - api_version=api_version, - ) - elif connection.authentication_type == AuthenticationType.SAS: - logger.debug( - "[InferenceOperations.get_azure_openai_client] " + "Creating AzureOpenAI using SAS authentication" - ) - raise ValueError( - "Getting an AzureOpenAI client from a connection with SAS authentication is not yet supported" - ) - else: - raise ValueError("Unknown authentication type") - - return client - - -class ConnectionsOperations(ConnectionsOperationsGenerated): - - @distributed_trace - def get_default( - self, *, connection_type: ConnectionType, include_credentials: bool = False, **kwargs: Any - ) -> ConnectionProperties: - """Get the properties of the default connection of a certain connection type, with or without - populating authentication credentials. Raises ~azure.core.exceptions.ResourceNotFoundError - exception if there are no connections of the given type. - - .. note:: - `get_default(connection_type=ConnectionType.AZURE_BLOB_STORAGE, include_credentials=True)` does not - currently work. It does work with `include_credentials=False`. - - :keyword connection_type: The connection type. Required. - :type connection_type: ~azure.ai.projects.models._models.ConnectionType - :keyword include_credentials: Whether to populate the connection properties with authentication credentials. - Optional. - :type include_credentials: bool - :return: The connection properties. - :rtype: ~azure.ai.projects.models.ConnectionProperties - :raises ~azure.core.exceptions.ResourceNotFoundError: - :raises ~azure.core.exceptions.HttpResponseError: - """ - kwargs.setdefault("merge_span", True) - if not connection_type: - raise ValueError("You must specify an connection type") - # Since there is no notion of default connection at the moment, list all connections in the category - # and return the first one (index 0), unless overridden by the environment variable DEFAULT_CONNECTION_INDEX. - connection_properties_list = self.list(connection_type=connection_type, **kwargs) - if len(connection_properties_list) > 0: - default_connection_index = int(os.getenv("DEFAULT_CONNECTION_INDEX", "0")) - if include_credentials: - return self.get( - connection_name=connection_properties_list[default_connection_index].name, - include_credentials=include_credentials, - **kwargs, - ) - return connection_properties_list[default_connection_index] - raise ResourceNotFoundError(f"No connection of type {connection_type} found") - - @distributed_trace - def get(self, *, connection_name: str, include_credentials: bool = False, **kwargs: Any) -> ConnectionProperties: - """Get the properties of a single connection, given its connection name, with or without - populating authentication credentials. Raises ~azure.core.exceptions.ResourceNotFoundError - exception if a connection with the given name was not found. - - .. note:: This method is not supported for Azure Blob Storage connections. - - :keyword connection_name: Connection Name. Required. - :type connection_name: str - :keyword include_credentials: Whether to populate the connection properties with authentication credentials. - Optional. - :type include_credentials: bool - :return: The connection properties, or `None` if a connection with this name does not exist. - :rtype: ~azure.ai.projects.models.ConnectionProperties - :raises ~azure.core.exceptions.ResourceNotFoundError: - :raises ~azure.core.exceptions.HttpResponseError: - """ - kwargs.setdefault("merge_span", True) - if not connection_name: - raise ValueError("Connection name cannot be empty") - if include_credentials: - connection: GetConnectionResponse = self._get_connection_with_secrets( - connection_name=connection_name, ignored="ignore", **kwargs - ) - if connection.properties.auth_type == AuthenticationType.ENTRA_ID: - return ConnectionProperties(connection=connection, token_credential=self._config.credential) - if connection.properties.auth_type == AuthenticationType.SAS: - from ..models._patch import SASTokenCredential - - cred_prop = cast(InternalConnectionPropertiesSASAuth, connection.properties) - - token_credential = SASTokenCredential( - sas_token=cred_prop.credentials.sas, - credential=self._config.credential, - subscription_id=self._config.subscription_id, - resource_group_name=self._config.resource_group_name, - project_name=self._config.project_name, - connection_name=connection_name, - ) - return ConnectionProperties(connection=connection, token_credential=token_credential) - - return ConnectionProperties(connection=connection) - connection = self._get_connection(connection_name=connection_name, **kwargs) - return ConnectionProperties(connection=connection) - - @distributed_trace - def list( - self, *, connection_type: Optional[ConnectionType] = None, **kwargs: Any - ) -> Sequence[ConnectionProperties]: - """List the properties of all connections, or all connections of a certain connection type. - - :keyword connection_type: The connection type. Optional. If provided, this method lists connections of this - type. If not provided, all connections are listed. - :type connection_type: ~azure.ai.projects.models._models.ConnectionType - :return: A list of connection properties - :rtype: Sequence[~azure.ai.projects.models._models.ConnectionProperties] - :raises ~azure.core.exceptions.HttpResponseError: - """ - kwargs.setdefault("merge_span", True) - connections_list: ListConnectionsResponse = self._list_connections( - include_all=True, category=connection_type, **kwargs - ) - - # Iterate to create the simplified result property - connection_properties_list: List[ConnectionProperties] = [] - for connection in connections_list.value: - connection_properties_list.append(ConnectionProperties(connection=connection)) - - return connection_properties_list - - -# Internal helper functions to enable OpenTelemetry, used by both sync and async clients -def _get_trace_exporter(destination: Union[TextIO, str, None]) -> Any: - if isinstance(destination, str): - # `destination` is the OTLP endpoint - # See: https://opentelemetry-python.readthedocs.io/en/latest/exporter/otlp/otlp.html#usage - try: - from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter # type: ignore - except ModuleNotFoundError as e: - raise ModuleNotFoundError( - "OpenTelemetry OTLP exporter is not installed. " - + "Please install it using 'pip install opentelemetry-exporter-otlp-proto-grpc'" - ) from e - return OTLPSpanExporter(endpoint=destination) - - if isinstance(destination, io.TextIOWrapper): - if destination is sys.stdout: - # See: https://opentelemetry-python.readthedocs.io/en/latest/sdk/trace.export.html#opentelemetry.sdk.trace.export.ConsoleSpanExporter # pylint: disable=line-too-long - try: - from opentelemetry.sdk.trace.export import ConsoleSpanExporter - except ModuleNotFoundError as e: - raise ModuleNotFoundError( - "OpenTelemetry SDK is not installed. Please install it using 'pip install opentelemetry-sdk'" - ) from e - - return ConsoleSpanExporter() - raise ValueError("Only `sys.stdout` is supported at the moment for type `TextIO`") - - return None - - -def _get_log_exporter(destination: Union[TextIO, str, None]) -> Any: - if isinstance(destination, str): - # `destination` is the OTLP endpoint - # See: https://opentelemetry-python.readthedocs.io/en/latest/exporter/otlp/otlp.html#usage - try: - # _logs are considered beta (not internal) in OpenTelemetry Python API/SDK. - # So it's ok to use it for local development, but we'll swallow - # any errors in case of any breaking changes on OTel side. - from opentelemetry.exporter.otlp.proto.grpc._log_exporter import OTLPLogExporter # type: ignore # pylint: disable=import-error,no-name-in-module - except Exception as ex: # pylint: disable=broad-exception-caught - # since OTel logging is still in beta in Python, we're going to swallow any errors - # and just warn about them. - logger.warning("Failed to configure OpenTelemetry logging.", exc_info=ex) - return None - - return OTLPLogExporter(endpoint=destination) - - if isinstance(destination, io.TextIOWrapper): - if destination is sys.stdout: - # See: https://opentelemetry-python.readthedocs.io/en/latest/sdk/trace.export.html#opentelemetry.sdk.trace.export.ConsoleSpanExporter # pylint: disable=line-too-long - try: - from opentelemetry.sdk._logs.export import ConsoleLogExporter - - return ConsoleLogExporter() - except ModuleNotFoundError as ex: - # since OTel logging is still in beta in Python, we're going to swallow any errors - # and just warn about them. - logger.warning("Failed to configure OpenTelemetry logging.", exc_info=ex) - return None - raise ValueError("Only `sys.stdout` is supported at the moment for type `TextIO`") - - return None - - -def _configure_tracing(span_exporter: Any) -> None: - if span_exporter is None: - return - - try: - from opentelemetry import trace - from opentelemetry.sdk.trace import TracerProvider - from opentelemetry.sdk.trace.export import SimpleSpanProcessor - except ModuleNotFoundError as e: - raise ModuleNotFoundError( - "OpenTelemetry SDK is not installed. Please install it using 'pip install opentelemetry-sdk'" - ) from e - - # if tracing was not setup before, we need to create a new TracerProvider - if not isinstance(trace.get_tracer_provider(), TracerProvider): - # If the provider is NoOpTracerProvider, we need to create a new TracerProvider - provider = TracerProvider() - trace.set_tracer_provider(provider) - - # get_tracer_provider returns opentelemetry.trace.TracerProvider - # however, we have opentelemetry.sdk.trace.TracerProvider, which implements - # add_span_processor method, though we need to cast it to fix type checking. - provider = cast(TracerProvider, trace.get_tracer_provider()) - provider.add_span_processor(SimpleSpanProcessor(span_exporter)) - - -def _configure_logging(log_exporter: Any) -> None: - if log_exporter is None: - return - - try: - # _events and _logs are considered beta (not internal) in - # OpenTelemetry Python API/SDK. - # So it's ok to use them for local development, but we'll swallow - # any errors in case of any breaking changes on OTel side. - from opentelemetry import _logs, _events - from opentelemetry.sdk._logs import LoggerProvider # pylint: disable=import-error,no-name-in-module - from opentelemetry.sdk._events import EventLoggerProvider # pylint: disable=import-error,no-name-in-module - from opentelemetry.sdk._logs.export import ( - SimpleLogRecordProcessor, - ) # pylint: disable=import-error,no-name-in-module - - if not isinstance(_logs.get_logger_provider(), LoggerProvider): - logger_provider = LoggerProvider() - _logs.set_logger_provider(logger_provider) - - # get_logger_provider returns opentelemetry._logs.LoggerProvider - # however, we have opentelemetry.sdk._logs.LoggerProvider, which implements - # add_log_record_processor method, though we need to cast it to fix type checking. - logger_provider = cast(LoggerProvider, _logs.get_logger_provider()) - logger_provider.add_log_record_processor(SimpleLogRecordProcessor(log_exporter)) - _events.set_event_logger_provider(EventLoggerProvider(logger_provider)) - except Exception as ex: # pylint: disable=broad-exception-caught - # since OTel logging is still in beta in Python, we're going to swallow any errors - # and just warn about them. - logger.warning("Failed to configure OpenTelemetry logging.", exc_info=ex) - - -def _enable_telemetry(destination: Union[TextIO, str, None], **kwargs) -> None: # pylint: disable=unused-argument - """Enable tracing and logging to console (sys.stdout), or to an OpenTelemetry Protocol (OTLP) endpoint. - - :param destination: `sys.stdout` to print telemetry to console or a string holding the - OpenTelemetry protocol (OTLP) endpoint. - If not provided, this method enables instrumentation, but does not configure OpenTelemetry - SDK to export traces and logs. - :type destination: Union[TextIO, str, None] - """ - span_exporter = _get_trace_exporter(destination) - _configure_tracing(span_exporter) - - log_exporter = _get_log_exporter(destination) - _configure_logging(log_exporter) - - # Silently try to load a set of relevant Instrumentors - try: - from azure.core.settings import settings - - settings.tracing_implementation = "opentelemetry" - except ModuleNotFoundError: - logger.warning( - "Azure SDK tracing plugin is not installed. " - + "Please install it using 'pip install azure-core-tracing-opentelemetry'" - ) - - try: - from azure.ai.inference.tracing import AIInferenceInstrumentor # type: ignore - - inference_instrumentor = AIInferenceInstrumentor() - if not inference_instrumentor.is_instrumented(): - inference_instrumentor.instrument() - except ModuleNotFoundError: - logger.warning( - "Could not call `AIInferenceInstrumentor().instrument()` since `azure-ai-inference` is not installed" - ) - - try: - from azure.ai.projects.telemetry.agents import AIAgentsInstrumentor - - agents_instrumentor = AIAgentsInstrumentor() - if not agents_instrumentor.is_instrumented(): - agents_instrumentor.instrument() - except Exception as exc: # pylint: disable=broad-exception-caught - logger.warning("Could not call `AIAgentsInstrumentor().instrument()`", exc_info=exc) - - try: - from opentelemetry.instrumentation.openai_v2 import OpenAIInstrumentor # type: ignore - - OpenAIInstrumentor().instrument() - except ModuleNotFoundError: - logger.warning( - "Could not call `OpenAIInstrumentor().instrument()` since " - + "`opentelemetry-instrumentation-openai-v2` is not installed" - ) - - try: - from opentelemetry.instrumentation.langchain import LangchainInstrumentor # type: ignore - - LangchainInstrumentor().instrument() - except ModuleNotFoundError: - logger.warning( - "Could not call LangchainInstrumentor().instrument()` since " - + "`opentelemetry-instrumentation-langchain` is not installed" - ) - - -class TelemetryOperations(TelemetryOperationsGenerated): - - _connection_string: Optional[str] = None - - def __init__(self, *args, **kwargs): - self._outer_instance = kwargs.pop("outer_instance") - super().__init__(*args, **kwargs) - - def get_connection_string(self) -> str: - """Get the Application Insights connection string associated with the Project's Application Insights resource. - - :return: The Application Insights connection string if a the resource was enabled for the Project. - :rtype: str - :raises ~azure.core.exceptions.ResourceNotFoundError: An Application Insights resource was not - enabled for this project. - """ - if not self._connection_string: - # Get the AI Foundry project properties, including Application Insights resource URL if exists - get_workspace_response: GetWorkspaceResponse = ( - self._outer_instance.connections._get_workspace() # pylint: disable=protected-access - ) - - if not get_workspace_response.properties.application_insights: - raise ResourceNotFoundError("Application Insights resource was not enabled for this Project.") - - # Make a GET call to the Application Insights resource URL to get the connection string - app_insights_respose: GetAppInsightsResponse = self._get_app_insights( - app_insights_resource_url=get_workspace_response.properties.application_insights - ) - - self._connection_string = app_insights_respose.properties.connection_string - - return self._connection_string - - # TODO: what about `set AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED=true`? - # TODO: This could be a class method. But we don't have a class property AIProjectClient.telemetry - def enable(self, *, destination: Union[TextIO, str, None] = None, **kwargs) -> None: - """Enables telemetry collection with OpenTelemetry for Azure AI clients and popular GenAI libraries. - - Following instrumentations are enabled (when corresponding packages are installed): - - - Azure AI Inference (`azure-ai-inference`) - - Azure AI Projects (`azure-ai-projects`) - - OpenAI (`opentelemetry-instrumentation-openai-v2`) - - Langchain (`opentelemetry-instrumentation-langchain`) - - The recording of prompt and completion messages is disabled by default. To enable it, set the - `AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED` environment variable to `true`. - - When destination is provided, the method configures OpenTelemetry SDK to export traces to - stdout or OTLP (OpenTelemetry protocol) gRPC endpoint. It's recommended for local - development only. For production use, make sure to configure OpenTelemetry SDK directly. - - :keyword destination: Recommended for local testing only. Set it to `sys.stdout` for - tracing to console output, or a string holding the OpenTelemetry protocol (OTLP) - endpoint such as "http://localhost:4317. - If not provided, the method enables instrumentations, but does not configure OpenTelemetry - SDK to export traces. - :paramtype destination: Union[TextIO, str, None] - """ - _enable_telemetry(destination=destination, **kwargs) - - -class AgentsOperations(AgentsOperationsGenerated): - - def __init__(self, *args, **kwargs) -> None: - super().__init__(*args, **kwargs) - self._toolset: Dict[str, _models.ToolSet] = {} - - # pylint: disable=arguments-differ - @overload - def create_agent( # pylint: disable=arguments-differ - self, - *, - model: str, - content_type: str = "application/json", - name: Optional[str] = None, - description: Optional[str] = None, - instructions: Optional[str] = None, - tools: Optional[List[_models.ToolDefinition]] = None, - tool_resources: Optional[_models.ToolResources] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any, - ) -> _models.Agent: - """Creates a new agent. - - :keyword model: The ID of the model to use. Required. - :paramtype model: str - :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. - Default value is "application/json". - :paramtype content_type: str - :keyword name: The name of the new agent. Default value is None. - :paramtype name: str - :keyword description: The description of the new agent. Default value is None. - :paramtype description: str - :keyword instructions: The system instructions for the new agent to use. Default value is None. - :paramtype instructions: str - :keyword tools: The collection of tools to enable for the new agent. Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword tool_resources: A set of resources that are used by the agent'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. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.ToolResources - :keyword 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. Default value is - None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword response_format: The response format of the tool calls used by this agent. Is one of - the following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - # pylint: disable=arguments-differ - @overload - def create_agent( # pylint: disable=arguments-differ - self, - *, - model: str, - content_type: str = "application/json", - name: Optional[str] = None, - description: Optional[str] = None, - instructions: Optional[str] = None, - toolset: Optional[_models.ToolSet] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any, - ) -> _models.Agent: - """Creates a new agent. - - :keyword model: The ID of the model to use. Required. - :paramtype model: str - :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. - Default value is "application/json". - :paramtype content_type: str - :keyword name: The name of the new agent. Default value is None. - :paramtype name: str - :keyword description: The description of the new agent. Default value is None. - :paramtype description: str - :keyword instructions: The system instructions for the new agent to use. Default value is None. - :paramtype instructions: str - :keyword toolset: The Collection of tools and resources (alternative to `tools` and `tool_resources` - and adds automatic execution logic for functions). Default value is None. - :paramtype toolset: ~azure.ai.projects.models.ToolSet - :keyword 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. Default value is - None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword response_format: The response format of the tool calls used by this agent. Is one of - the following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def create_agent(self, body: JSON, *, content_type: str = "application/json", **kwargs: Any) -> _models.Agent: - """Creates a new agent. - - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def create_agent(self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any) -> _models.Agent: - """Creates a new agent. - - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace - def create_agent( - self, - body: Union[JSON, IO[bytes]] = _Unset, - *, - model: str = _Unset, - name: Optional[str] = None, - description: Optional[str] = None, - instructions: Optional[str] = None, - tools: Optional[List[_models.ToolDefinition]] = None, - tool_resources: Optional[_models.ToolResources] = None, - toolset: Optional[_models.ToolSet] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - metadata: Optional[Dict[str, str]] = None, - content_type: str = "application/json", - **kwargs: Any, - ) -> _models.Agent: - """ - Creates a new agent with various configurations, delegating to the generated operations. - - :param body: JSON or IO[bytes]. Required if `model` is not provided. - :type body: Union[JSON, IO[bytes]] - :keyword model: The ID of the model to use. Required if `body` is not provided. - :paramtype model: str - :keyword name: The name of the new agent. - :paramtype name: Optional[str] - :keyword description: A description for the new agent. - :paramtype description: Optional[str] - :keyword instructions: System instructions for the agent. - :paramtype instructions: Optional[str] - :keyword tools: List of tools definitions for the agent. - :paramtype tools: Optional[List[_models.ToolDefinition]] - :keyword tool_resources: Resources used by the agent's tools. - :paramtype tool_resources: Optional[_models.ToolResources] - :keyword toolset: Collection of tools and resources (alternative to `tools` and `tool_resources` - and adds automatic execution logic for functions). - :paramtype toolset: Optional[_models.ToolSet] - :keyword temperature: Sampling temperature for generating agent responses. - :paramtype temperature: Optional[float] - :keyword top_p: Nucleus sampling parameter. - :paramtype top_p: Optional[float] - :keyword response_format: Response format for tool calls. - :paramtype response_format: Optional["_types.AgentsApiResponseFormatOption"] - :keyword metadata: Key/value pairs for storing additional information. - :paramtype metadata: Optional[Dict[str, str]] - :keyword content_type: Content type of the body. - :paramtype content_type: str - :return: An Agent object. - :rtype: _models.Agent - :raises: HttpResponseError for HTTP errors. - """ - - self._validate_tools_and_tool_resources(tools, tool_resources) - - if body is not _Unset: - if isinstance(body, io.IOBase): - return super().create_agent(body=body, content_type=content_type, **kwargs) - return super().create_agent(body=body, **kwargs) - - if toolset is not None: - tools = toolset.definitions - tool_resources = toolset.resources - - new_agent = super().create_agent( - model=model, - name=name, - description=description, - instructions=instructions, - tools=tools, - tool_resources=tool_resources, - temperature=temperature, - top_p=top_p, - response_format=response_format, - metadata=metadata, - **kwargs, - ) - - if toolset is not None: - self._toolset[new_agent.id] = toolset - return new_agent - - # pylint: disable=arguments-differ - @overload - def update_agent( # pylint: disable=arguments-differ - self, - assistant_id: str, - *, - content_type: str = "application/json", - 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.ToolResources] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any, - ) -> _models.Agent: - """Modifies an existing agent. - - :param assistant_id: The ID of the agent to modify. Required. - :type assistant_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 model: The ID of the model to use. Default value is None. - :paramtype model: str - :keyword name: The modified name for the agent to use. Default value is None. - :paramtype name: str - :keyword description: The modified description for the agent to use. Default value is None. - :paramtype description: str - :keyword instructions: The modified system instructions for the new agent to use. Default value - is None. - :paramtype instructions: str - :keyword tools: The modified collection of tools to enable for the agent. Default value is - None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword tool_resources: A set of resources that are used by the agent'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. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.ToolResources - :keyword 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. Default value is - None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword response_format: The response format of the tool calls used by this agent. Is one of - the following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - # pylint: disable=arguments-differ - @overload - def update_agent( # pylint: disable=arguments-differ - self, - assistant_id: str, - *, - content_type: str = "application/json", - model: Optional[str] = None, - name: Optional[str] = None, - description: Optional[str] = None, - instructions: Optional[str] = None, - toolset: Optional[_models.ToolSet] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any, - ) -> _models.Agent: - """Modifies an existing agent. - - :param assistant_id: The ID of the agent to modify. Required. - :type assistant_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 model: The ID of the model to use. Default value is None. - :paramtype model: str - :keyword name: The modified name for the agent to use. Default value is None. - :paramtype name: str - :keyword description: The modified description for the agent to use. Default value is None. - :paramtype description: str - :keyword instructions: The modified system instructions for the new agent to use. Default value - is None. - :paramtype instructions: str - :keyword toolset: The Collection of tools and resources (alternative to `tools` and `tool_resources` - and adds automatic execution logic for functions). Default value is None. - :paramtype toolset: ~azure.ai.projects.models.ToolSet - :keyword 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. Default value is - None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword response_format: The response format of the tool calls used by this agent. Is one of - the following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def update_agent( - self, assistant_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any - ) -> _models.Agent: - """Modifies an existing agent. - - :param assistant_id: The ID of the agent to modify. Required. - :type assistant_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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def update_agent( - self, assistant_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any - ) -> _models.Agent: - """Modifies an existing agent. - - :param assistant_id: The ID of the agent to modify. Required. - :type assistant_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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace - def update_agent( - self, - assistant_id: str, - body: Union[JSON, IO[bytes]] = _Unset, - *, - 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.ToolResources] = None, - toolset: Optional[_models.ToolSet] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - content_type: str = "application/json", - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any, - ) -> _models.Agent: - """Modifies an existing agent. - - :param assistant_id: The ID of the agent to modify. Required. - :type assistant_id: str - :param body: Is either a JSON type or a IO[bytes] type. Required. - :type body: JSON or IO[bytes] - :keyword model: The ID of the model to use. Default value is None. - :paramtype model: str - :keyword name: The modified name for the agent to use. Default value is None. - :paramtype name: str - :keyword description: The modified description for the agent to use. Default value is None. - :paramtype description: str - :keyword instructions: The modified system instructions for the new agent to use. Default value - is None. - :paramtype instructions: str - :keyword tools: The modified collection of tools to enable for the agent. Default value is - None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword tool_resources: A set of resources that are used by the agent'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. Default value is None. - :paramtype tool_resources: ~azure.ai.projects.models.ToolResources - :keyword toolset: The Collection of tools and resources (alternative to `tools` and `tool_resources` - and adds automatic execution logic for functions). Default value is None. - :paramtype toolset: ~azure.ai.projects.models.ToolSet - :keyword 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. Default value is - None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword response_format: The response format of the tool calls used by this agent. Is one of - the following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat - :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: Agent. The Agent is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.Agent - :raises ~azure.core.exceptions.HttpResponseError: - """ - self._validate_tools_and_tool_resources(tools, tool_resources) - - if body is not _Unset: - if isinstance(body, io.IOBase): - return super().update_agent(body=body, content_type=content_type, **kwargs) - return super().update_agent(body=body, **kwargs) - - if toolset is not None: - self._toolset[assistant_id] = toolset - tools = toolset.definitions - tool_resources = toolset.resources - - return super().update_agent( - assistant_id=assistant_id, - model=model, - name=name, - description=description, - instructions=instructions, - tools=tools, - tool_resources=tool_resources, - temperature=temperature, - top_p=top_p, - response_format=response_format, - metadata=metadata, - **kwargs, - ) - - def _validate_tools_and_tool_resources( - self, tools: Optional[List[_models.ToolDefinition]], tool_resources: Optional[_models.ToolResources] - ): - if tool_resources is None: - return - if tools is None: - tools = [] - - if tool_resources.file_search is not None and not any( - isinstance(tool, _models.FileSearchToolDefinition) for tool in tools - ): - raise ValueError( - "Tools must contain a FileSearchToolDefinition when tool_resources.file_search is provided" - ) - if tool_resources.code_interpreter is not None and not any( - isinstance(tool, _models.CodeInterpreterToolDefinition) for tool in tools - ): - raise ValueError( - "Tools must contain a CodeInterpreterToolDefinition when tool_resources.code_interpreter is provided" - ) - - # pylint: disable=arguments-differ - @overload - def create_run( # pylint: disable=arguments-differ - self, - thread_id: str, - *, - assistant_id: str, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - content_type: str = "application/json", - model: Optional[str] = None, - instructions: Optional[str] = None, - additional_instructions: Optional[str] = None, - additional_messages: Optional[List[_models.ThreadMessageOptions]] = None, - tools: Optional[List[_models.ToolDefinition]] = 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.AgentsApiToolChoiceOption"] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - parallel_tool_calls: Optional[bool] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any, - ) -> _models.ThreadRun: - """Creates a new run for an agent thread. - - :param thread_id: Required. - :type thread_id: str - :keyword assistant_id: The ID of the agent that should run the thread. Required. - :paramtype assistant_id: str - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. - Default value is "application/json". - :paramtype content_type: str - :keyword model: The overridden model name that the agent should use to run the thread. Default - value is None. - :paramtype model: str - :keyword instructions: The overridden system instructions that the agent should use to run the - thread. Default value is None. - :paramtype instructions: str - :keyword 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. Default value is None. - :paramtype additional_instructions: str - :keyword additional_messages: Adds additional messages to the thread before creating the run. - Default value is None. - :paramtype additional_messages: list[~azure.ai.projects.models.ThreadMessageOptions] - :keyword tools: The overridden list of enabled tools that the agent should use to run the - thread. Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword 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. Default - value is None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword 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. Default - value is None. - :paramtype max_prompt_tokens: int - :keyword 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. Default value is None. - :paramtype max_completion_tokens: int - :keyword truncation_strategy: The strategy to use for dropping messages as the context windows - moves forward. Default value is None. - :paramtype truncation_strategy: ~azure.ai.projects.models.TruncationObject - :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of - the following types: str, Union[str, "_models.AgentsApiToolChoiceOptionMode"], - AgentsNamedToolChoice Default value is None. - :paramtype tool_choice: str or str or ~azure.ai.projects.models.AgentsApiToolChoiceOptionMode or - ~azure.ai.projects.models.AgentsNamedToolChoice - :keyword response_format: Specifies the format that the model must output. Is one of the - following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat - :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. - Default value is None. - :paramtype parallel_tool_calls: bool - :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.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def create_run( - self, - thread_id: str, - body: JSON, - *, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - content_type: str = "application/json", - **kwargs: Any, - ) -> _models.ThreadRun: - """Creates a new run for an agent thread. - - :param thread_id: Required. - :type thread_id: str - :param body: Required. - :type body: JSON - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :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.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def create_run( - self, - thread_id: str, - body: IO[bytes], - *, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - content_type: str = "application/json", - **kwargs: Any, - ) -> _models.ThreadRun: - """Creates a new run for an agent thread. - - :param thread_id: Required. - :type thread_id: str - :param body: Required. - :type body: IO[bytes] - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :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.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace - def create_run( - self, - thread_id: str, - body: Union[JSON, IO[bytes]] = _Unset, - *, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - assistant_id: str = _Unset, - model: Optional[str] = None, - instructions: Optional[str] = None, - additional_instructions: Optional[str] = None, - additional_messages: Optional[List[_models.ThreadMessageOptions]] = None, - tools: Optional[List[_models.ToolDefinition]] = 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.AgentsApiToolChoiceOption"] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - parallel_tool_calls: Optional[bool] = None, - metadata: Optional[Dict[str, str]] = None, - **kwargs: Any, - ) -> _models.ThreadRun: - """Creates a new run for an agent thread. - - :param thread_id: Required. - :type thread_id: str - :param body: Is either a JSON type or a IO[bytes] type. Required. - :type body: JSON or IO[bytes] - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :keyword assistant_id: The ID of the agent that should run the thread. Required. - :paramtype assistant_id: str - :keyword model: The overridden model name that the agent should use to run the thread. Default - value is None. - :paramtype model: str - :keyword instructions: The overridden system instructions that the agent should use to run the - thread. Default value is None. - :paramtype instructions: str - :keyword 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. Default value is None. - :paramtype additional_instructions: str - :keyword additional_messages: Adds additional messages to the thread before creating the run. - Default value is None. - :paramtype additional_messages: list[~azure.ai.projects.models.ThreadMessageOptions] - :keyword tools: The overridden list of enabled tools that the agent should use to run the - thread. Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword 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. Default - value is None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword 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. Default - value is None. - :paramtype max_prompt_tokens: int - :keyword 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. Default value is None. - :paramtype max_completion_tokens: int - :keyword truncation_strategy: The strategy to use for dropping messages as the context windows - moves forward. Default value is None. - :paramtype truncation_strategy: ~azure.ai.projects.models.TruncationObject - :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of - the following types: str, Union[str, "_models.AgentsApiToolChoiceOptionMode"], - AgentsNamedToolChoice Default value is None. - :paramtype tool_choice: str or str or ~azure.ai.projects.models.AgentsApiToolChoiceOptionMode or - ~azure.ai.projects.models.AgentsNamedToolChoice - :keyword response_format: Specifies the format that the model must output. Is one of the - following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat - :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. - Default value is None. - :paramtype parallel_tool_calls: bool - :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.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - if isinstance(body, dict): # Handle overload with JSON body. - content_type = kwargs.get("content_type", "application/json") - response = super().create_run(thread_id, body, include=include, content_type=content_type, **kwargs) - - elif assistant_id is not _Unset: # Handle overload with keyword arguments. - response = super().create_run( - thread_id, - include=include, - assistant_id=assistant_id, - model=model, - instructions=instructions, - additional_instructions=additional_instructions, - additional_messages=additional_messages, - tools=tools, - stream_parameter=False, - stream=False, - temperature=temperature, - top_p=top_p, - max_prompt_tokens=max_prompt_tokens, - max_completion_tokens=max_completion_tokens, - truncation_strategy=truncation_strategy, - tool_choice=tool_choice, - response_format=response_format, - parallel_tool_calls=parallel_tool_calls, - metadata=metadata, - **kwargs, - ) - - elif isinstance(body, io.IOBase): # Handle overload with binary body. - content_type = kwargs.get("content_type", "application/json") - response = super().create_run(thread_id, body, include=include, content_type=content_type, **kwargs) - - else: - raise ValueError("Invalid combination of arguments provided.") - - return response - - @distributed_trace - def create_and_process_run( - self, - thread_id: str, - *, - assistant_id: str, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - model: Optional[str] = None, - instructions: Optional[str] = None, - additional_instructions: Optional[str] = None, - additional_messages: Optional[List[_models.ThreadMessageOptions]] = None, - toolset: Optional[_models.ToolSet] = 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.AgentsApiToolChoiceOption"] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - parallel_tool_calls: Optional[bool] = None, - metadata: Optional[Dict[str, str]] = None, - sleep_interval: int = 1, - **kwargs: Any, - ) -> _models.ThreadRun: - """Creates a new run for an agent thread and processes the run. - - :param thread_id: Required. - :type thread_id: str - :keyword assistant_id: The ID of the agent that should run the thread. Required. - :paramtype assistant_id: str - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :keyword model: The overridden model name that the agent should use to run the thread. - Default value is None. - :paramtype model: str - :keyword instructions: The overridden system instructions that the agent should use to run - the thread. Default value is None. - :paramtype instructions: str - :keyword 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. Default value is None. - :paramtype additional_instructions: str - :keyword additional_messages: Adds additional messages to the thread before creating the run. - Default value is None. - :paramtype additional_messages: list[~azure.ai.projects.models.ThreadMessageOptions] - :keyword toolset: The Collection of tools and resources (alternative to `tools` and - `tool_resources`). Default value is None. - :paramtype toolset: ~azure.ai.projects.models.ToolSet - :keyword 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. Default - value is None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword 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. Default - value is None. - :paramtype max_prompt_tokens: int - :keyword 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. Default value is None. - :paramtype max_completion_tokens: int - :keyword truncation_strategy: The strategy to use for dropping messages as the context windows - moves forward. Default value is None. - :paramtype truncation_strategy: ~azure.ai.projects.models.TruncationObject - :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of - the following types: str, Union[str, "_models.AgentsApiToolChoiceOptionMode"], - AgentsNamedToolChoice Default value is None. - :paramtype tool_choice: str or str or - ~azure.ai.projects.models.AgentsApiToolChoiceOptionMode or - ~azure.ai.projects.models.AgentsNamedToolChoice - :keyword response_format: Specifies the format that the model must output. Is one of the - following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat Default value is None. - :paramtype response_format: str or str or - ~azure.ai.projects.models.AgentsApiResponseFormatMode or - ~azure.ai.projects.models.AgentsApiResponseFormat - :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. - Default value is None. - :paramtype parallel_tool_calls: bool - :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] - :keyword sleep_interval: The time in seconds to wait between polling the service for run status. - Default value is 1. - :paramtype sleep_interval: int - :return: AgentRunStream. AgentRunStream is compatible with Iterable and supports streaming. - :rtype: ~azure.ai.projects.models.AgentRunStream - :raises ~azure.core.exceptions.HttpResponseError: - """ - # Create and initiate the run with additional parameters - run = self.create_run( - thread_id=thread_id, - include=include, - assistant_id=assistant_id, - model=model, - instructions=instructions, - additional_instructions=additional_instructions, - additional_messages=additional_messages, - tools=toolset.definitions if toolset else None, - temperature=temperature, - top_p=top_p, - max_prompt_tokens=max_prompt_tokens, - max_completion_tokens=max_completion_tokens, - truncation_strategy=truncation_strategy, - tool_choice=tool_choice, - response_format=response_format, - parallel_tool_calls=parallel_tool_calls, - metadata=metadata, - **kwargs, - ) - - # Monitor and process the run status - while run.status in [ - RunStatus.QUEUED, - RunStatus.IN_PROGRESS, - RunStatus.REQUIRES_ACTION, - ]: - time.sleep(sleep_interval) - run = self.get_run(thread_id=thread_id, run_id=run.id) - - if run.status == RunStatus.REQUIRES_ACTION and isinstance( - run.required_action, _models.SubmitToolOutputsAction - ): - tool_calls = run.required_action.submit_tool_outputs.tool_calls - if not tool_calls: - logging.warning("No tool calls provided - cancelling run") - self.cancel_run(thread_id=thread_id, run_id=run.id) - break - # We need tool set only if we are executing local function. In case if - # the tool is azure_function we just need to wait when it will be finished. - if any(tool_call.type == "function" for tool_call in tool_calls): - toolset = toolset or self._toolset.get(run.assistant_id) - if toolset is not None: - tool_outputs = toolset.execute_tool_calls(tool_calls) - else: - raise ValueError("Toolset is not available in the client.") - - logging.info("Tool outputs: %s", tool_outputs) - if tool_outputs: - self.submit_tool_outputs_to_run(thread_id=thread_id, run_id=run.id, tool_outputs=tool_outputs) - - logging.info("Current run status: %s", run.status) - - return run - - @overload - def create_stream( - self, - thread_id: str, - *, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - assistant_id: str, - content_type: str = "application/json", - model: Optional[str] = None, - instructions: Optional[str] = None, - additional_instructions: Optional[str] = None, - additional_messages: Optional[List[_models.ThreadMessageOptions]] = None, - tools: Optional[List[_models.ToolDefinition]] = 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.AgentsApiToolChoiceOption"] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - parallel_tool_calls: Optional[bool] = None, - metadata: Optional[Dict[str, str]] = None, - event_handler: None = None, - **kwargs: Any, - ) -> _models.AgentRunStream[_models.AgentEventHandler]: - """Creates a new stream for an agent thread. - - :param thread_id: Required. - :type thread_id: str - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :keyword assistant_id: The ID of the agent that should run the thread. Required. - :paramtype assistant_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 model: The overridden model name that the agent should use to run the thread. Default - value is None. - :paramtype model: str - :keyword instructions: The overridden system instructions that the agent should use to run the - thread. Default value is None. - :paramtype instructions: str - :keyword 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. Default value is None. - :paramtype additional_instructions: str - :keyword additional_messages: Adds additional messages to the thread before creating the run. - Default value is None. - :paramtype additional_messages: list[~azure.ai.projects.models.ThreadMessage] - :keyword tools: The overridden list of enabled tools that the agent should use to run the - thread. Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword 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. Default - value is None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword 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. Default - value is None. - :paramtype max_prompt_tokens: int - :keyword 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. Default value is None. - :paramtype max_completion_tokens: int - :keyword truncation_strategy: The strategy to use for dropping messages as the context windows - moves forward. Default value is None. - :paramtype truncation_strategy: ~azure.ai.projects.models.TruncationObject - :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of - the following types: str, Union[str, "_models.AgentsApiToolChoiceOptionMode"], - AgentsNamedToolChoice Default value is None. - :paramtype tool_choice: str or str or ~azure.ai.projects.models.AgentsApiToolChoiceOptionMode or - ~azure.ai.projects.models.AgentsNamedToolChoice - :keyword response_format: Specifies the format that the model must output. Is one of the - following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat - :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. - Default value is None. - :paramtype parallel_tool_calls: bool - :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] - :keyword event_handler: None - :paramtype event_handler: None. _models.AgentEventHandler will be applied as default. - :return: AgentRunStream. AgentRunStream is compatible with Iterable and supports streaming. - :rtype: ~azure.ai.projects.models.AgentRunStream - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def create_stream( - self, - thread_id: str, - *, - assistant_id: str, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - content_type: str = "application/json", - model: Optional[str] = None, - instructions: Optional[str] = None, - additional_instructions: Optional[str] = None, - additional_messages: Optional[List[_models.ThreadMessageOptions]] = None, - tools: Optional[List[_models.ToolDefinition]] = 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.AgentsApiToolChoiceOption"] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - parallel_tool_calls: Optional[bool] = None, - metadata: Optional[Dict[str, str]] = None, - event_handler: _models.BaseAgentEventHandlerT, - **kwargs: Any, - ) -> _models.AgentRunStream[_models.BaseAgentEventHandlerT]: - """Creates a new stream for an agent thread. - - :param thread_id: Required. - :type thread_id: str - :keyword assistant_id: The ID of the agent that should run the thread. Required. - :paramtype assistant_id: str - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. - Default value is "application/json". - :paramtype content_type: str - :keyword model: The overridden model name that the agent should use to run the thread. Default - value is None. - :paramtype model: str - :keyword instructions: The overridden system instructions that the agent should use to run the - thread. Default value is None. - :paramtype instructions: str - :keyword 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. Default value is None. - :paramtype additional_instructions: str - :keyword additional_messages: Adds additional messages to the thread before creating the run. - Default value is None. - :paramtype additional_messages: list[~azure.ai.projects.models.ThreadMessage] - :keyword tools: The overridden list of enabled tools that the agent should use to run the - thread. Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword 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. Default - value is None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword 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. Default - value is None. - :paramtype max_prompt_tokens: int - :keyword 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. Default value is None. - :paramtype max_completion_tokens: int - :keyword truncation_strategy: The strategy to use for dropping messages as the context windows - moves forward. Default value is None. - :paramtype truncation_strategy: ~azure.ai.projects.models.TruncationObject - :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of - the following types: str, Union[str, "_models.AgentsApiToolChoiceOptionMode"], - AgentsNamedToolChoice Default value is None. - :paramtype tool_choice: str or str or ~azure.ai.projects.models.AgentsApiToolChoiceOptionMode or - ~azure.ai.projects.models.AgentsNamedToolChoice - :keyword response_format: Specifies the format that the model must output. Is one of the - following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat - :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. - Default value is None. - :paramtype parallel_tool_calls: bool - :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] - :keyword event_handler: The event handler to use for processing events during the run. Default - value is None. - :paramtype event_handler: ~azure.ai.projects.models.AgentEventHandler - :return: AgentRunStream. AgentRunStream is compatible with Iterable and supports streaming. - :rtype: ~azure.ai.projects.models.AgentRunStream - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def create_stream( - self, - thread_id: str, - body: Union[JSON, IO[bytes]], - *, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - event_handler: None = None, - content_type: str = "application/json", - **kwargs: Any, - ) -> _models.AgentRunStream[_models.AgentEventHandler]: - """Creates a new run for an agent thread. - - Terminating when the Run enters a terminal state with a ``data: [DONE]`` message. - - :param thread_id: Required. - :type thread_id: str - :param body: Required. - :type body: IO[bytes] - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :keyword event_handler: None - :paramtype event_handler: None. _models.AgentEventHandler will be applied as default. - :keyword content_type: Body Parameter content-type. Content type parameter for binary body. - Default value is "application/json". - :paramtype content_type: str - :return: AgentRunStream. AgentRunStream is compatible with Iterable and supports streaming. - :rtype: ~azure.ai.projects.models.AgentRunStream - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def create_stream( - self, - thread_id: str, - body: Union[JSON, IO[bytes]], - *, - event_handler: _models.BaseAgentEventHandlerT, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - content_type: str = "application/json", - **kwargs: Any, - ) -> _models.AgentRunStream[_models.BaseAgentEventHandlerT]: - """Creates a new run for an agent thread. - - Terminating when the Run enters a terminal state with a ``data: [DONE]`` message. - - :param thread_id: Required. - :type thread_id: str - :param body: Required. - :type body: IO[bytes] - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :keyword event_handler: The event handler to use for processing events during the run. Default - value is None. - :paramtype event_handler: ~azure.ai.projects.models.AgentEventHandler - :keyword content_type: Body Parameter content-type. Content type parameter for binary body. - Default value is "application/json". - :paramtype content_type: str - :return: AgentRunStream. AgentRunStream is compatible with Iterable and supports streaming. - :rtype: ~azure.ai.projects.models.AgentRunStream - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace - def create_stream( # pyright: ignore[reportInconsistentOverload] - self, - thread_id: str, - body: Union[JSON, IO[bytes]] = _Unset, - *, - include: Optional[List[Union[str, _models.RunAdditionalFieldList]]] = None, - assistant_id: str = _Unset, - model: Optional[str] = None, - instructions: Optional[str] = None, - additional_instructions: Optional[str] = None, - additional_messages: Optional[List[_models.ThreadMessageOptions]] = None, - tools: Optional[List[_models.ToolDefinition]] = 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.AgentsApiToolChoiceOption"] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - parallel_tool_calls: Optional[bool] = None, - metadata: Optional[Dict[str, str]] = None, - event_handler: Optional[_models.BaseAgentEventHandlerT] = None, - **kwargs: Any, - ) -> _models.AgentRunStream[_models.BaseAgentEventHandlerT]: - """Creates a new run for an agent thread. - - Terminating when the Run enters a terminal state with a ``data: [DONE]`` message. - - :param thread_id: Required. - :type thread_id: str - :param body: Is either a JSON type or a IO[bytes] type. Required. - :type body: JSON or IO[bytes] - :keyword include: A list of additional fields to include in the response. - Currently the only supported value is - ``step_details.tool_calls[*].file_search.results[*].content`` to fetch the file search result - content. Default value is None. - :paramtype include: list[str or ~azure.ai.projects.models.RunAdditionalFieldList] - :keyword assistant_id: The ID of the agent that should run the thread. Required. - :paramtype assistant_id: str - :keyword model: The overridden model name that the agent should use to run the thread. Default - value is None. - :paramtype model: str - :keyword instructions: The overridden system instructions that the agent should use to run the - thread. Default value is None. - :paramtype instructions: str - :keyword 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. Default value is None. - :paramtype additional_instructions: str - :keyword additional_messages: Adds additional messages to the thread before creating the run. - Default value is None. - :paramtype additional_messages: list[~azure.ai.projects.models.ThreadMessage] - :keyword tools: The overridden list of enabled tools that the agent should use to run the - thread. Default value is None. - :paramtype tools: list[~azure.ai.projects.models.ToolDefinition] - :keyword 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. Default - value is None. - :paramtype temperature: float - :keyword 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. Default value is None. - :paramtype top_p: float - :keyword 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. Default - value is None. - :paramtype max_prompt_tokens: int - :keyword 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. Default value is None. - :paramtype max_completion_tokens: int - :keyword truncation_strategy: The strategy to use for dropping messages as the context windows - moves forward. Default value is None. - :paramtype truncation_strategy: ~azure.ai.projects.models.TruncationObject - :keyword tool_choice: Controls whether or not and which tool is called by the model. Is one of - the following types: str, Union[str, "_models.AgentsApiToolChoiceOptionMode"], - AgentsNamedToolChoice Default value is None. - :paramtype tool_choice: str or str or ~azure.ai.projects.models.AgentsApiToolChoiceOptionMode or - ~azure.ai.projects.models.AgentsNamedToolChoice - :keyword response_format: Specifies the format that the model must output. Is one of the - following types: str, Union[str, "_models.AgentsApiResponseFormatMode"], - AgentsApiResponseFormat Default value is None. - :paramtype response_format: str or str or ~azure.ai.projects.models.AgentsApiResponseFormatMode - or ~azure.ai.projects.models.AgentsApiResponseFormat - :keyword parallel_tool_calls: If ``true`` functions will run in parallel during tool use. - Default value is None. - :paramtype parallel_tool_calls: bool - :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] - :keyword event_handler: The event handler to use for processing events during the run. Default - value is None. - :paramtype event_handler: ~azure.ai.projects.models.AgentEventHandler - :return: AgentRunStream. AgentRunStream is compatible with Iterable and supports streaming. - :rtype: ~azure.ai.projects.models.AgentRunStream - :raises ~azure.core.exceptions.HttpResponseError: - """ - - if isinstance(body, dict): # Handle overload with JSON body. - content_type = kwargs.get("content_type", "application/json") - response = super().create_run(thread_id, body, include=include, content_type=content_type, **kwargs) - - elif assistant_id is not _Unset: # Handle overload with keyword arguments. - response = super().create_run( - thread_id, - include=include, - assistant_id=assistant_id, - model=model, - instructions=instructions, - additional_instructions=additional_instructions, - additional_messages=additional_messages, - tools=tools, - stream_parameter=True, - stream=True, - temperature=temperature, - top_p=top_p, - max_prompt_tokens=max_prompt_tokens, - max_completion_tokens=max_completion_tokens, - truncation_strategy=truncation_strategy, - tool_choice=tool_choice, - response_format=response_format, - parallel_tool_calls=parallel_tool_calls, - metadata=metadata, - **kwargs, - ) - - elif isinstance(body, io.IOBase): # Handle overload with binary body. - content_type = kwargs.get("content_type", "application/json") - response = super().create_run(thread_id, body, include=include, content_type=content_type, **kwargs) - - else: - raise ValueError("Invalid combination of arguments provided.") - - response_iterator: Iterator[bytes] = cast(Iterator[bytes], response) - - if not event_handler: - event_handler = cast(_models.BaseAgentEventHandlerT, _models.AgentEventHandler()) - return _models.AgentRunStream(response_iterator, self._handle_submit_tool_outputs, event_handler) - - # pylint: disable=arguments-differ - @overload - def submit_tool_outputs_to_run( # pylint: disable=arguments-differ - self, - thread_id: str, - run_id: str, - *, - tool_outputs: List[_models.ToolOutput], - content_type: str = "application/json", - event_handler: Optional[_models.AgentEventHandler] = 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: Required. - :type thread_id: str - :param run_id: Required. - :type run_id: str - :keyword tool_outputs: Required. - :paramtype tool_outputs: list[~azure.ai.projects.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 event_handler: The event handler to use for processing events during the run. Default - value is None. - :paramtype event_handler: ~azure.ai.projects.models.AgentEventHandler - :return: ThreadRun. The ThreadRun is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @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: Required. - :type thread_id: str - :param run_id: 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.projects.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: Required. - :type thread_id: str - :param run_id: 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.projects.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, - **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: Required. - :type thread_id: str - :param run_id: 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: Required. - :paramtype tool_outputs: list[~azure.ai.projects.models.ToolOutput] - :return: ThreadRun. The ThreadRun is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.ThreadRun - :raises ~azure.core.exceptions.HttpResponseError: - """ - - if isinstance(body, dict): - content_type = kwargs.get("content_type", "application/json") - response = super().submit_tool_outputs_to_run(thread_id, run_id, body, content_type=content_type, **kwargs) - - elif tool_outputs is not _Unset: - response = super().submit_tool_outputs_to_run( - thread_id, - run_id, - tool_outputs=tool_outputs, - stream_parameter=False, - stream=False, - **kwargs, - ) - - elif isinstance(body, io.IOBase): - content_type = kwargs.get("content_type", "application/json") - response = super().submit_tool_outputs_to_run(thread_id, run_id, body, content_type=content_type, **kwargs) - - else: - raise ValueError("Invalid combination of arguments provided.") - - return response - - @overload - def submit_tool_outputs_to_stream( - self, - thread_id: str, - run_id: str, - body: Union[JSON, IO[bytes]], - *, - event_handler: _models.BaseAgentEventHandler, - content_type: str = "application/json", - **kwargs: Any, - ) -> None: - """Submits outputs from tools as requested by tool calls in a stream. Runs that need submitted tool - outputs will have a status of 'requires_action' with a required_action.type of - 'submit_tool_outputs'. terminating when the Run enters a terminal state with a ``data: [DONE]`` message. - - :param thread_id: Required. - :type thread_id: str - :param run_id: 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 event_handler: The event handler to use for processing events during the run. - :paramtype event_handler: ~azure.ai.projects.models.BaseAgentEventHandler - :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. - Default value is "application/json". - :paramtype content_type: str - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def submit_tool_outputs_to_stream( - self, - thread_id: str, - run_id: str, - *, - tool_outputs: List[_models.ToolOutput], - content_type: str = "application/json", - event_handler: _models.BaseAgentEventHandler, - **kwargs: Any, - ) -> None: - """Submits outputs from tools as requested by tool calls in a stream. Runs that need submitted tool - outputs will have a status of 'requires_action' with a required_action.type of - 'submit_tool_outputs'. terminating when the Run enters a terminal state with a ``data: [DONE]`` message. - - :param thread_id: Required. - :type thread_id: str - :param run_id: Required. - :type run_id: str - :keyword tool_outputs: Required. - :paramtype tool_outputs: list[~azure.ai.projects.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 event_handler: The event handler to use for processing events during the run. - :paramtype event_handler: ~azure.ai.projects.models.BaseAgentEventHandler - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace - def submit_tool_outputs_to_stream( # pyright: ignore[reportInconsistentOverload] - self, - thread_id: str, - run_id: str, - body: Union[JSON, IO[bytes]] = _Unset, - *, - tool_outputs: List[_models.ToolOutput] = _Unset, - event_handler: _models.BaseAgentEventHandler, - **kwargs: Any, - ) -> None: - """Submits outputs from tools as requested by tool calls in a stream. Runs that need submitted tool - outputs will have a status of 'requires_action' with a required_action.type of - 'submit_tool_outputs'. terminating when the Run enters a terminal state with a ``data: [DONE]`` message. - - :param thread_id: Required. - :type thread_id: str - :param run_id: 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: Required. - :paramtype tool_outputs: list[~azure.ai.projects.models.ToolOutput] - :keyword event_handler: The event handler to use for processing events during the run. - :paramtype event_handler: ~azure.ai.projects.models.BaseAgentEventHandler - :raises ~azure.core.exceptions.HttpResponseError: - """ - - if isinstance(body, dict): - content_type = kwargs.get("content_type", "application/json") - response = super().submit_tool_outputs_to_run(thread_id, run_id, body, content_type=content_type, **kwargs) - - elif tool_outputs is not _Unset: - response = super().submit_tool_outputs_to_run( - thread_id, run_id, tool_outputs=tool_outputs, stream_parameter=True, stream=True, **kwargs - ) - - elif isinstance(body, io.IOBase): - content_type = kwargs.get("content_type", "application/json") - response = super().submit_tool_outputs_to_run(thread_id, run_id, body, content_type=content_type, **kwargs) - - else: - raise ValueError("Invalid combination of arguments provided.") - - # Cast the response to Iterator[bytes] for type correctness - response_iterator: Iterator[bytes] = cast(Iterator[bytes], response) - - event_handler.initialize(response_iterator, self._handle_submit_tool_outputs) - - def _handle_submit_tool_outputs(self, run: _models.ThreadRun, event_handler: _models.BaseAgentEventHandler) -> None: - if isinstance(run.required_action, _models.SubmitToolOutputsAction): - tool_calls = run.required_action.submit_tool_outputs.tool_calls - if not tool_calls: - logger.debug("No tool calls to execute.") - return - - # We need tool set only if we are executing local function. In case if - # the tool is azure_function we just need to wait when it will be finished. - if any(tool_call.type == "function" for tool_call in tool_calls): - toolset = self._toolset.get(run.assistant_id) - if toolset: - tool_outputs = toolset.execute_tool_calls(tool_calls) - else: - logger.debug("Toolset is not available in the client.") - return - - logger.info("Tool outputs: %s", tool_outputs) - if tool_outputs: - self.submit_tool_outputs_to_stream( - thread_id=run.thread_id, - run_id=run.id, - tool_outputs=tool_outputs, - event_handler=event_handler, - ) - - # pylint: disable=arguments-differ - @overload - def upload_file( # pylint: disable=arguments-differ - self, *, file_path: str, purpose: Union[str, _models.FilePurpose], **kwargs: Any - ) -> _models.OpenAIFile: - """Uploads a file for use by other operations. - - :keyword file_path: Required. - :type file_path: str - :keyword purpose: Known values are: "fine-tune", "fine-tune-results", "assistants", - "assistants_output", "batch", "batch_output", and "vision". Required. - :paramtype purpose: str or ~azure.ai.projects.models.FilePurpose - :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.OpenAIFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - # pylint: disable=arguments-differ - @overload - def upload_file( # pylint: disable=arguments-differ - 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: Required. - :paramtype file: ~azure.ai.projects._vendor.FileType - :keyword purpose: Known values are: "fine-tune", "fine-tune-results", "assistants", - "assistants_output", "batch", "batch_output", and "vision". Required. - :paramtype purpose: str or ~azure.ai.projects.models.FilePurpose - :keyword filename: Default value is None. - :paramtype filename: str - :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.OpenAIFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @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.projects.models.OpenAIFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace - def upload_file( - self, - body: Optional[JSON] = None, - *, - file: Optional[FileType] = None, - file_path: Optional[str] = None, - purpose: Union[str, _models.FilePurpose, None] = None, - filename: Optional[str] = None, - **kwargs: Any, - ) -> _models.OpenAIFile: - """ - Uploads a file for use by other operations, delegating to the generated operations. - - :param body: JSON. Required if `file` and `purpose` are not provided. - :type body: Optional[JSON] - :keyword file: File content. Required if `body` and `purpose` are not provided. - :paramtype file: Optional[FileType] - :keyword file_path: Path to the file. Required if `body` and `purpose` are not provided. - :paramtype file_path: Optional[str] - :keyword purpose: Known values are: "fine-tune", "fine-tune-results", "assistants", - :paramtype purpose: Union[str, _models.FilePurpose, None] - "assistants_output", "batch", "batch_output", and "vision". Required if `body` and `file` are not provided. - :keyword filename: The name of the file. - :paramtype filename: Optional[str] - :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping - :rtype: _models.OpenAIFile - :raises FileNotFoundError: If the file_path is invalid. - :raises IOError: If there are issues with reading the file. - :raises: HttpResponseError for HTTP errors. - """ - if body is not None: - return super().upload_file(body=body, **kwargs) - - if isinstance(purpose, FilePurpose): - purpose = purpose.value - - if file is not None and purpose is not None: - return super().upload_file(file=file, purpose=purpose, filename=filename, **kwargs) - - if file_path is not None and purpose is not None: - if not os.path.isfile(file_path): - raise FileNotFoundError(f"The file path provided does not exist: {file_path}") - - try: - with open(file_path, "rb") as f: - content = f.read() - - # Determine filename and create correct FileType - base_filename = filename or os.path.basename(file_path) - file_content: FileType = (base_filename, content) - - return super().upload_file(file=file_content, purpose=purpose, **kwargs) - except IOError as e: - raise IOError(f"Unable to read file: {file_path}") from e - - raise ValueError("Invalid parameters for upload_file. Please provide the necessary arguments.") - - @overload - def upload_file_and_poll(self, body: JSON, *, sleep_interval: float = 1, **kwargs: Any) -> _models.OpenAIFile: - """Uploads a file for use by other operations. - - :param body: Required. - :type body: JSON - :keyword sleep_interval: Time to wait before polling for the status of the uploaded file. Default value - is 1. - :paramtype sleep_interval: float - :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.OpenAIFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def upload_file_and_poll( - self, - *, - file: FileType, - purpose: Union[str, _models.FilePurpose], - filename: Optional[str] = None, - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.OpenAIFile: - """Uploads a file for use by other operations. - - :keyword file: Required. - :paramtype file: ~azure.ai.projects._vendor.FileType - :keyword purpose: Known values are: "fine-tune", "fine-tune-results", "assistants", - "assistants_output", "batch", "batch_output", and "vision". Required. - :paramtype purpose: str or ~azure.ai.projects.models.FilePurpose - :keyword filename: Default value is None. - :paramtype filename: str - :keyword sleep_interval: Time to wait before polling for the status of the uploaded file. Default value - is 1. - :paramtype sleep_interval: float - :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.OpenAIFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def upload_file_and_poll( - self, *, file_path: str, purpose: Union[str, _models.FilePurpose], sleep_interval: float = 1, **kwargs: Any - ) -> _models.OpenAIFile: - """Uploads a file for use by other operations. - - :keyword file_path: Required. - :type file_path: str - :keyword purpose: Known values are: "fine-tune", "fine-tune-results", "assistants", - "assistants_output", "batch", "batch_output", and "vision". Required. - :paramtype purpose: str or ~azure.ai.projects.models.FilePurpose - :keyword sleep_interval: Time to wait before polling for the status of the uploaded file. Default value - is 1. - :paramtype sleep_interval: float - :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.OpenAIFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace - def upload_file_and_poll( - self, - body: Optional[JSON] = None, - *, - file: Optional[FileType] = None, - file_path: Optional[str] = None, - purpose: Union[str, _models.FilePurpose, None] = None, - filename: Optional[str] = None, - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.OpenAIFile: - """ - Uploads a file for use by other operations, delegating to the generated operations. - - :param body: JSON. Required if `file` and `purpose` are not provided. - :type body: Optional[JSON] - :keyword file: File content. Required if `body` and `purpose` are not provided. - :paramtype file: Optional[FileType] - :keyword file_path: Path to the file. Required if `body` and `purpose` are not provided. - :paramtype file_path: Optional[str] - :keyword purpose: Known values are: "fine-tune", "fine-tune-results", "assistants", - "assistants_output", "batch", "batch_output", and "vision". Required if `body` and `file` are not provided. - :paramtype purpose: Union[str, _models.FilePurpose, None] - :keyword filename: The name of the file. - :paramtype filename: Optional[str] - :keyword sleep_interval: Time to wait before polling for the status of the uploaded file. Default value - is 1. - :paramtype sleep_interval: float - :return: OpenAIFile. The OpenAIFile is compatible with MutableMapping - :rtype: _models.OpenAIFile - :raises FileNotFoundError: If the file_path is invalid. - :raises IOError: If there are issues with reading the file. - :raises: HttpResponseError for HTTP errors. - """ - if body is not None: - uploaded_file = self.upload_file(body=body, **kwargs) - elif file is not None and purpose is not None: - uploaded_file = self.upload_file(file=file, purpose=purpose, filename=filename, **kwargs) - elif file_path is not None and purpose is not None: - uploaded_file = self.upload_file(file_path=file_path, purpose=purpose, **kwargs) - else: - raise ValueError( - "Invalid parameters for upload_file_and_poll. Please provide either 'body', " - "or both 'file' and 'purpose', or both 'file_path' and 'purpose'." - ) - - while uploaded_file.status in ["uploaded", "pending", "running"]: - time.sleep(sleep_interval) - uploaded_file = self.get_file(uploaded_file.id) - - return uploaded_file - - @overload - def create_vector_store_and_poll( - self, body: JSON, *, content_type: str = "application/json", sleep_interval: float = 1, **kwargs: Any - ) -> _models.VectorStore: - """Creates a vector store and poll. - - :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 - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStore. The VectorStore is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStore - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def create_vector_store_and_poll( - self, - *, - content_type: str = "application/json", - file_ids: Optional[List[str]] = None, - name: Optional[str] = None, - data_sources: Optional[List[_models.VectorStoreDataSource]] = None, - expires_after: Optional[_models.VectorStoreExpirationPolicy] = None, - chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, - metadata: Optional[Dict[str, str]] = None, - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.VectorStore: - """Creates a vector store and poll. - - :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. - Default value is "application/json". - :paramtype content_type: 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. Default value is None. - :paramtype file_ids: list[str] - :keyword name: The name of the vector store. Default value is None. - :paramtype name: str - :keyword data_sources: List of Azure assets. Default value is None. - :paramtype data_sources: list[~azure.ai.projects.models.VectorStoreDataSource] - :keyword expires_after: Details on when this vector store expires. Default value is None. - :paramtype expires_after: ~azure.ai.projects.models.VectorStoreExpirationPolicy - :keyword 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. Default value is None. - :paramtype chunking_strategy: ~azure.ai.projects.models.VectorStoreChunkingStrategyRequest - :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] - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStore. The VectorStore is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStore - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def create_vector_store_and_poll( - self, body: IO[bytes], *, content_type: str = "application/json", sleep_interval: float = 1, **kwargs: Any - ) -> _models.VectorStore: - """Creates a vector store and poll. - - :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 - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStore. The VectorStore is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStore - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace - def create_vector_store_and_poll( - self, - body: Union[JSON, IO[bytes]] = _Unset, - *, - content_type: str = "application/json", - file_ids: Optional[List[str]] = None, - name: Optional[str] = None, - data_sources: Optional[List[_models.VectorStoreDataSource]] = None, - expires_after: Optional[_models.VectorStoreExpirationPolicy] = None, - chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, - metadata: Optional[Dict[str, str]] = None, - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.VectorStore: - """Creates a vector store and poll. - - :param body: Is either a JSON type or a IO[bytes] type. Required. - :type body: JSON or IO[bytes] - :keyword content_type: Body Parameter content-type. Content type parameter for binary body. - Default value is "application/json". - :paramtype content_type: 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. Default value is None. - :paramtype file_ids: list[str] - :keyword name: The name of the vector store. Default value is None. - :paramtype name: str - :keyword data_sources: List of Azure assets. Default value is None. - :paramtype data_sources: list[~azure.ai.projects.models.VectorStoreDataSource] - :keyword expires_after: Details on when this vector store expires. Default value is None. - :paramtype expires_after: ~azure.ai.projects.models.VectorStoreExpirationPolicy - :keyword 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. Default value is None. - :paramtype chunking_strategy: ~azure.ai.projects.models.VectorStoreChunkingStrategyRequest - :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] - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStore. The VectorStore is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStore - :raises ~azure.core.exceptions.HttpResponseError: - """ - - if body is not _Unset: - if isinstance(body, dict): - vector_store = super().create_vector_store( - body=body, content_type=content_type or "application/json", **kwargs - ) - elif isinstance(body, io.IOBase): - vector_store = super().create_vector_store(body=body, content_type=content_type, **kwargs) - else: - raise ValueError("Invalid 'body' type: must be a dictionary (JSON) or a file-like object (IO[bytes]).") - else: - store_configuration = None - if data_sources: - store_configuration = _models.VectorStoreConfiguration(data_sources=data_sources) - - vector_store = super().create_vector_store( - file_ids=file_ids, - store_configuration=store_configuration, - name=name, - expires_after=expires_after, - chunking_strategy=chunking_strategy, - metadata=metadata, - **kwargs, - ) - - while vector_store.status == "in_progress": - time.sleep(sleep_interval) - vector_store = super().get_vector_store(vector_store.id) - - return vector_store - - @overload - def create_vector_store_file_batch_and_poll( - self, - vector_store_id: str, - body: JSON, - *, - content_type: str = "application/json", - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.VectorStoreFileBatch: - """Create a vector store file batch and poll. - - :param vector_store_id: Identifier of the vector store. 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 - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStoreFileBatch - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def create_vector_store_file_batch_and_poll( - self, - vector_store_id: str, - *, - file_ids: Optional[List[str]] = None, - data_sources: Optional[List[_models.VectorStoreDataSource]] = None, - content_type: str = "application/json", - chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.VectorStoreFileBatch: - """Create a vector store file batch and poll. - - :param vector_store_id: Identifier of the vector store. Required. - :type vector_store_id: str - :keyword file_ids: List of file identifiers. Required. - :paramtype file_ids: list[str] - :keyword data_sources: List of Azure assets. Default value is None. - :paramtype data_sources: list[~azure.ai.projects.models.VectorStoreDataSource] - :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.projects.models.VectorStoreChunkingStrategyRequest - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStoreFileBatch - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def create_vector_store_file_batch_and_poll( - self, - vector_store_id: str, - body: IO[bytes], - *, - content_type: str = "application/json", - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.VectorStoreFileBatch: - """Create a vector store file batch and poll. - - :param vector_store_id: Identifier of the vector store. 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 - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStoreFileBatch - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace - def create_vector_store_file_batch_and_poll( - self, - vector_store_id: str, - body: Union[JSON, IO[bytes]] = _Unset, - *, - file_ids: Optional[List[str]] = None, - data_sources: Optional[List[_models.VectorStoreDataSource]] = None, - chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, - content_type: str = "application/json", - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.VectorStoreFileBatch: - """Create a vector store file batch and poll. - - :param vector_store_id: Identifier of the vector store. 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: List of file identifiers. Required. - :paramtype file_ids: list[str] - :keyword data_sources: List of Azure assets. Default value is None. - :paramtype data_sources: list[~azure.ai.client.models.VectorStoreDataSource] - :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.projects.models.VectorStoreChunkingStrategyRequest - :keyword content_type: Body parameter content-type. Defaults to "application/json". - :paramtype content_type: str - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStoreFileBatch. The VectorStoreFileBatch is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStoreFileBatch - :raises ~azure.core.exceptions.HttpResponseError: - """ - - if body is not _Unset: - if isinstance(body, dict): - vector_store_file_batch = super().create_vector_store_file_batch( - vector_store_id=vector_store_id, - body=body, - content_type=content_type or "application/json", - **kwargs, - ) - elif isinstance(body, io.IOBase): - vector_store_file_batch = super().create_vector_store_file_batch( - vector_store_id=vector_store_id, - body=body, - content_type=content_type, - **kwargs, - ) - else: - raise ValueError("Invalid type for 'body'. Must be a dict (JSON) or file-like (IO[bytes]).") - else: - vector_store_file_batch = super().create_vector_store_file_batch( - vector_store_id=vector_store_id, - file_ids=file_ids, - data_sources=data_sources, - chunking_strategy=chunking_strategy, - **kwargs, - ) - - while vector_store_file_batch.status == "in_progress": - time.sleep(sleep_interval) - vector_store_file_batch = super().get_vector_store_file_batch( - vector_store_id=vector_store_id, batch_id=vector_store_file_batch.id - ) - - return vector_store_file_batch - - @distributed_trace - def get_file_content(self, file_id: str, **kwargs: Any) -> Iterator[bytes]: - """ - Returns file content as byte stream for given file_id. - - :param file_id: The ID of the file to retrieve. Required. - :type file_id: str - :return: An iterator that yields bytes from the file content. - :rtype: Iterator[bytes] - :raises ~azure.core.exceptions.HttpResponseError: If the HTTP request fails. - """ - kwargs["stream"] = True - response = super()._get_file_content(file_id, **kwargs) - return cast(Iterator[bytes], response) - - @distributed_trace - def save_file(self, file_id: str, file_name: str, target_dir: Optional[Union[str, Path]] = None) -> None: - """ - Synchronously saves file content retrieved using a file identifier to the specified local directory. - - :param file_id: The unique identifier for the file to retrieve. - :type file_id: str - :param file_name: The name of the file to be saved. - :type file_name: str - :param target_dir: The directory where the file should be saved. Defaults to the current working directory. - :type target_dir: Optional[Union[str, Path]] - :raises ValueError: If the target path is not a directory or the file name is invalid. - :raises RuntimeError: If file content retrieval fails or no content is found. - :raises TypeError: If retrieved chunks are not bytes-like objects. - :raises IOError: If writing to the file fails. - """ - try: - # Determine and validate the target directory - path = Path(target_dir).expanduser().resolve() if target_dir else Path.cwd() - path.mkdir(parents=True, exist_ok=True) - if not path.is_dir(): - raise ValueError(f"The target path '{path}' is not a directory.") - - # Sanitize and validate the file name - sanitized_file_name = Path(file_name).name - if not sanitized_file_name: - raise ValueError("The provided file name is invalid.") - - # Retrieve the file content - file_content_stream = self.get_file_content(file_id) - if not file_content_stream: - raise RuntimeError(f"No content retrievable for file ID '{file_id}'.") - - target_file_path = path / sanitized_file_name - - # Write the file content to disk - with target_file_path.open("wb") as file: - for chunk in file_content_stream: - if isinstance(chunk, (bytes, bytearray)): - file.write(chunk) - else: - raise TypeError(f"Expected bytes or bytearray, got {type(chunk).__name__}") - - logger.debug("File '%s' saved successfully at '%s'.", sanitized_file_name, target_file_path) - - except (ValueError, RuntimeError, TypeError, IOError) as e: - logger.error("An error occurred in save_file: %s", e) - raise - - @overload - def create_vector_store_file_and_poll( - self, - vector_store_id: str, - body: JSON, - *, - content_type: str = "application/json", - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.VectorStoreFile: - """Create a vector store file by attaching a file to a vector store. - - :param vector_store_id: Identifier of the vector store. 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 - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStoreFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def create_vector_store_file_and_poll( - self, - vector_store_id: str, - *, - content_type: str = "application/json", - file_id: Optional[str] = None, - data_source: Optional[_models.VectorStoreDataSource] = None, - chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.VectorStoreFile: - """Create a vector store file by attaching a file to a vector store. - - :param vector_store_id: Identifier of the vector store. Required. - :type vector_store_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 file_id: Identifier of the file. Default value is None. - :paramtype file_id: str - :keyword data_source: Azure asset ID. Default value is None. - :paramtype data_source: ~azure.ai.projects.models.VectorStoreDataSource - :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.projects.models.VectorStoreChunkingStrategyRequest - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStoreFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def create_vector_store_file_and_poll( - self, - vector_store_id: str, - body: IO[bytes], - *, - content_type: str = "application/json", - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.VectorStoreFile: - """Create a vector store file by attaching a file to a vector store. - - :param vector_store_id: Identifier of the vector store. 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 - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStoreFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace - def create_vector_store_file_and_poll( - self, - vector_store_id: str, - body: Union[JSON, IO[bytes]] = _Unset, - *, - content_type: str = "application/json", - file_id: Optional[str] = None, - data_source: Optional[_models.VectorStoreDataSource] = None, - chunking_strategy: Optional[_models.VectorStoreChunkingStrategyRequest] = None, - sleep_interval: float = 1, - **kwargs: Any, - ) -> _models.VectorStoreFile: - """Create a vector store file by attaching a file to a vector store. - - :param vector_store_id: Identifier of the vector store. 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 content_type: Body Parameter content-type. Defaults to 'application/json'. - :paramtype content_type: str - :keyword file_id: Identifier of the file. Default value is None. - :paramtype file_id: str - :keyword data_source: Azure asset ID. Default value is None. - :paramtype data_source: ~azure.ai.projects.models.VectorStoreDataSource - :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.projects.models.VectorStoreChunkingStrategyRequest - :keyword sleep_interval: Time to wait before polling for the status of the vector store. Default value - is 1. - :paramtype sleep_interval: float - :return: VectorStoreFile. The VectorStoreFile is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.VectorStoreFile - :raises ~azure.core.exceptions.HttpResponseError: - """ - - if body is not _Unset: - if isinstance(body, dict): - vector_store_file = super().create_vector_store_file( - vector_store_id=vector_store_id, - body=body, - content_type=content_type or "application/json", - **kwargs, - ) - elif isinstance(body, io.IOBase): - vector_store_file = super().create_vector_store_file( - vector_store_id=vector_store_id, - body=body, - content_type=content_type, - **kwargs, - ) - else: - raise ValueError("Invalid type for 'body'. Must be a dict (JSON) or file-like object (IO[bytes]).") - else: - vector_store_file = super().create_vector_store_file( - vector_store_id=vector_store_id, - file_id=file_id, - data_source=data_source, - chunking_strategy=chunking_strategy, - **kwargs, - ) - - while vector_store_file.status == "in_progress": - time.sleep(sleep_interval) - vector_store_file = super().get_vector_store_file( - vector_store_id=vector_store_id, file_id=vector_store_file.id - ) - - return vector_store_file - - @distributed_trace - def delete_agent(self, assistant_id: str, **kwargs: Any) -> _models.AgentDeletionStatus: - """Deletes an agent. - - :param assistant_id: Identifier of the agent. Required. - :type assistant_id: str - :return: AgentDeletionStatus. The AgentDeletionStatus is compatible with MutableMapping - :rtype: ~azure.ai.projects.models.AgentDeletionStatus - :raises ~azure.core.exceptions.HttpResponseError: - """ - if assistant_id in self._toolset: - del self._toolset[assistant_id] - return super().delete_agent(assistant_id, **kwargs) - - -__all__: List[str] = [ - "AgentsOperations", - "ConnectionsOperations", - "TelemetryOperations", - "InferenceOperations", -] # Add all objects you want publicly available to users at this package level +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level def patch_sdk(): diff --git a/sdk/ai/azure-ai-projects/azure/ai/projects/telemetry/__init__.py b/sdk/ai/azure-ai-projects/azure/ai/projects/telemetry/__init__.py deleted file mode 100644 index a5e9e67bf233..000000000000 --- a/sdk/ai/azure-ai-projects/azure/ai/projects/telemetry/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# 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 ._trace_function import trace_function - -__all__ = [ - "trace_function", -] -__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore diff --git a/sdk/ai/azure-ai-projects/azure/ai/projects/telemetry/_trace_function.py b/sdk/ai/azure-ai-projects/azure/ai/projects/telemetry/_trace_function.py deleted file mode 100644 index 1890a6f1e88d..000000000000 --- a/sdk/ai/azure-ai-projects/azure/ai/projects/telemetry/_trace_function.py +++ /dev/null @@ -1,204 +0,0 @@ -# ------------------------------------ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. -# ------------------------------------ -import functools -import asyncio -from typing import Any, Callable, Optional, Dict - -try: - # pylint: disable = no-name-in-module - from opentelemetry import trace as opentelemetry_trace - - tracer = opentelemetry_trace.get_tracer(__name__) - _tracing_library_available = True -except ModuleNotFoundError: - _tracing_library_available = False - -if _tracing_library_available: - - def trace_function(span_name: Optional[str] = None): - """ - A decorator for tracing function calls using OpenTelemetry. - - This decorator handles various data types for function parameters and return values, - and records them as attributes in the trace span. The supported data types include: - - Basic data types: str, int, float, bool - - Collections: list, dict, tuple, set - - Special handling for collections: - - If a collection (list, dict, tuple, set) contains nested collections, the entire collection - is converted to a string before being recorded as an attribute. - - Sets and dictionaries are always converted to strings to ensure compatibility with span attributes. - - Object types are omitted, and the corresponding parameter is not traced. - - :param span_name: The name of the span. If not provided, the function name is used. - :type span_name: Optional[str] - :return: The decorated function with tracing enabled. - :rtype: Callable - """ - - def decorator(func: Callable) -> Callable: - @functools.wraps(func) - async def async_wrapper(*args: Any, **kwargs: Any) -> Any: - """ - Wrapper function for asynchronous functions. - - :param args: Positional arguments passed to the function. - :type args: Tuple[Any] - :return: The result of the decorated asynchronous function. - :rtype: Any - """ - name = span_name if span_name else func.__name__ - with tracer.start_as_current_span(name) as span: - try: - # Sanitize parameters and set them as attributes - sanitized_params = sanitize_parameters(func, *args, **kwargs) - span.set_attributes(sanitized_params) - result = await func(*args, **kwargs) - sanitized_result = sanitize_for_attributes(result) - if sanitized_result is not None: - if isinstance(sanitized_result, (list, dict, tuple, set)): - if any(isinstance(i, (list, dict, tuple, set)) for i in sanitized_result): - sanitized_result = str(sanitized_result) - span.set_attribute("code.function.return.value", sanitized_result) # type: ignore - return result - except Exception as e: - span.record_exception(e) - span.set_attribute("error.type", e.__class__.__qualname__) # type: ignore - raise - - @functools.wraps(func) - def sync_wrapper(*args: Any, **kwargs: Any) -> Any: - """ - Wrapper function for synchronous functions. - - :param args: Positional arguments passed to the function. - :type args: Tuple[Any] - :return: The result of the decorated synchronous function. - :rtype: Any - """ - name = span_name if span_name else func.__name__ - with tracer.start_as_current_span(name) as span: - try: - # Sanitize parameters and set them as attributes - sanitized_params = sanitize_parameters(func, *args, **kwargs) - span.set_attributes(sanitized_params) - result = func(*args, **kwargs) - sanitized_result = sanitize_for_attributes(result) - if sanitized_result is not None: - if isinstance(sanitized_result, (list, dict, tuple, set)): - if any(isinstance(i, (list, dict, tuple, set)) for i in sanitized_result): - sanitized_result = str(sanitized_result) - span.set_attribute("code.function.return.value", sanitized_result) # type: ignore - return result - except Exception as e: - span.record_exception(e) - span.set_attribute("error.type", e.__class__.__qualname__) # type: ignore - raise - - # Determine if the function is async - if asyncio.iscoroutinefunction(func): - return async_wrapper - return sync_wrapper - - return decorator - -else: - # Define a no-op decorator if OpenTelemetry is not available - def trace_function(span_name: Optional[str] = None): # pylint: disable=unused-argument - """ - A no-op decorator for tracing function calls when OpenTelemetry is not available. - - :param span_name: Not used in this version. - :type span_name: Optional[str] - :return: The original function. - :rtype: Callable - """ - - def decorator(func: Callable) -> Callable: - return func - - return decorator - - -def sanitize_parameters(func, *args, **kwargs) -> Dict[str, Any]: - """ - Sanitize function parameters to include only built-in data types. - - :param func: The function being decorated. - :type func: Callable - :param args: Positional arguments passed to the function. - :type args: Tuple[Any] - :return: A dictionary of sanitized parameters. - :rtype: Dict[str, Any] - """ - import inspect - - params = inspect.signature(func).parameters - sanitized_params = {} - - for i, (name, param) in enumerate(params.items()): - if param.default == inspect.Parameter.empty and i < len(args): - value = args[i] - else: - value = kwargs.get(name, param.default) - - sanitized_value = sanitize_for_attributes(value) - # Check if the collection has nested collections - if isinstance(sanitized_value, (list, dict, tuple, set)): - if any(isinstance(i, (list, dict, tuple, set)) for i in sanitized_value): - sanitized_value = str(sanitized_value) - if sanitized_value is not None: - sanitized_params["code.function.parameter." + name] = sanitized_value - - return sanitized_params - - -# pylint: disable=R0911 -def sanitize_for_attributes(value: Any, is_recursive: bool = False) -> Any: - """ - Sanitize a value to be used as an attribute. - - :param value: The value to sanitize. - :type value: Any - :param is_recursive: Indicates if the function is being called recursively. Default is False. - :type is_recursive: bool - :return: The sanitized value or None if the value is not a supported type. - :rtype: Any - """ - if isinstance(value, (str, int, float, bool)): - return value - if isinstance(value, list): - return [ - sanitize_for_attributes(item, True) - for item in value - if isinstance(item, (str, int, float, bool, list, dict, tuple, set)) - ] - if isinstance(value, dict): - retval = { - k: sanitize_for_attributes(v, True) - for k, v in value.items() - if isinstance(v, (str, int, float, bool, list, dict, tuple, set)) - } - # dict to compatible with span attribute, so return it as a string - if is_recursive: - return retval - return str(retval) - if isinstance(value, tuple): - return tuple( - sanitize_for_attributes(item, True) - for item in value - if isinstance(item, (str, int, float, bool, list, dict, tuple, set)) - ) - if isinstance(value, set): - retval_set = { - sanitize_for_attributes(item, True) - for item in value - if isinstance(item, (str, int, float, bool, list, dict, tuple, set)) - } - if is_recursive: - return retval_set - return str(retval_set) - return None diff --git a/sdk/ai/azure-ai-projects/azure/ai/projects/telemetry/agents/__init__.py b/sdk/ai/azure-ai-projects/azure/ai/projects/telemetry/agents/__init__.py deleted file mode 100644 index 34fb7e5f7cd8..000000000000 --- a/sdk/ai/azure-ai-projects/azure/ai/projects/telemetry/agents/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# 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 ._ai_agents_instrumentor import AIAgentsInstrumentor - -__all__ = [ - "AIAgentsInstrumentor", -] diff --git a/sdk/ai/azure-ai-projects/azure/ai/projects/telemetry/agents/_ai_agents_instrumentor.py b/sdk/ai/azure-ai-projects/azure/ai/projects/telemetry/agents/_ai_agents_instrumentor.py deleted file mode 100644 index a0c7bbadd1b8..000000000000 --- a/sdk/ai/azure-ai-projects/azure/ai/projects/telemetry/agents/_ai_agents_instrumentor.py +++ /dev/null @@ -1,1793 +0,0 @@ -# pylint: disable=too-many-lines -# ------------------------------------ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. -# ------------------------------------ -import copy -import functools -import importlib -import json -import logging -import os -from enum import Enum -from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast -from urllib.parse import urlparse - -from azure.ai.projects import _types -from azure.ai.projects.models import AgentRunStream, AsyncAgentRunStream, _models -from azure.ai.projects.models._enums import AgentsApiResponseFormatMode, MessageRole, RunStepStatus -from azure.ai.projects.models import ( - MessageAttachment, - MessageDeltaChunk, - MessageIncompleteDetails, - RunStep, - RunStepDeltaChunk, - RunStepFunctionToolCall, - RunStepToolCallDetails, - ThreadMessage, - ThreadRun, - ToolDefinition, - ToolOutput, - ToolResources, -) -from azure.ai.projects.models._patch import AgentEventHandler, AsyncAgentEventHandler, ToolSet -from azure.ai.projects.telemetry.agents._utils import ( - AZ_AI_AGENT_SYSTEM, - ERROR_TYPE, - GEN_AI_AGENT_DESCRIPTION, - GEN_AI_AGENT_ID, - GEN_AI_AGENT_NAME, - GEN_AI_EVENT_CONTENT, - GEN_AI_MESSAGE_ID, - GEN_AI_MESSAGE_STATUS, - GEN_AI_RESPONSE_MODEL, - GEN_AI_SYSTEM, - GEN_AI_SYSTEM_MESSAGE, - GEN_AI_THREAD_ID, - GEN_AI_THREAD_RUN_ID, - GEN_AI_THREAD_RUN_STATUS, - GEN_AI_USAGE_INPUT_TOKENS, - GEN_AI_USAGE_OUTPUT_TOKENS, - OperationName, - start_span, -) -from azure.core import CaseInsensitiveEnumMeta # type: ignore -from azure.core.settings import settings -from azure.core.tracing import AbstractSpan - -_Unset: Any = object() - -try: - # pylint: disable = no-name-in-module - from opentelemetry.trace import Span, StatusCode - - _tracing_library_available = True -except ModuleNotFoundError: - _tracing_library_available = False - - -__all__ = [ - "AIAgentsInstrumentor", -] - - -_agents_traces_enabled: bool = False -_trace_agents_content: bool = False - - -class TraceType(str, Enum, metaclass=CaseInsensitiveEnumMeta): # pylint: disable=C4747 - """An enumeration class to represent different types of traces.""" - - AGENTS = "Agents" - - -class AIAgentsInstrumentor: - """ - A class for managing the trace instrumentation of AI Agents. - - This class allows enabling or disabling tracing for AI Agents. - and provides functionality to check whether instrumentation is active. - - """ - - def __init__(self): - if not _tracing_library_available: - raise ModuleNotFoundError( - "Azure Core Tracing Opentelemetry is not installed. " - "Please install it using 'pip install azure-core-tracing-opentelemetry'" - ) - # In the future we could support different versions from the same library - # and have a parameter that specifies the version to use. - self._impl = _AIAgentsInstrumentorPreview() - - def instrument(self, enable_content_recording: Optional[bool] = None) -> None: - """ - Enable trace instrumentation for AI Agents. - - :param enable_content_recording: Whether content recording is enabled as part - of the traces or not. Content in this context refers to chat message content - and function call tool related function names, function parameter names and - values. True will enable content recording, False will disable it. If no value - is provided, then the value read from environment variable - AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED is used. If the environment variable - is not found, then the value will default to False. Please note that successive calls - to instrument will always apply the content recording value provided with the most - recent call to instrument (including applying the environment variable if no value is - provided and defaulting to false if the environment variable is not found), even if - instrument was already previously called without uninstrument being called in between - the instrument calls. - :type enable_content_recording: bool, optional - - """ - self._impl.instrument(enable_content_recording) - - def uninstrument(self) -> None: - """ - Remove trace instrumentation for AI Agents. - - This method removes any active instrumentation, stopping the tracing - of AI Agents. - """ - self._impl.uninstrument() - - def is_instrumented(self) -> bool: - """ - Check if trace instrumentation for AI Agents is currently enabled. - - :return: True if instrumentation is active, False otherwise. - :rtype: bool - """ - return self._impl.is_instrumented() - - def is_content_recording_enabled(self) -> bool: - """This function gets the content recording value. - - :return: A bool value indicating whether content recording is enabled. - :rtype: bool - """ - return self._impl.is_content_recording_enabled() - - -class _AIAgentsInstrumentorPreview: - # pylint: disable=R0904 - """ - A class for managing the trace instrumentation of AI Agents. - - This class allows enabling or disabling tracing for AI Agents. - and provides functionality to check whether instrumentation is active. - """ - - def _str_to_bool(self, s): - if s is None: - return False - return str(s).lower() == "true" - - def instrument(self, enable_content_recording: Optional[bool] = None): - """ - Enable trace instrumentation for AI Agents. - - :param enable_content_recording: Whether content recording is enabled as part - of the traces or not. Content in this context refers to chat message content - and function call tool related function names, function parameter names and - values. True will enable content recording, False will disable it. If no value - is provided, then the value read from environment variable - AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED is used. If the environment variable - is not found, then the value will default to False. - - :type enable_content_recording: bool, optional - """ - if enable_content_recording is None: - var_value = os.environ.get("AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED") - enable_content_recording = self._str_to_bool(var_value) - if not self.is_instrumented(): - self._instrument_agents(enable_content_recording) - else: - self._set_enable_content_recording(enable_content_recording=enable_content_recording) - - def uninstrument(self): - """ - Disable trace instrumentation for AI Agents. - - This method removes any active instrumentation, stopping the tracing - of AI Agents. - """ - if self.is_instrumented(): - self._uninstrument_agents() - - def is_instrumented(self): - """ - Check if trace instrumentation for AI Agents is currently enabled. - - :return: True if instrumentation is active, False otherwise. - :rtype: bool - """ - return self._is_instrumented() - - def set_enable_content_recording(self, enable_content_recording: bool = False) -> None: - """This function sets the content recording value. - - :param enable_content_recording: Indicates whether tracing of message content should be enabled. - This also controls whether function call tool function names, - parameter names and parameter values are traced. - :type enable_content_recording: bool - """ - self._set_enable_content_recording(enable_content_recording=enable_content_recording) - - def is_content_recording_enabled(self) -> bool: - """This function gets the content recording value. - - :return: A bool value indicating whether content tracing is enabled. - :rtype bool - """ - return self._is_content_recording_enabled() - - def _set_attributes(self, span: "AbstractSpan", *attrs: Tuple[str, Any]) -> None: - for attr in attrs: - key, value = attr - if value is not None: - span.add_attribute(key, value) - - def _parse_url(self, url): - parsed = urlparse(url) - server_address = parsed.hostname - port = parsed.port - return server_address, port - - def _remove_function_call_names_and_arguments(self, tool_calls: list) -> list: - tool_calls_copy = copy.deepcopy(tool_calls) - for tool_call in tool_calls_copy: - if "function" in tool_call: - if "name" in tool_call["function"]: - del tool_call["function"]["name"] - if "arguments" in tool_call["function"]: - del tool_call["function"]["arguments"] - if not tool_call["function"]: - del tool_call["function"] - return tool_calls_copy - - def _create_event_attributes( - self, - thread_id: Optional[str] = None, - agent_id: Optional[str] = None, - thread_run_id: Optional[str] = None, - message_id: Optional[str] = None, - message_status: Optional[str] = None, - usage: Optional[_models.RunStepCompletionUsage] = None, - ) -> Dict[str, Any]: - attrs: Dict[str, Any] = {GEN_AI_SYSTEM: AZ_AI_AGENT_SYSTEM} - if thread_id: - attrs[GEN_AI_THREAD_ID] = thread_id - - if agent_id: - attrs[GEN_AI_AGENT_ID] = agent_id - - if thread_run_id: - attrs[GEN_AI_THREAD_RUN_ID] = thread_run_id - - if message_id: - attrs[GEN_AI_MESSAGE_ID] = message_id - - if message_status: - attrs[GEN_AI_MESSAGE_STATUS] = self._status_to_string(message_status) - - if usage: - attrs[GEN_AI_USAGE_INPUT_TOKENS] = usage.prompt_tokens - attrs[GEN_AI_USAGE_OUTPUT_TOKENS] = usage.completion_tokens - - return attrs - - def add_thread_message_event( - self, span, message: ThreadMessage, usage: Optional[_models.RunStepCompletionUsage] = None - ) -> None: - content_body = {} - if _trace_agents_content: - for content in message.content: - typed_content = content.get(content.type, None) - if typed_content: - content_details = {"value": self._get_field(typed_content, "value")} - annotations = self._get_field(typed_content, "annotations") - if annotations: - content_details["annotations"] = [a.as_dict() for a in annotations] - content_body[content.type] = content_details - - self._add_message_event( - span, - self._get_role(message.role), - content_body, - attachments=message.attachments, - thread_id=message.thread_id, - agent_id=message.assistant_id, - message_id=message.id, - thread_run_id=message.run_id, - message_status=message.status, - incomplete_details=message.incomplete_details, - usage=usage, - ) - - def _add_message_event( - self, - span, - role: str, - content: Any, - attachments: Any = None, # Optional[List[MessageAttachment]] or dict - thread_id: Optional[str] = None, - agent_id: Optional[str] = None, - message_id: Optional[str] = None, - thread_run_id: Optional[str] = None, - message_status: Optional[str] = None, - incomplete_details: Optional[MessageIncompleteDetails] = None, - usage: Optional[_models.RunStepCompletionUsage] = None, - ) -> None: - # TODO document new fields - - event_body = {} - if _trace_agents_content: - event_body["content"] = content - if attachments: - event_body["attachments"] = [] - for attachment in attachments: - attachment_body = {"id": attachment.file_id} - if attachment.tools: - attachment_body["tools"] = [self._get_field(tool, "type") for tool in attachment.tools] - event_body["attachments"].append(attachment_body) - - if incomplete_details: - event_body["incomplete_details"] = incomplete_details - event_body["role"] = role - - attributes = self._create_event_attributes( - thread_id=thread_id, - agent_id=agent_id, - thread_run_id=thread_run_id, - message_id=message_id, - message_status=message_status, - usage=usage, - ) - attributes[GEN_AI_EVENT_CONTENT] = json.dumps(event_body) - span.span_instance.add_event(name=f"gen_ai.{role}.message", attributes=attributes) - - def _get_field(self, obj: Any, field: str) -> Any: - if not obj: - return None - - if isinstance(obj, dict): - return obj.get(field, None) - - return getattr(obj, field, None) - - def _add_instructions_event( - self, - span: "AbstractSpan", - instructions: Optional[str], - additional_instructions: Optional[str], - agent_id: Optional[str] = None, - thread_id: Optional[str] = None, - ) -> None: - if not instructions: - return - - event_body: Dict[str, Any] = {} - if _trace_agents_content and (instructions or additional_instructions): - if instructions and additional_instructions: - event_body["content"] = f"{instructions} {additional_instructions}" - else: - event_body["content"] = instructions or additional_instructions - - attributes = self._create_event_attributes(agent_id=agent_id, thread_id=thread_id) - attributes[GEN_AI_EVENT_CONTENT] = json.dumps(event_body) - span.span_instance.add_event(name=GEN_AI_SYSTEM_MESSAGE, attributes=attributes) - - def _get_role(self, role: Optional[Union[str, MessageRole]]) -> str: - if role is None or role is _Unset: - return "user" - - if isinstance(role, MessageRole): - return role.value - - return role - - def _status_to_string(self, status: Any) -> str: - return status.value if hasattr(status, "value") else status - - def _add_tool_assistant_message_event(self, span, step: RunStep) -> None: - # do we want a new event for it ? - tool_calls = [ - { - "id": t.id, - "type": t.type, - "function": ( - {"name": t.function.name, "arguments": json.loads(t.function.arguments)} - if isinstance(t, RunStepFunctionToolCall) - else None - ), - } - for t in cast(RunStepToolCallDetails, step.step_details).tool_calls - ] - - attributes = self._create_event_attributes( - thread_id=step.thread_id, - agent_id=step.assistant_id, - thread_run_id=step.run_id, - message_status=step.status, - usage=step.usage, - ) - - if _trace_agents_content: - attributes[GEN_AI_EVENT_CONTENT] = json.dumps({"tool_calls": tool_calls}) - else: - tool_calls_non_recording = self._remove_function_call_names_and_arguments(tool_calls=tool_calls) - attributes[GEN_AI_EVENT_CONTENT] = json.dumps({"tool_calls": tool_calls_non_recording}) - span.span_instance.add_event(name="gen_ai.assistant.message", attributes=attributes) - - def set_end_run(self, span: "AbstractSpan", run: Optional[ThreadRun]) -> None: - if run and span and span.span_instance.is_recording: - span.add_attribute(GEN_AI_THREAD_RUN_STATUS, self._status_to_string(run.status)) - span.add_attribute(GEN_AI_RESPONSE_MODEL, run.model) - if run and run.usage: - span.add_attribute(GEN_AI_USAGE_INPUT_TOKENS, run.usage.prompt_tokens) - span.add_attribute(GEN_AI_USAGE_OUTPUT_TOKENS, run.usage.completion_tokens) - - @staticmethod - def agent_api_response_to_str(response_format: Any) -> Optional[str]: - """ - Convert response_format to string. - - :param response_format: The response format. - :type response_format: ~azure.ai.projects._types.AgentsApiResponseFormatOption - :returns: string for the response_format. - :rtype: Optional[str] - :raises: Value error if response_format is not of type AgentsApiResponseFormatOption. - """ - if isinstance(response_format, str) or response_format is None: - return response_format - if isinstance(response_format, AgentsApiResponseFormatMode): - return response_format.value - if isinstance(response_format, _models.AgentsApiResponseFormat): - return response_format.type - if isinstance(response_format, _models.ResponseFormatJsonSchemaType): - return response_format.type - raise ValueError(f"Unknown response format {type(response_format)}") - - def start_thread_run_span( - self, - operation_name: OperationName, - project_name: str, - thread_id: Optional[str] = None, - agent_id: Optional[str] = None, - model: Optional[str] = None, - instructions: Optional[str] = None, - additional_instructions: Optional[str] = None, - additional_messages: Optional[List[ThreadMessage]] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - _tools: Optional[List[ToolDefinition]] = None, - max_prompt_tokens: Optional[int] = None, - max_completion_tokens: Optional[int] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - ) -> "Optional[AbstractSpan]": - span = start_span( - operation_name, - project_name, - thread_id=thread_id, - agent_id=agent_id, - model=model, - temperature=temperature, - top_p=top_p, - max_prompt_tokens=max_prompt_tokens, - max_completion_tokens=max_completion_tokens, - response_format=_AIAgentsInstrumentorPreview.agent_api_response_to_str(response_format), - ) - if span and span.span_instance.is_recording and instructions and additional_instructions: - self._add_instructions_event( - span, instructions, additional_instructions, thread_id=thread_id, agent_id=agent_id - ) - - if additional_messages: - for message in additional_messages: - self.add_thread_message_event(span, message) - return span - - def start_submit_tool_outputs_span( - self, - project_name: str, - thread_id: Optional[str] = None, - run_id: Optional[str] = None, - tool_outputs: Optional[List[ToolOutput]] = None, - event_handler: Optional[Union[AgentEventHandler, AsyncAgentEventHandler]] = None, - ) -> "Optional[AbstractSpan]": - run_span = event_handler.span if isinstance(event_handler, _AgentEventHandlerTraceWrapper) else None - if run_span is None: - run_span = event_handler.span if isinstance(event_handler, _AsyncAgentEventHandlerTraceWrapper) else None - - if run_span: - recorded = self._add_tool_message_events(run_span, tool_outputs) - else: - recorded = False - - span = start_span(OperationName.SUBMIT_TOOL_OUTPUTS, project_name, thread_id=thread_id, run_id=run_id) - if not recorded: - self._add_tool_message_events(span, tool_outputs) - return span - - def _add_tool_message_events( - self, span: "Optional[AbstractSpan]", tool_outputs: Optional[List[ToolOutput]] - ) -> bool: - if span and span.span_instance.is_recording and tool_outputs: - for tool_output in tool_outputs: - if _trace_agents_content: - body = {"content": tool_output["output"], "id": tool_output["tool_call_id"]} - else: - body = {"content": "", "id": tool_output["tool_call_id"]} - span.span_instance.add_event("gen_ai.tool.message", {"gen_ai.event.content": json.dumps(body)}) - return True - - return False - - def start_create_agent_span( - self, - project_name: str, - model: Optional[str] = None, - name: Optional[str] = None, - description: Optional[str] = None, - instructions: Optional[str] = None, - _tools: Optional[List[ToolDefinition]] = None, - _tool_resources: Optional[ToolResources] = None, - _toolset: Optional[ToolSet] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - response_format: Optional["_types.AgentsApiResponseFormatOption"] = None, - ) -> "Optional[AbstractSpan]": - span = start_span( - OperationName.CREATE_AGENT, - project_name, - span_name=f"{OperationName.CREATE_AGENT.value} {name}", - model=model, - temperature=temperature, - top_p=top_p, - response_format=_AIAgentsInstrumentorPreview.agent_api_response_to_str(response_format), - ) - if span and span.span_instance.is_recording: - if name: - span.add_attribute(GEN_AI_AGENT_NAME, name) - if description: - span.add_attribute(GEN_AI_AGENT_DESCRIPTION, description) - self._add_instructions_event(span, instructions, None) - - return span - - def start_create_thread_span( - self, - project_name: str, - messages: Optional[List[ThreadMessage]] = None, - _tool_resources: Optional[ToolResources] = None, - ) -> "Optional[AbstractSpan]": - span = start_span(OperationName.CREATE_THREAD, project_name) - if span and span.span_instance.is_recording: - for message in messages or []: - self.add_thread_message_event(span, message) - - return span - - def start_list_messages_span(self, project_name: str, thread_id: Optional[str] = None) -> "Optional[AbstractSpan]": - return start_span(OperationName.LIST_MESSAGES, project_name, thread_id=thread_id) - - def trace_create_agent(self, function, *args, **kwargs): - project_name = args[ # pylint: disable=protected-access # pyright: ignore [reportFunctionMemberAccess] - 0 - ]._config.project_name - name = kwargs.get("name") - model = kwargs.get("model") - description = kwargs.get("description") - instructions = kwargs.get("instructions") - tools = kwargs.get("tools") - tool_resources = kwargs.get("tool_resources") - toolset = kwargs.get("toolset") - temperature = kwargs.get("temperature") - top_p = kwargs.get("top_p") - response_format = kwargs.get("response_format") - - span = self.start_create_agent_span( - project_name=project_name, - name=name, - model=model, - description=description, - instructions=instructions, - _tools=tools, - _tool_resources=tool_resources, - _toolset=toolset, - temperature=temperature, - top_p=top_p, - response_format=response_format, - ) - - if span is None: - return function(*args, **kwargs) - - with span: - try: - result = function(*args, **kwargs) - span.add_attribute(GEN_AI_AGENT_ID, result.id) - except Exception as exc: - # Set the span status to error - if isinstance(span.span_instance, Span): # pyright: ignore [reportPossiblyUnboundVariable] - span.span_instance.set_status( - StatusCode.ERROR, # pyright: ignore [reportPossiblyUnboundVariable] - description=str(exc), - ) - module = getattr(exc, "__module__", "") - module = module if module != "builtins" else "" - error_type = f"{module}.{type(exc).__name__}" if module else type(exc).__name__ - self._set_attributes(span, ("error.type", error_type)) - raise - - return result - - async def trace_create_agent_async(self, function, *args, **kwargs): - project_name = args[ # pylint: disable=protected-access # pyright: ignore [reportFunctionMemberAccess] - 0 - ]._config.project_name - name = kwargs.get("name") - model = kwargs.get("model") - description = kwargs.get("description") - instructions = kwargs.get("instructions") - tools = kwargs.get("tools") - tool_resources = kwargs.get("tool_resources") - toolset = kwargs.get("toolset") - temperature = kwargs.get("temperature") - top_p = kwargs.get("top_p") - response_format = kwargs.get("response_format") - - span = self.start_create_agent_span( - project_name=project_name, - name=name, - model=model, - description=description, - instructions=instructions, - _tools=tools, - _tool_resources=tool_resources, - _toolset=toolset, - temperature=temperature, - top_p=top_p, - response_format=response_format, - ) - - if span is None: - return await function(*args, **kwargs) - - with span: - try: - result = await function(*args, **kwargs) - span.add_attribute(GEN_AI_AGENT_ID, result.id) - except Exception as exc: - # Set the span status to error - if isinstance(span.span_instance, Span): # pyright: ignore [reportPossiblyUnboundVariable] - span.span_instance.set_status( - StatusCode.ERROR, # pyright: ignore [reportPossiblyUnboundVariable] - description=str(exc), - ) - module = getattr(exc, "__module__", "") - module = module if module != "builtins" else "" - error_type = f"{module}.{type(exc).__name__}" if module else type(exc).__name__ - self._set_attributes(span, ("error.type", error_type)) - raise - - return result - - def trace_create_thread(self, function, *args, **kwargs): - project_name = args[ # pylint: disable=protected-access # pyright: ignore [reportFunctionMemberAccess] - 0 - ]._config.project_name - messages = kwargs.get("messages") - - span = self.start_create_thread_span(project_name=project_name, messages=messages) - - if span is None: - return function(*args, **kwargs) - - with span: - try: - result = function(*args, **kwargs) - span.add_attribute(GEN_AI_THREAD_ID, result.get("id")) - except Exception as exc: - # Set the span status to error - if isinstance(span.span_instance, Span): # pyright: ignore [reportPossiblyUnboundVariable] - span.span_instance.set_status( - StatusCode.ERROR, # pyright: ignore [reportPossiblyUnboundVariable] - description=str(exc), - ) - module = getattr(exc, "__module__", "") - module = module if module != "builtins" else "" - error_type = f"{module}.{type(exc).__name__}" if module else type(exc).__name__ - self._set_attributes(span, ("error.type", error_type)) - raise - - return result - - async def trace_create_thread_async(self, function, *args, **kwargs): - project_name = args[ # pylint: disable=protected-access # pyright: ignore [reportFunctionMemberAccess] - 0 - ]._config.project_name - messages = kwargs.get("messages") - - span = self.start_create_thread_span(project_name=project_name, messages=messages) - - if span is None: - return await function(*args, **kwargs) - - with span: - try: - result = await function(*args, **kwargs) - span.add_attribute(GEN_AI_THREAD_ID, result.get("id")) - except Exception as exc: - # Set the span status to error - if isinstance(span.span_instance, Span): # pyright: ignore [reportPossiblyUnboundVariable] - span.span_instance.set_status( - StatusCode.ERROR, # pyright: ignore [reportPossiblyUnboundVariable] - description=str(exc), - ) - module = getattr(exc, "__module__", "") - module = module if module != "builtins" else "" - error_type = f"{module}.{type(exc).__name__}" if module else type(exc).__name__ - self._set_attributes(span, ("error.type", error_type)) - raise - - return result - - def trace_create_message(self, function, *args, **kwargs): - project_name = args[ # pylint: disable=protected-access # pyright: ignore [reportFunctionMemberAccess] - 0 - ]._config.project_name - thread_id = kwargs.get("thread_id") - role = kwargs.get("role") - content = kwargs.get("content") - attachments = kwargs.get("attachments") - - span = self.start_create_message_span( - project_name=project_name, thread_id=thread_id, content=content, role=role, attachments=attachments - ) - - if span is None: - return function(*args, **kwargs) - - with span: - try: - result = function(*args, **kwargs) - span.add_attribute(GEN_AI_MESSAGE_ID, result.get("id")) - except Exception as exc: - # Set the span status to error - if isinstance(span.span_instance, Span): # pyright: ignore [reportPossiblyUnboundVariable] - span.span_instance.set_status( - StatusCode.ERROR, # pyright: ignore [reportPossiblyUnboundVariable] - description=str(exc), - ) - module = getattr(exc, "__module__", "") - module = module if module != "builtins" else "" - error_type = f"{module}.{type(exc).__name__}" if module else type(exc).__name__ - self._set_attributes(span, ("error.type", error_type)) - raise - - return result - - async def trace_create_message_async(self, function, *args, **kwargs): - project_name = args[ # pylint: disable=protected-access # pyright: ignore [reportFunctionMemberAccess] - 0 - ]._config.project_name - thread_id = kwargs.get("thread_id") - role = kwargs.get("role") - content = kwargs.get("content") - attachments = kwargs.get("attachments") - - span = self.start_create_message_span( - project_name=project_name, thread_id=thread_id, content=content, role=role, attachments=attachments - ) - - if span is None: - return await function(*args, **kwargs) - - with span: - try: - result = await function(*args, **kwargs) - span.add_attribute(GEN_AI_MESSAGE_ID, result.get("id")) - except Exception as exc: - # Set the span status to error - if isinstance(span.span_instance, Span): # pyright: ignore [reportPossiblyUnboundVariable] - span.span_instance.set_status( - StatusCode.ERROR, # pyright: ignore [reportPossiblyUnboundVariable] - description=str(exc), - ) - module = getattr(exc, "__module__", "") - module = module if module != "builtins" else "" - error_type = f"{module}.{type(exc).__name__}" if module else type(exc).__name__ - self._set_attributes(span, ("error.type", error_type)) - raise - - return result - - def trace_create_run(self, operation_name, function, *args, **kwargs): - project_name = args[ # pylint: disable=protected-access # pyright: ignore [reportFunctionMemberAccess] - 0 - ]._config.project_name - thread_id = kwargs.get("thread_id") - assistant_id = kwargs.get("assistant_id") - model = kwargs.get("model") - instructions = kwargs.get("instructions") - additional_instructions = kwargs.get("additional_instructions") - additional_messages = kwargs.get("additional_messages") - temperature = kwargs.get("temperature") - tools = kwargs.get("tools") - top_p = kwargs.get("top_p") - max_prompt_tokens = kwargs.get("max_prompt_tokens") - max_completion_tokens = kwargs.get("max_completion_tokens") - response_format = kwargs.get("response_format") - - span = self.start_thread_run_span( - operation_name, - project_name, - thread_id, - assistant_id, - model=model, - instructions=instructions, - additional_instructions=additional_instructions, - additional_messages=additional_messages, - temperature=temperature, - _tools=tools, - top_p=top_p, - max_prompt_tokens=max_prompt_tokens, - max_completion_tokens=max_completion_tokens, - response_format=response_format, - ) - - if span is None: - return function(*args, **kwargs) - - with span: - try: - result = function(*args, **kwargs) - self.set_end_run(span, result) - except Exception as exc: - # Set the span status to error - if isinstance(span.span_instance, Span): # pyright: ignore [reportPossiblyUnboundVariable] - span.span_instance.set_status( - StatusCode.ERROR, # pyright: ignore [reportPossiblyUnboundVariable] - description=str(exc), - ) - module = getattr(exc, "__module__", "") - module = module if module != "builtins" else "" - error_type = f"{module}.{type(exc).__name__}" if module else type(exc).__name__ - self._set_attributes(span, ("error.type", error_type)) - raise - - return result - - async def trace_create_run_async(self, operation_name, function, *args, **kwargs): - project_name = args[ # pylint: disable=protected-access # pyright: ignore [reportFunctionMemberAccess] - 0 - ]._config.project_name - thread_id = kwargs.get("thread_id") - assistant_id = kwargs.get("assistant_id") - model = kwargs.get("model") - instructions = kwargs.get("instructions") - additional_instructions = kwargs.get("additional_instructions") - additional_messages = kwargs.get("additional_messages") - temperature = kwargs.get("temperature") - tools = kwargs.get("tools") - top_p = kwargs.get("top_p") - max_prompt_tokens = kwargs.get("max_prompt_tokens") - max_completion_tokens = kwargs.get("max_completion_tokens") - response_format = kwargs.get("response_format") - - span = self.start_thread_run_span( - operation_name, - project_name, - thread_id, - assistant_id, - model=model, - instructions=instructions, - additional_instructions=additional_instructions, - additional_messages=additional_messages, - temperature=temperature, - _tools=tools, - top_p=top_p, - max_prompt_tokens=max_prompt_tokens, - max_completion_tokens=max_completion_tokens, - response_format=response_format, - ) - - if span is None: - return await function(*args, **kwargs) - - with span: - try: - result = await function(*args, **kwargs) - if span.span_instance.is_recording: - span.add_attribute(GEN_AI_THREAD_RUN_STATUS, self._status_to_string(result.status)) - span.add_attribute(GEN_AI_RESPONSE_MODEL, result.model) - if result.usage: - span.add_attribute(GEN_AI_USAGE_INPUT_TOKENS, result.usage.prompt_tokens) - span.add_attribute(GEN_AI_USAGE_OUTPUT_TOKENS, result.usage.completion_tokens) - span.add_attribute(GEN_AI_MESSAGE_ID, result.get("id")) - except Exception as exc: - # Set the span status to error - if isinstance(span.span_instance, Span): # pyright: ignore [reportPossiblyUnboundVariable] - span.span_instance.set_status( - StatusCode.ERROR, # pyright: ignore [reportPossiblyUnboundVariable] - description=str(exc), - ) - module = getattr(exc, "__module__", "") - module = module if module != "builtins" else "" - error_type = f"{module}.{type(exc).__name__}" if module else type(exc).__name__ - self._set_attributes(span, ("error.type", error_type)) - raise - - return result - - def trace_submit_tool_outputs(self, stream, function, *args, **kwargs): - project_name = args[ # pylint: disable=protected-access # pyright: ignore [reportFunctionMemberAccess] - 0 - ]._config.project_name - thread_id = kwargs.get("thread_id") - run_id = kwargs.get("run_id") - tool_outputs = kwargs.get("tool_outputs") - event_handler = kwargs.get("event_handler") - - span = self.start_submit_tool_outputs_span( - project_name=project_name, - thread_id=thread_id, - run_id=run_id, - tool_outputs=tool_outputs, - event_handler=event_handler, - ) - - if span is None: - return function(*args, **kwargs) - - with span: - try: - if stream and event_handler: - kwargs["event_handler"] = self.wrap_handler(event_handler, span) - - result = function(*args, **kwargs) - if not isinstance(result, AgentRunStream): - self.set_end_run(span, result) - except Exception as exc: - # Set the span status to error - if isinstance(span.span_instance, Span): # pyright: ignore [reportPossiblyUnboundVariable] - span.span_instance.set_status( - StatusCode.ERROR, # pyright: ignore [reportPossiblyUnboundVariable] - description=str(exc), - ) - module = getattr(exc, "__module__", "") - module = module if module != "builtins" else "" - error_type = f"{module}.{type(exc).__name__}" if module else type(exc).__name__ - self._set_attributes(span, ("error.type", error_type)) - raise - - return result - - async def trace_submit_tool_outputs_async(self, stream, function, *args, **kwargs): - project_name = args[ # pylint: disable=protected-access # pyright: ignore [reportFunctionMemberAccess] - 0 - ]._config.project_name - thread_id = kwargs.get("thread_id") - run_id = kwargs.get("run_id") - tool_outputs = kwargs.get("tool_outputs") - event_handler = kwargs.get("event_handler") - - span = self.start_submit_tool_outputs_span( - project_name=project_name, - thread_id=thread_id, - run_id=run_id, - tool_outputs=tool_outputs, - event_handler=event_handler, - ) - - if span is None: - return await function(*args, **kwargs) - - with span: - try: - if stream: - kwargs["event_handler"] = self.wrap_async_handler(event_handler, span) - - result = await function(*args, **kwargs) - if not isinstance(result, AsyncAgentRunStream): - self.set_end_run(span, result) - except Exception as exc: - # Set the span status to error - if isinstance(span.span_instance, Span): # pyright: ignore [reportPossiblyUnboundVariable] - span.span_instance.set_status( - StatusCode.ERROR, # pyright: ignore [reportPossiblyUnboundVariable] - description=str(exc), - ) - module = getattr(exc, "__module__", "") - module = module if module != "builtins" else "" - error_type = f"{module}.{type(exc).__name__}" if module else type(exc).__name__ - self._set_attributes(span, ("error.type", error_type)) - raise - - return result - - def trace_handle_submit_tool_outputs(self, function, *args, **kwargs): - event_handler = kwargs.get("event_handler") - if event_handler is None: - event_handler = args[2] - span = getattr(event_handler, "span", None) - - if span is None: - return function(*args, **kwargs) - - with span.change_context(span.span_instance): - try: - result = function(*args, **kwargs) - except Exception as exc: - # Set the span status to error - if isinstance(span.span_instance, Span): # pyright: ignore [reportPossiblyUnboundVariable] - span.span_instance.set_status( - StatusCode.ERROR, # pyright: ignore [reportPossiblyUnboundVariable] - description=str(exc), - ) - module = getattr(exc, "__module__", "") - module = module if module != "builtins" else "" - error_type = f"{module}.{type(exc).__name__}" if module else type(exc).__name__ - self._set_attributes(span, ("error.type", error_type)) - raise - - return result - - async def trace_handle_submit_tool_outputs_async(self, function, *args, **kwargs): - event_handler = kwargs.get("event_handler") - if event_handler is None: - event_handler = args[2] - span = getattr(event_handler, "span", None) - - if span is None: - return await function(*args, **kwargs) - - with span.change_context(span.span_instance): - try: - result = await function(*args, **kwargs) - except Exception as exc: - # Set the span status to error - if isinstance(span.span_instance, Span): # pyright: ignore [reportPossiblyUnboundVariable] - span.span_instance.set_status( - StatusCode.ERROR, # pyright: ignore [reportPossiblyUnboundVariable] - description=str(exc), - ) - module = getattr(exc, "__module__", "") - module = module if module != "builtins" else "" - error_type = f"{module}.{type(exc).__name__}" if module else type(exc).__name__ - self._set_attributes(span, ("error.type", error_type)) - raise - - return result - - def trace_create_stream(self, function, *args, **kwargs): - operation_name = OperationName.PROCESS_THREAD_RUN - project_name = args[ # pylint: disable=protected-access # pyright: ignore [reportFunctionMemberAccess] - 0 - ]._config.project_name - thread_id = kwargs.get("thread_id") - assistant_id = kwargs.get("assistant_id") - model = kwargs.get("model") - instructions = kwargs.get("instructions") - additional_instructions = kwargs.get("additional_instructions") - additional_messages = kwargs.get("additional_messages") - temperature = kwargs.get("temperature") - tools = kwargs.get("tools") - top_p = kwargs.get("top_p") - max_prompt_tokens = kwargs.get("max_prompt_tokens") - max_completion_tokens = kwargs.get("max_completion_tokens") - response_format = kwargs.get("response_format") - event_handler = kwargs.get("event_handler") - - span = self.start_thread_run_span( - operation_name, - project_name, - thread_id, - assistant_id, - model=model, - instructions=instructions, - additional_instructions=additional_instructions, - additional_messages=additional_messages, - temperature=temperature, - _tools=tools, - top_p=top_p, - max_prompt_tokens=max_prompt_tokens, - max_completion_tokens=max_completion_tokens, - response_format=response_format, - ) - - if span is None: - return function(*args, **kwargs) - - # TODO: how to keep span active in the current context without existing? - # TODO: dummy span for none - with span.change_context(span.span_instance): - try: - kwargs["event_handler"] = self.wrap_handler(event_handler, span) - result = function(*args, **kwargs) - except Exception as exc: - # Set the span status to error - if isinstance(span.span_instance, Span): # pyright: ignore [reportPossiblyUnboundVariable] - span.span_instance.set_status( - StatusCode.ERROR, # pyright: ignore [reportPossiblyUnboundVariable] - description=str(exc), - ) - module = getattr(exc, "__module__", "") - module = module if module != "builtins" else "" - error_type = f"{module}.{type(exc).__name__}" if module else type(exc).__name__ - self._set_attributes(span, ("error.type", error_type)) - raise - - return result - - async def trace_create_stream_async(self, function, *args, **kwargs): - operation_name = OperationName.PROCESS_THREAD_RUN - project_name = args[ # pylint: disable=protected-access # pyright: ignore [reportFunctionMemberAccess] - 0 - ]._config.project_name - thread_id = kwargs.get("thread_id") - assistant_id = kwargs.get("assistant_id") - model = kwargs.get("model") - instructions = kwargs.get("instructions") - additional_instructions = kwargs.get("additional_instructions") - additional_messages = kwargs.get("additional_messages") - temperature = kwargs.get("temperature") - tools = kwargs.get("tools") - top_p = kwargs.get("top_p") - max_prompt_tokens = kwargs.get("max_prompt_tokens") - max_completion_tokens = kwargs.get("max_completion_tokens") - response_format = kwargs.get("response_format") - event_handler = kwargs.get("event_handler") - - span = self.start_thread_run_span( - operation_name, - project_name, - thread_id, - assistant_id, - model=model, - instructions=instructions, - additional_instructions=additional_instructions, - additional_messages=additional_messages, - temperature=temperature, - _tools=tools, - top_p=top_p, - max_prompt_tokens=max_prompt_tokens, - max_completion_tokens=max_completion_tokens, - response_format=response_format, - ) - - if span is None: - return await function(*args, **kwargs) - - # TODO: how to keep span active in the current context without existing? - # TODO: dummy span for none - with span.change_context(span.span_instance): - try: - kwargs["event_handler"] = self.wrap_async_handler(event_handler, span) - result = await function(*args, **kwargs) - except Exception as exc: - # Set the span status to error - if isinstance(span.span_instance, Span): # pyright: ignore [reportPossiblyUnboundVariable] - span.span_instance.set_status( - StatusCode.ERROR, # pyright: ignore [reportPossiblyUnboundVariable] - description=str(exc), - ) - module = getattr(exc, "__module__", "") - module = module if module != "builtins" else "" - error_type = f"{module}.{type(exc).__name__}" if module else type(exc).__name__ - self._set_attributes(span, ("error.type", error_type)) - raise - - return result - - def trace_list_messages(self, function, *args, **kwargs): - project_name = args[ # pylint: disable=protected-access # pyright: ignore [reportFunctionMemberAccess] - 0 - ]._config.project_name - thread_id = kwargs.get("thread_id") - - span = self.start_list_messages_span(project_name=project_name, thread_id=thread_id) - - if span is None: - return function(*args, **kwargs) - - with span: - try: - result = function(*args, **kwargs) - for message in result.data: - self.add_thread_message_event(span, message) - - except Exception as exc: - # Set the span status to error - if isinstance(span.span_instance, Span): # pyright: ignore [reportPossiblyUnboundVariable] - span.span_instance.set_status( - StatusCode.ERROR, # pyright: ignore [reportPossiblyUnboundVariable] - description=str(exc), - ) - module = getattr(exc, "__module__", "") - module = module if module != "builtins" else "" - error_type = f"{module}.{type(exc).__name__}" if module else type(exc).__name__ - self._set_attributes(span, ("error.type", error_type)) - raise - - return result - - async def trace_list_messages_async(self, function, *args, **kwargs): - project_name = args[ # pylint: disable=protected-access # pyright: ignore [reportFunctionMemberAccess] - 0 - ]._config.project_name - thread_id = kwargs.get("thread_id") - - span = self.start_list_messages_span(project_name=project_name, thread_id=thread_id) - - if span is None: - return await function(*args, **kwargs) - - with span: - try: - result = await function(*args, **kwargs) - for message in result.data: - self.add_thread_message_event(span, message) - - except Exception as exc: - # Set the span status to error - if isinstance(span.span_instance, Span): # pyright: ignore [reportPossiblyUnboundVariable] - span.span_instance.set_status( - StatusCode.ERROR, # pyright: ignore [reportPossiblyUnboundVariable] - description=str(exc), - ) - module = getattr(exc, "__module__", "") - module = module if module != "builtins" else "" - error_type = f"{module}.{type(exc).__name__}" if module else type(exc).__name__ - self._set_attributes(span, ("error.type", error_type)) - raise - - return result - - def handle_run_stream_exit(self, _function, *args, **kwargs): - agent_run_stream = args[0] - exc_type = kwargs.get("exc_type") - exc_val = kwargs.get("exc_val") - exc_tb = kwargs.get("exc_tb") - # TODO: is it a good idea? - # if not, we'll need to wrap stream and call exit - if ( - agent_run_stream.event_handler - and agent_run_stream.event_handler.__class__.__name__ == "_AgentEventHandlerTraceWrapper" - ): - agent_run_stream.event_handler.__exit__(exc_type, exc_val, exc_tb) - elif ( - agent_run_stream.event_handler - and agent_run_stream.event_handler.__class__.__name__ == "_AsyncAgentEventHandlerTraceWrapper" - ): - agent_run_stream.event_handler.__aexit__(exc_type, exc_val, exc_tb) - - def wrap_handler( - self, handler: "Optional[AgentEventHandler]" = None, span: "Optional[AbstractSpan]" = None - ) -> "Optional[AgentEventHandler]": - if isinstance(handler, _AgentEventHandlerTraceWrapper): - return handler - - if span and span.span_instance.is_recording: - return _AgentEventHandlerTraceWrapper(self, span, handler) - - return handler - - def wrap_async_handler( - self, handler: "Optional[AsyncAgentEventHandler]" = None, span: "Optional[AbstractSpan]" = None - ) -> "Optional[AsyncAgentEventHandler]": - if isinstance(handler, _AsyncAgentEventHandlerTraceWrapper): - return handler - - if span and span.span_instance.is_recording: - return _AsyncAgentEventHandlerTraceWrapper(self, span, handler) - - return handler - - def start_create_message_span( - self, - project_name: str, - thread_id: Optional[str] = None, - content: Optional[str] = None, - role: Optional[Union[str, MessageRole]] = None, - attachments: Optional[List[MessageAttachment]] = None, - ) -> "Optional[AbstractSpan]": - role_str = self._get_role(role) - span = start_span(OperationName.CREATE_MESSAGE, project_name, thread_id=thread_id) - if span and span.span_instance.is_recording: - self._add_message_event(span, role_str, content, attachments=attachments, thread_id=thread_id) - return span - - def _trace_sync_function( - self, - function: Callable, - *, - _args_to_ignore: Optional[List[str]] = None, - _trace_type=TraceType.AGENTS, - _name: Optional[str] = None, - ) -> Callable: - """ - Decorator that adds tracing to a synchronous function. - - :param function: The function to be traced. - :type function: Callable - :param args_to_ignore: A list of argument names to be ignored in the trace. - Defaults to None. - :type: args_to_ignore: [List[str]], optional - :param trace_type: The type of the trace. Defaults to TraceType.AGENTS. - :type trace_type: TraceType, optional - :param name: The name of the trace, will set to func name if not provided. - :type name: str, optional - :return: The traced function. - :rtype: Callable - """ - - @functools.wraps(function) - def inner(*args, **kwargs): # pylint: disable=R0911 - span_impl_type = settings.tracing_implementation() # pylint: disable=E1102 - if span_impl_type is None: - return function(*args, **kwargs) - - class_function_name = function.__qualname__ - - if class_function_name.startswith("AgentsOperations.create_agent"): - return self.trace_create_agent(function, *args, **kwargs) - if class_function_name.startswith("AgentsOperations.create_thread"): - return self.trace_create_thread(function, *args, **kwargs) - if class_function_name.startswith("AgentsOperations.create_message"): - return self.trace_create_message(function, *args, **kwargs) - if class_function_name.startswith("AgentsOperations.create_run"): - return self.trace_create_run(OperationName.START_THREAD_RUN, function, *args, **kwargs) - if class_function_name.startswith("AgentsOperations.create_and_process_run"): - return self.trace_create_run(OperationName.PROCESS_THREAD_RUN, function, *args, **kwargs) - if class_function_name.startswith("AgentsOperations.submit_tool_outputs_to_run"): - return self.trace_submit_tool_outputs(False, function, *args, **kwargs) - if class_function_name.startswith("AgentsOperations.submit_tool_outputs_to_stream"): - return self.trace_submit_tool_outputs(True, function, *args, **kwargs) - if class_function_name.startswith("AgentsOperations._handle_submit_tool_outputs"): - return self.trace_handle_submit_tool_outputs(function, *args, **kwargs) - if class_function_name.startswith("AgentsOperations.create_stream"): - return self.trace_create_stream(function, *args, **kwargs) - if class_function_name.startswith("AgentsOperations.list_messages"): - return self.trace_list_messages(function, *args, **kwargs) - if class_function_name.startswith("AgentRunStream.__exit__"): - return self.handle_run_stream_exit(function, *args, **kwargs) - # Handle the default case (if the function name does not match) - return None # Ensure all paths return - - return inner - - def _trace_async_function( - self, - function: Callable, - *, - _args_to_ignore: Optional[List[str]] = None, - _trace_type=TraceType.AGENTS, - _name: Optional[str] = None, - ) -> Callable: - """ - Decorator that adds tracing to an asynchronous function. - - :param function: The function to be traced. - :type function: Callable - :param args_to_ignore: A list of argument names to be ignored in the trace. - Defaults to None. - :type: args_to_ignore: [List[str]], optional - :param trace_type: The type of the trace. Defaults to TraceType.AGENTS. - :type trace_type: TraceType, optional - :param name: The name of the trace, will set to func name if not provided. - :type name: str, optional - :return: The traced function. - :rtype: Callable - """ - - @functools.wraps(function) - async def inner(*args, **kwargs): # pylint: disable=R0911 - span_impl_type = settings.tracing_implementation() # pylint: disable=E1102 - if span_impl_type is None: - return function(*args, **kwargs) - - class_function_name = function.__qualname__ - - if class_function_name.startswith("AgentsOperations.create_agent"): - return await self.trace_create_agent_async(function, *args, **kwargs) - if class_function_name.startswith("AgentsOperations.create_thread"): - return await self.trace_create_thread_async(function, *args, **kwargs) - if class_function_name.startswith("AgentsOperations.create_message"): - return await self.trace_create_message_async(function, *args, **kwargs) - if class_function_name.startswith("AgentsOperations.create_run"): - return await self.trace_create_run_async(OperationName.START_THREAD_RUN, function, *args, **kwargs) - if class_function_name.startswith("AgentsOperations.create_and_process_run"): - return await self.trace_create_run_async(OperationName.PROCESS_THREAD_RUN, function, *args, **kwargs) - if class_function_name.startswith("AgentsOperations.submit_tool_outputs_to_run"): - return await self.trace_submit_tool_outputs_async(False, function, *args, **kwargs) - if class_function_name.startswith("AgentsOperations.submit_tool_outputs_to_stream"): - return await self.trace_submit_tool_outputs_async(True, function, *args, **kwargs) - if class_function_name.startswith("AgentsOperations._handle_submit_tool_outputs"): - return await self.trace_handle_submit_tool_outputs_async(function, *args, **kwargs) - if class_function_name.startswith("AgentsOperations.create_stream"): - return await self.trace_create_stream_async(function, *args, **kwargs) - if class_function_name.startswith("AgentsOperations.list_messages"): - return await self.trace_list_messages_async(function, *args, **kwargs) - if class_function_name.startswith("AsyncAgentRunStream.__aexit__"): - return self.handle_run_stream_exit(function, *args, **kwargs) - # Handle the default case (if the function name does not match) - return None # Ensure all paths return - - return inner - - def _inject_async(self, f, _trace_type, _name): - wrapper_fun = self._trace_async_function(f) - wrapper_fun._original = f # pylint: disable=protected-access # pyright: ignore [reportFunctionMemberAccess] - return wrapper_fun - - def _inject_sync(self, f, _trace_type, _name): - wrapper_fun = self._trace_sync_function(f) - wrapper_fun._original = f # pylint: disable=protected-access # pyright: ignore [reportFunctionMemberAccess] - return wrapper_fun - - def _agents_apis(self): - sync_apis = ( - ("azure.ai.projects.operations", "AgentsOperations", "create_agent", TraceType.AGENTS, "agent_create"), - ("azure.ai.projects.operations", "AgentsOperations", "create_thread", TraceType.AGENTS, "thread_create"), - ("azure.ai.projects.operations", "AgentsOperations", "create_message", TraceType.AGENTS, "message_create"), - ("azure.ai.projects.operations", "AgentsOperations", "create_run", TraceType.AGENTS, "create_run"), - ( - "azure.ai.projects.operations", - "AgentsOperations", - "create_and_process_run", - TraceType.AGENTS, - "create_and_process_run", - ), - ( - "azure.ai.projects.operations", - "AgentsOperations", - "submit_tool_outputs_to_run", - TraceType.AGENTS, - "submit_tool_outputs_to_run", - ), - ( - "azure.ai.projects.operations", - "AgentsOperations", - "submit_tool_outputs_to_stream", - TraceType.AGENTS, - "submit_tool_outputs_to_stream", - ), - ( - "azure.ai.projects.operations", - "AgentsOperations", - "_handle_submit_tool_outputs", - TraceType.AGENTS, - "_handle_submit_tool_outputs", - ), - ("azure.ai.projects.operations", "AgentsOperations", "create_stream", TraceType.AGENTS, "create_stream"), - ("azure.ai.projects.operations", "AgentsOperations", "list_messages", TraceType.AGENTS, "list_messages"), - ("azure.ai.projects.models", "AgentRunStream", "__exit__", TraceType.AGENTS, "__exit__"), - ) - async_apis = ( - ("azure.ai.projects.aio.operations", "AgentsOperations", "create_agent", TraceType.AGENTS, "agent_create"), - ( - "azure.ai.projects.aio.operations", - "AgentsOperations", - "create_thread", - TraceType.AGENTS, - "agents_thread_create", - ), - ( - "azure.ai.projects.aio.operations", - "AgentsOperations", - "create_message", - TraceType.AGENTS, - "agents_thread_message", - ), - ("azure.ai.projects.aio.operations", "AgentsOperations", "create_run", TraceType.AGENTS, "create_run"), - ( - "azure.ai.projects.aio.operations", - "AgentsOperations", - "create_and_process_run", - TraceType.AGENTS, - "create_and_process_run", - ), - ( - "azure.ai.projects.aio.operations", - "AgentsOperations", - "submit_tool_outputs_to_run", - TraceType.AGENTS, - "submit_tool_outputs_to_run", - ), - ( - "azure.ai.projects.aio.operations", - "AgentsOperations", - "submit_tool_outputs_to_stream", - TraceType.AGENTS, - "submit_tool_outputs_to_stream", - ), - ( - "azure.ai.projects.aio.operations", - "AgentsOperations", - "_handle_submit_tool_outputs", - TraceType.AGENTS, - "_handle_submit_tool_outputs", - ), - ( - "azure.ai.projects.aio.operations", - "AgentsOperations", - "create_stream", - TraceType.AGENTS, - "create_stream", - ), - ( - "azure.ai.projects.aio.operations", - "AgentsOperations", - "list_messages", - TraceType.AGENTS, - "list_messages", - ), - ("azure.ai.projects.models", "AsyncAgentRunStream", "__aexit__", TraceType.AGENTS, "__aexit__"), - ) - return sync_apis, async_apis - - def _agents_api_list(self): - sync_apis, async_apis = self._agents_apis() - yield sync_apis, self._inject_sync - yield async_apis, self._inject_async - - def _generate_api_and_injector(self, apis): - for api, injector in apis: - for module_name, class_name, method_name, trace_type, name in api: - try: - module = importlib.import_module(module_name) - api = getattr(module, class_name) - if hasattr(api, method_name): - yield api, method_name, trace_type, injector, name - except AttributeError as e: - # Log the attribute exception with the missing class information - logging.warning( - "AttributeError: The module '%s' does not have the class '%s'. %s", - module_name, - class_name, - str(e), - ) - except Exception as e: # pylint: disable=broad-except - # Log other exceptions as a warning, as we're not sure what they might be - logging.warning("An unexpected error occurred: '%s'", str(e)) - - def _available_agents_apis_and_injectors(self): - """ - Generates a sequence of tuples containing Agents API classes, method names, and - corresponding injector functions. - - :return: A generator yielding tuples. - :rtype: tuple - """ - yield from self._generate_api_and_injector(self._agents_api_list()) - - def _instrument_agents(self, enable_content_tracing: bool = False): - """This function modifies the methods of the Agents API classes to - inject logic before calling the original methods. - The original methods are stored as _original attributes of the methods. - - :param enable_content_tracing: Indicates whether tracing of message content should be enabled. - This also controls whether function call tool function names, - parameter names and parameter values are traced. - :type enable_content_tracing: bool - """ - # pylint: disable=W0603 - global _agents_traces_enabled - global _trace_agents_content - if _agents_traces_enabled: - raise RuntimeError("Traces already started for AI Agents") - _agents_traces_enabled = True - _trace_agents_content = enable_content_tracing - for ( - api, - method, - trace_type, - injector, - name, - ) in self._available_agents_apis_and_injectors(): - # Check if the method of the api class has already been modified - if not hasattr(getattr(api, method), "_original"): - setattr(api, method, injector(getattr(api, method), trace_type, name)) - - def _uninstrument_agents(self): - """This function restores the original methods of the Agents API classes - by assigning them back from the _original attributes of the modified methods. - """ - # pylint: disable=W0603 - global _agents_traces_enabled - global _trace_agents_content - _trace_agents_content = False - for api, method, _, _, _ in self._available_agents_apis_and_injectors(): - if hasattr(getattr(api, method), "_original"): - setattr(api, method, getattr(getattr(api, method), "_original")) - _agents_traces_enabled = False - - def _is_instrumented(self): - """This function returns True if Agents API has already been instrumented - for tracing and False if it has not been instrumented. - - :return: A value indicating whether the Agents API is currently instrumented or not. - :rtype: bool - """ - return _agents_traces_enabled - - def _set_enable_content_recording(self, enable_content_recording: bool = False) -> None: - """This function sets the content recording value. - - :param enable_content_recording: Indicates whether tracing of message content should be enabled. - This also controls whether function call tool function names, - parameter names and parameter values are traced. - :type enable_content_recording: bool - """ - global _trace_agents_content # pylint: disable=W0603 - _trace_agents_content = enable_content_recording - - def _is_content_recording_enabled(self) -> bool: - """This function gets the content recording value. - - :return: A bool value indicating whether content tracing is enabled. - :rtype bool - """ - return _trace_agents_content - - -class _AgentEventHandlerTraceWrapper(AgentEventHandler): - def __init__( - self, - instrumentor: _AIAgentsInstrumentorPreview, - span: "AbstractSpan", - inner_handler: Optional[AgentEventHandler] = None, - ): - super().__init__() - self.span = span - self.inner_handler = inner_handler - self.ended = False - self.last_run: Optional[ThreadRun] = None - self.last_message: Optional[ThreadMessage] = None - self.instrumentor = instrumentor - - def on_message_delta(self, delta: "MessageDeltaChunk") -> None: - if self.inner_handler: - self.inner_handler.on_message_delta(delta) - - def on_thread_message(self, message: "ThreadMessage") -> None: - if self.inner_handler: - self.inner_handler.on_thread_message(message) - - if message.status in {"completed", "incomplete"}: - self.last_message = message - - def on_thread_run(self, run: "ThreadRun") -> None: - if self.inner_handler: - self.inner_handler.on_thread_run(run) - self.last_run = run - - def on_run_step(self, step: "RunStep") -> None: - if self.inner_handler: - self.inner_handler.on_run_step(step) - - if step.status == RunStepStatus.IN_PROGRESS: - return - - # todo - report errors for failure statuses here and in run ? - if step.type == "tool_calls" and isinstance(step.step_details, RunStepToolCallDetails): - self.instrumentor._add_tool_assistant_message_event( # pylint: disable=protected-access # pyright: ignore [reportFunctionMemberAccess] - self.span, step - ) - elif step.type == "message_creation" and step.status == RunStepStatus.COMPLETED: - self.instrumentor.add_thread_message_event(self.span, cast(ThreadMessage, self.last_message), step.usage) - self.last_message = None - - def on_run_step_delta(self, delta: "RunStepDeltaChunk") -> None: - if self.inner_handler: - self.inner_handler.on_run_step_delta(delta) - - def on_error(self, data: str) -> None: - if self.inner_handler: - self.inner_handler.on_error(data) - - def on_done(self) -> None: - if self.inner_handler: - self.inner_handler.on_done() - # it could be called multiple tines (for each step) __exit__ - - def on_unhandled_event(self, event_type: str, event_data: Any) -> None: - if self.inner_handler: - self.inner_handler.on_unhandled_event(event_type, event_data) - - def __exit__(self, exc_type, exc_val, exc_tb): - if not self.ended: - self.ended = True - self.instrumentor.set_end_run(self.span, self.last_run) - - if self.last_run and self.last_run.last_error: - self.span.span_instance.set_status( - StatusCode.ERROR, # pyright: ignore [reportPossiblyUnboundVariable] - self.last_run.last_error.message, - ) - self.span.add_attribute(ERROR_TYPE, self.last_run.last_error.code) - - self.span.__exit__(exc_type, exc_val, exc_tb) - self.span.finish() - - -class _AsyncAgentEventHandlerTraceWrapper(AsyncAgentEventHandler): - def __init__( - self, - instrumentor: _AIAgentsInstrumentorPreview, - span: "AbstractSpan", - inner_handler: Optional[AsyncAgentEventHandler] = None, - ): - super().__init__() - self.span = span - self.inner_handler = inner_handler - self.ended = False - self.last_run: Optional[ThreadRun] = None - self.last_message: Optional[ThreadMessage] = None - self.instrumentor = instrumentor - - async def on_message_delta(self, delta: "MessageDeltaChunk") -> None: # type: ignore[func-returns-value] - if self.inner_handler: - await self.inner_handler.on_message_delta(delta) - - async def on_thread_message(self, message: "ThreadMessage") -> None: # type: ignore[func-returns-value] - if self.inner_handler: - await self.inner_handler.on_thread_message(message) - - if message.status in {"completed", "incomplete"}: - self.last_message = message - - async def on_thread_run(self, run: "ThreadRun") -> None: # type: ignore[func-returns-value] - if self.inner_handler: - await self.inner_handler.on_thread_run(run) - self.last_run = run - - async def on_run_step(self, step: "RunStep") -> None: # type: ignore[func-returns-value] - if self.inner_handler: - await self.inner_handler.on_run_step(step) - - if step.status == RunStepStatus.IN_PROGRESS: - return - - # todo - report errors for failure statuses here and in run ? - if step.type == "tool_calls" and isinstance(step.step_details, RunStepToolCallDetails): - self.instrumentor._add_tool_assistant_message_event( # pylint: disable=protected-access # pyright: ignore [reportFunctionMemberAccess] - self.span, step - ) - elif step.type == "message_creation" and step.status == RunStepStatus.COMPLETED: - self.instrumentor.add_thread_message_event(self.span, cast(ThreadMessage, self.last_message), step.usage) - self.last_message = None - - async def on_run_step_delta(self, delta: "RunStepDeltaChunk") -> None: # type: ignore[func-returns-value] - if self.inner_handler: - await self.inner_handler.on_run_step_delta(delta) - - async def on_error(self, data: str) -> None: # type: ignore[func-returns-value] - if self.inner_handler: - await self.inner_handler.on_error(data) - - async def on_done(self) -> None: # type: ignore[func-returns-value] - if self.inner_handler: - await self.inner_handler.on_done() - # it could be called multiple tines (for each step) __exit__ - - async def on_unhandled_event(self, event_type: str, event_data: Any) -> None: # type: ignore[func-returns-value] - if self.inner_handler: - await self.inner_handler.on_unhandled_event(event_type, event_data) - - def __aexit__(self, exc_type, exc_val, exc_tb): - if not self.ended: - self.ended = True - self.instrumentor.set_end_run(self.span, self.last_run) - - if self.last_run and self.last_run.last_error: - self.span.set_status( - StatusCode.ERROR, # pyright: ignore [reportPossiblyUnboundVariable] - self.last_run.last_error.message, - ) - self.span.add_attribute(ERROR_TYPE, self.last_run.last_error.code) - - self.span.__exit__(exc_type, exc_val, exc_tb) - self.span.finish() diff --git a/sdk/ai/azure-ai-projects/azure/ai/projects/telemetry/agents/_utils.py b/sdk/ai/azure-ai-projects/azure/ai/projects/telemetry/agents/_utils.py deleted file mode 100644 index bdc18e1381e8..000000000000 --- a/sdk/ai/azure-ai-projects/azure/ai/projects/telemetry/agents/_utils.py +++ /dev/null @@ -1,139 +0,0 @@ -# ------------------------------------ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. -# ------------------------------------ - -from enum import Enum -from typing import Optional - -from azure.core.tracing import AbstractSpan, SpanKind # type: ignore -from azure.core.settings import settings # type: ignore - -try: - from opentelemetry.trace import StatusCode, Span # noqa: F401 # pylint: disable=unused-import - - _span_impl_type = settings.tracing_implementation() # pylint: disable=not-callable -except ModuleNotFoundError: - _span_impl_type = None - - -GEN_AI_MESSAGE_ID = "gen_ai.message.id" -GEN_AI_MESSAGE_STATUS = "gen_ai.message.status" -GEN_AI_THREAD_ID = "gen_ai.thread.id" -GEN_AI_THREAD_RUN_ID = "gen_ai.thread.run.id" -GEN_AI_AGENT_ID = "gen_ai.agent.id" -GEN_AI_AGENT_NAME = "gen_ai.agent.name" -GEN_AI_AGENT_DESCRIPTION = "gen_ai.agent.description" -GEN_AI_OPERATION_NAME = "gen_ai.operation.name" -GEN_AI_THREAD_RUN_STATUS = "gen_ai.thread.run.status" -GEN_AI_REQUEST_MODEL = "gen_ai.request.model" -GEN_AI_REQUEST_TEMPERATURE = "gen_ai.request.temperature" -GEN_AI_REQUEST_TOP_P = "gen_ai.request.top_p" -GEN_AI_REQUEST_MAX_INPUT_TOKENS = "gen_ai.request.max_input_tokens" -GEN_AI_REQUEST_MAX_OUTPUT_TOKENS = "gen_ai.request.max_output_tokens" -GEN_AI_RESPONSE_MODEL = "gen_ai.response.model" -GEN_AI_SYSTEM = "gen_ai.system" -SERVER_ADDRESS = "server.address" -AZ_AI_AGENT_SYSTEM = "az.ai.agents" -GEN_AI_TOOL_NAME = "gen_ai.tool.name" -GEN_AI_TOOL_CALL_ID = "gen_ai.tool.call.id" -GEN_AI_REQUEST_RESPONSE_FORMAT = "gen_ai.request.response_format" -GEN_AI_USAGE_INPUT_TOKENS = "gen_ai.usage.input_tokens" -GEN_AI_USAGE_OUTPUT_TOKENS = "gen_ai.usage.output_tokens" -GEN_AI_SYSTEM_MESSAGE = "gen_ai.system.message" -GEN_AI_EVENT_CONTENT = "gen_ai.event.content" -ERROR_TYPE = "error.type" - - -class OperationName(Enum): - CREATE_AGENT = "create_agent" - CREATE_THREAD = "create_thread" - CREATE_MESSAGE = "create_message" - START_THREAD_RUN = "start_thread_run" - EXECUTE_TOOL = "execute_tool" - LIST_MESSAGES = "list_messages" - SUBMIT_TOOL_OUTPUTS = "submit_tool_outputs" - PROCESS_THREAD_RUN = "process_thread_run" - - -def trace_tool_execution( - tool_call_id: str, - tool_name: str, - thread_id: Optional[str] = None, # TODO: would be nice to have this, but need to propagate somehow - agent_id: Optional[str] = None, # TODO: would be nice to have this, but need to propagate somehow - run_id: Optional[str] = None, # TODO: would be nice to have this, but need to propagate somehow -) -> "Optional[AbstractSpan]": - span = start_span( - OperationName.EXECUTE_TOOL, - server_address=None, - span_name=f"execute_tool {tool_name}", - thread_id=thread_id, - agent_id=agent_id, - run_id=run_id, - gen_ai_system=None, - ) # it's a client code execution, not GenAI span - if span is not None and span.span_instance.is_recording: - span.add_attribute(GEN_AI_TOOL_CALL_ID, tool_call_id) - span.add_attribute(GEN_AI_TOOL_NAME, tool_name) - - return span - - -def start_span( - operation_name: OperationName, - server_address: Optional[str], - span_name: Optional[str] = None, - thread_id: Optional[str] = None, - agent_id: Optional[str] = None, - run_id: Optional[str] = None, - model: Optional[str] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - max_prompt_tokens: Optional[int] = None, - max_completion_tokens: Optional[int] = None, - response_format: Optional[str] = None, - gen_ai_system: Optional[str] = AZ_AI_AGENT_SYSTEM, - kind: SpanKind = SpanKind.CLIENT, -) -> "Optional[AbstractSpan]": - if _span_impl_type is None: - return None - - span = _span_impl_type(name=span_name or operation_name.value, kind=kind) - - if span and span.span_instance.is_recording: - if gen_ai_system: - span.add_attribute(GEN_AI_SYSTEM, AZ_AI_AGENT_SYSTEM) - - span.add_attribute(GEN_AI_OPERATION_NAME, operation_name.value) - - if server_address: - span.add_attribute(SERVER_ADDRESS, server_address) - - if thread_id: - span.add_attribute(GEN_AI_THREAD_ID, thread_id) - - if agent_id: - span.add_attribute(GEN_AI_AGENT_ID, agent_id) - - if run_id: - span.add_attribute(GEN_AI_THREAD_RUN_ID, run_id) - - if model: - span.add_attribute(GEN_AI_REQUEST_MODEL, model) - - if temperature: - span.add_attribute(GEN_AI_REQUEST_TEMPERATURE, str(temperature)) - - if top_p: - span.add_attribute(GEN_AI_REQUEST_TOP_P, str(top_p)) - - if max_prompt_tokens: - span.add_attribute(GEN_AI_REQUEST_MAX_INPUT_TOKENS, max_prompt_tokens) - - if max_completion_tokens: - span.add_attribute(GEN_AI_REQUEST_MAX_OUTPUT_TOKENS, max_completion_tokens) - - if response_format: - span.add_attribute(GEN_AI_REQUEST_RESPONSE_FORMAT, response_format) - - return span diff --git a/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_azure_functions_async.py b/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_azure_functions_async.py index 3b4c6bd54a30..ba59380b4ff7 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_azure_functions_async.py +++ b/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_azure_functions_async.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_code_interpreter_async.py b/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_code_interpreter_async.py index 11e75c3a4849..480f13bb8092 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_code_interpreter_async.py +++ b/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_code_interpreter_async.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_code_interpreter_attachment_async.py b/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_code_interpreter_attachment_async.py index ad93d01bc0b5..b3abdb389978 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_code_interpreter_attachment_async.py +++ b/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_code_interpreter_attachment_async.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_code_interpreter_attachment_enterprise_search_async.py b/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_code_interpreter_attachment_enterprise_search_async.py index d0bba841aca6..0c0452971a4b 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_code_interpreter_attachment_enterprise_search_async.py +++ b/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_code_interpreter_attachment_enterprise_search_async.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_stream_eventhandler_with_functions_async.py b/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_stream_eventhandler_with_functions_async.py index ae314c9c459e..a354d71e8785 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_stream_eventhandler_with_functions_async.py +++ b/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_stream_eventhandler_with_functions_async.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_stream_eventhandler_with_toolset_async.py b/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_stream_eventhandler_with_toolset_async.py index 868f384faa97..2f96630c3948 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_stream_eventhandler_with_toolset_async.py +++ b/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_stream_eventhandler_with_toolset_async.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_vector_store_batch_enterprise_file_search_async.py b/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_vector_store_batch_enterprise_file_search_async.py index 21ef6f2d8b6c..8a13c4b4bdbc 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_vector_store_batch_enterprise_file_search_async.py +++ b/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_vector_store_batch_enterprise_file_search_async.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_vector_store_batch_file_search_async.py b/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_vector_store_batch_file_search_async.py index 5b846c10ef93..a43b7d98629f 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_vector_store_batch_file_search_async.py +++ b/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_vector_store_batch_file_search_async.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_vector_store_enterprise_file_search_async.py b/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_vector_store_enterprise_file_search_async.py index e63ae6629e4d..aeddb4e6a45c 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_vector_store_enterprise_file_search_async.py +++ b/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_vector_store_enterprise_file_search_async.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_vector_store_file_search_async.py b/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_vector_store_file_search_async.py index bb39337f5896..91856614d85e 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_vector_store_file_search_async.py +++ b/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_vector_store_file_search_async.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_with_file_search_attachment_async.py b/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_with_file_search_attachment_async.py index 320f73417c19..773f0ee7e4d7 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_with_file_search_attachment_async.py +++ b/sdk/ai/azure-ai-projects/samples/agents/async_samples/sample_agents_with_file_search_attachment_async.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/async_samples/user_async_functions.py b/sdk/ai/azure-ai-projects/samples/agents/async_samples/user_async_functions.py index 057d6a07fd4b..2033bcc6d368 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/async_samples/user_async_functions.py +++ b/sdk/ai/azure-ai-projects/samples/agents/async_samples/user_async_functions.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/multiagent/sample_agents_agent_team_custom_team_leader.py b/sdk/ai/azure-ai-projects/samples/agents/multiagent/sample_agents_agent_team_custom_team_leader.py index d2cbca871ae2..62a270799eee 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/multiagent/sample_agents_agent_team_custom_team_leader.py +++ b/sdk/ai/azure-ai-projects/samples/agents/multiagent/sample_agents_agent_team_custom_team_leader.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/multiagent/user_functions_with_traces.py b/sdk/ai/azure-ai-projects/samples/agents/multiagent/user_functions_with_traces.py index 1a4910b19d83..2c4f2377ddaf 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/multiagent/user_functions_with_traces.py +++ b/sdk/ai/azure-ai-projects/samples/agents/multiagent/user_functions_with_traces.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_azure_ai_search.py b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_azure_ai_search.py index 6783244c5d75..dbca4b5c2375 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_azure_ai_search.py +++ b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_azure_ai_search.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_azure_functions.py b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_azure_functions.py index ec3e5115450a..4f25baa96c20 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_azure_functions.py +++ b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_azure_functions.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_code_interpreter.py b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_code_interpreter.py index 0c0e64566991..c3c2a455832e 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_code_interpreter.py +++ b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_code_interpreter.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_code_interpreter_attachment_enterprise_search.py b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_code_interpreter_attachment_enterprise_search.py index 51af96b959c5..ae45350938aa 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_code_interpreter_attachment_enterprise_search.py +++ b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_code_interpreter_attachment_enterprise_search.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_logic_apps.py b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_logic_apps.py index f2f3d32ec35f..89d56da186ca 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_logic_apps.py +++ b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_logic_apps.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_openapi.py b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_openapi.py index a8ba498ad38b..01755554367b 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_openapi.py +++ b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_openapi.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_stream_eventhandler_with_functions.py b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_stream_eventhandler_with_functions.py index a00c37187cdf..bcc9662eaa05 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_stream_eventhandler_with_functions.py +++ b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_stream_eventhandler_with_functions.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_stream_eventhandler_with_toolset.py b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_stream_eventhandler_with_toolset.py index e9edbb9c812e..342a0f031ec1 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_stream_eventhandler_with_toolset.py +++ b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_stream_eventhandler_with_toolset.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_stream_iteration_with_file_search.py b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_stream_iteration_with_file_search.py index 9e39b0b0534b..5958341abcb1 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_stream_iteration_with_file_search.py +++ b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_stream_iteration_with_file_search.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_stream_with_base_override_eventhandler.py b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_stream_with_base_override_eventhandler.py index f723f38e5d6c..e6d1262bd996 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_stream_with_base_override_eventhandler.py +++ b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_stream_with_base_override_eventhandler.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_vector_store_batch_file_search.py b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_vector_store_batch_file_search.py index ed49e3c6a0b7..c44cd09f1a9f 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_vector_store_batch_file_search.py +++ b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_vector_store_batch_file_search.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_with_code_interpreter_file_attachment.py b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_with_code_interpreter_file_attachment.py index 8bdac9039a5c..a5311724ffae 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_with_code_interpreter_file_attachment.py +++ b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_with_code_interpreter_file_attachment.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # ------------------------------------ # Copyright (c) Microsoft Corporation. diff --git a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_with_file_search_attachment.py b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_with_file_search_attachment.py index 2a59a3e79307..3112a8be0ee8 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/sample_agents_with_file_search_attachment.py +++ b/sdk/ai/azure-ai-projects/samples/agents/sample_agents_with_file_search_attachment.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/agents/user_functions.py b/sdk/ai/azure-ai-projects/samples/agents/user_functions.py index e0df6f3515cf..cb1e3d9cf43d 100644 --- a/sdk/ai/azure-ai-projects/samples/agents/user_functions.py +++ b/sdk/ai/azure-ai-projects/samples/agents/user_functions.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/connections/async_samples/sample_inference_client_from_connection_async.py b/sdk/ai/azure-ai-projects/samples/connections/async_samples/sample_inference_client_from_connection_async.py index 20c9d5edb311..1c0a85c59d0f 100644 --- a/sdk/ai/azure-ai-projects/samples/connections/async_samples/sample_inference_client_from_connection_async.py +++ b/sdk/ai/azure-ai-projects/samples/connections/async_samples/sample_inference_client_from_connection_async.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/connections/sample_inference_client_from_connection.py b/sdk/ai/azure-ai-projects/samples/connections/sample_inference_client_from_connection.py index 3b8dafd613b4..3196eaf90d1d 100644 --- a/sdk/ai/azure-ai-projects/samples/connections/sample_inference_client_from_connection.py +++ b/sdk/ai/azure-ai-projects/samples/connections/sample_inference_client_from_connection.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/samples/inference/async_samples/sample_image_embeddings_with_azure_ai_inference_client_async.py b/sdk/ai/azure-ai-projects/samples/inference/async_samples/sample_image_embeddings_with_azure_ai_inference_client_async.py index 6d6d4179cd60..b9d7036ad44d 100644 --- a/sdk/ai/azure-ai-projects/samples/inference/async_samples/sample_image_embeddings_with_azure_ai_inference_client_async.py +++ b/sdk/ai/azure-ai-projects/samples/inference/async_samples/sample_image_embeddings_with_azure_ai_inference_client_async.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/sdk_packaging.toml b/sdk/ai/azure-ai-projects/sdk_packaging.toml new file mode 100644 index 000000000000..e7687fdae93b --- /dev/null +++ b/sdk/ai/azure-ai-projects/sdk_packaging.toml @@ -0,0 +1,2 @@ +[packaging] +auto_update = false \ No newline at end of file diff --git a/sdk/ai/azure-ai-projects/setup.py b/sdk/ai/azure-ai-projects/setup.py index 3c806ef0e972..37a6290f3338 100644 --- a/sdk/ai/azure-ai-projects/setup.py +++ b/sdk/ai/azure-ai-projects/setup.py @@ -13,17 +13,7 @@ PACKAGE_NAME = "azure-ai-projects" -PACKAGE_PPRINT_NAME = "Azure AI Projects" - -PIPY_LONG_DESCRIPTION_BEGIN = "" -PIPY_LONG_DESCRIPTION_END = "" -LINKS_DIVIDER = "" - -GITHUB_URL = f"https://aka.ms/azsdk/azure-ai-projects/python/code" - -# Define the regular expression pattern to match links in the format [section name](#section_header) -pattern = re.compile(r"\[([^\]]+)\]\(#([^\)]+)\)") - +PACKAGE_PPRINT_NAME = "Azure Ai Projects" # a-b-c => a/b/c package_folder_path = PACKAGE_NAME.replace("-", "/") @@ -36,36 +26,17 @@ raise RuntimeError("Cannot find version information") -long_description = "" - -# When you click the links in the Table of Content which has the format of {URL/#section_header}, you are supposed to be redirected to the section header. -# However, this is not supported when the README is rendered in pypi.org. The README doesn't render with id={section_header} in HTML. -# To resolve this broken link, we make the long description to have top of the README content, the Table of Content, and the links at the bottom of the README -# And replace the links in Table of Content to redirect to github.com. -with open("README.md", "r") as f: - readme_content = f.read() - start_index = readme_content.find(PIPY_LONG_DESCRIPTION_BEGIN) + len(PIPY_LONG_DESCRIPTION_BEGIN) - end_index = readme_content.find(PIPY_LONG_DESCRIPTION_END) - long_description = readme_content[start_index:end_index].strip() - long_description = long_description.replace("{{package_name}}", PACKAGE_PPRINT_NAME) - long_description = re.sub(pattern, rf"[\1]({GITHUB_URL})", long_description) - links_index = readme_content.find(LINKS_DIVIDER) - long_description += "\n\n" + readme_content[links_index:].strip() - -with open("CHANGELOG.md", "r") as f: - long_description += "\n\n" + f.read() - setup( name=PACKAGE_NAME, version=version, description="Microsoft {} Client Library for Python".format(PACKAGE_PPRINT_NAME), - long_description=long_description, + 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/ai/azure-ai-projects", - keywords="azure sdk, azure, ai, agents, foundry, inference, chat completion, project, evaluation", + url="https://github.com/Azure/azure-sdk-for-python/tree/main/sdk", + keywords="azure, azure sdk", classifiers=[ "Development Status :: 4 - Beta", "Programming Language :: Python", @@ -76,9 +47,7 @@ "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", - "Programming Language :: Python :: 3.13", "License :: OSI Approved :: MIT License", - "Topic :: Scientific/Engineering :: Artificial Intelligence", ], zip_safe=False, packages=find_packages( @@ -96,7 +65,7 @@ install_requires=[ "isodate>=0.6.1", "azure-core>=1.30.0", - "typing-extensions>=4.12.2", + "typing-extensions>=4.6.0", ], python_requires=">=3.8", ) diff --git a/sdk/ai/azure-ai-projects/tests/agents/test_agents_client.py b/sdk/ai/azure-ai-projects/tests/agents/test_agents_client.py index 6862bad9744b..361cde03a1d4 100644 --- a/sdk/ai/azure-ai-projects/tests/agents/test_agents_client.py +++ b/sdk/ai/azure-ai-projects/tests/agents/test_agents_client.py @@ -1,4 +1,4 @@ -# pylint: disable=too-many-lines +# pylint: disable=too-many-lines,line-too-long,useless-suppression # # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. @@ -3132,4 +3132,4 @@ def test_client_with_thread_messages(self, **kwargs): assert client.agents.delete_agent(agent.id).deleted, "The agent was not deleted" messages = client.agents.list_messages(thread_id=thread.id) - assert len(messages.data), "The data from the agent was not received." + assert len(messages.data), "The data from the agent was not received." \ No newline at end of file diff --git a/sdk/ai/azure-ai-projects/tests/agents/test_agents_client_async.py b/sdk/ai/azure-ai-projects/tests/agents/test_agents_client_async.py index a1dcb52ce881..394bdd909c70 100644 --- a/sdk/ai/azure-ai-projects/tests/agents/test_agents_client_async.py +++ b/sdk/ai/azure-ai-projects/tests/agents/test_agents_client_async.py @@ -1,4 +1,4 @@ -# pylint: disable=too-many-lines +# pylint: disable=too-many-lines,line-too-long,useless-suppression # # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. @@ -3037,4 +3037,4 @@ async def test_negative_create_delete_agent(self, **kwargs): # close client and confirm an exception was caught await client.close() assert exception_caught - """ + """ \ No newline at end of file diff --git a/sdk/ai/azure-ai-projects/tests/agents/test_vector_store.py b/sdk/ai/azure-ai-projects/tests/agents/test_vector_store.py index 5796e9a7fa3f..51998f9a7a68 100644 --- a/sdk/ai/azure-ai-projects/tests/agents/test_vector_store.py +++ b/sdk/ai/azure-ai-projects/tests/agents/test_vector_store.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/tests/agents/user_functions.py b/sdk/ai/azure-ai-projects/tests/agents/user_functions.py index 0dfada80689b..883fd2fa8e32 100644 --- a/sdk/ai/azure-ai-projects/tests/agents/user_functions.py +++ b/sdk/ai/azure-ai-projects/tests/agents/user_functions.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/tests/conftest.py b/sdk/ai/azure-ai-projects/tests/conftest.py index 0c9c0567346c..dfa929b049af 100644 --- a/sdk/ai/azure-ai-projects/tests/conftest.py +++ b/sdk/ai/azure-ai-projects/tests/conftest.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/tests/connections/connection_test_base.py b/sdk/ai/azure-ai-projects/tests/connections/connection_test_base.py index 7096f87493be..a25e9b98ea41 100644 --- a/sdk/ai/azure-ai-projects/tests/connections/connection_test_base.py +++ b/sdk/ai/azure-ai-projects/tests/connections/connection_test_base.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/tests/connections/test_connections_unit_tests.py b/sdk/ai/azure-ai-projects/tests/connections/test_connections_unit_tests.py index dc28671bb667..d190a090c3d6 100644 --- a/sdk/ai/azure-ai-projects/tests/connections/test_connections_unit_tests.py +++ b/sdk/ai/azure-ai-projects/tests/connections/test_connections_unit_tests.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/tests/evaluation/evaluation_test_base.py b/sdk/ai/azure-ai-projects/tests/evaluation/evaluation_test_base.py index 8010ef7effcc..e3addb83f57f 100644 --- a/sdk/ai/azure-ai-projects/tests/evaluation/evaluation_test_base.py +++ b/sdk/ai/azure-ai-projects/tests/evaluation/evaluation_test_base.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/tests/inference/inference_test_base.py b/sdk/ai/azure-ai-projects/tests/inference/inference_test_base.py index f654702c6634..cafed9389223 100644 --- a/sdk/ai/azure-ai-projects/tests/inference/inference_test_base.py +++ b/sdk/ai/azure-ai-projects/tests/inference/inference_test_base.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/tests/inference/test_inference.py b/sdk/ai/azure-ai-projects/tests/inference/test_inference.py index 3860c7161917..b6567ae3574c 100644 --- a/sdk/ai/azure-ai-projects/tests/inference/test_inference.py +++ b/sdk/ai/azure-ai-projects/tests/inference/test_inference.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/tests/inference/test_inference_async.py b/sdk/ai/azure-ai-projects/tests/inference/test_inference_async.py index 11dec67b0896..d3c8b1d7ac65 100644 --- a/sdk/ai/azure-ai-projects/tests/inference/test_inference_async.py +++ b/sdk/ai/azure-ai-projects/tests/inference/test_inference_async.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/tests/telemetry/telemetry_test_base.py b/sdk/ai/azure-ai-projects/tests/telemetry/telemetry_test_base.py index 732baa2acc16..c755ec1dd6ba 100644 --- a/sdk/ai/azure-ai-projects/tests/telemetry/telemetry_test_base.py +++ b/sdk/ai/azure-ai-projects/tests/telemetry/telemetry_test_base.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/tests/telemetry/test_ai_agents_instrumentor.py b/sdk/ai/azure-ai-projects/tests/telemetry/test_ai_agents_instrumentor.py index cb73287e472f..b97fbd741d61 100644 --- a/sdk/ai/azure-ai-projects/tests/telemetry/test_ai_agents_instrumentor.py +++ b/sdk/ai/azure-ai-projects/tests/telemetry/test_ai_agents_instrumentor.py @@ -1,4 +1,4 @@ -# pylint: disable=too-many-lines +# pylint: disable=too-many-lines,line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. @@ -1048,4 +1048,4 @@ def on_done(self) -> None: print("Stream completed.") def on_unhandled_event(self, event_type: str, event_data: Any) -> None: - print(f"Unhandled Event Type: {event_type}, Data: {event_data}") + print(f"Unhandled Event Type: {event_type}, Data: {event_data}") \ No newline at end of file diff --git a/sdk/ai/azure-ai-projects/tests/telemetry/test_ai_agents_instrumentor_async.py b/sdk/ai/azure-ai-projects/tests/telemetry/test_ai_agents_instrumentor_async.py index 81fd11a2615c..d82fcd32c502 100644 --- a/sdk/ai/azure-ai-projects/tests/telemetry/test_ai_agents_instrumentor_async.py +++ b/sdk/ai/azure-ai-projects/tests/telemetry/test_ai_agents_instrumentor_async.py @@ -1,3 +1,4 @@ +# pylint: disable=line-too-long,useless-suppression # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-projects/tsp-location.yaml b/sdk/ai/azure-ai-projects/tsp-location.yaml index 2e8d2adc8a0a..84c6c49918d6 100644 --- a/sdk/ai/azure-ai-projects/tsp-location.yaml +++ b/sdk/ai/azure-ai-projects/tsp-location.yaml @@ -1,4 +1,4 @@ directory: specification/ai/Azure.AI.Projects -commit: 6e507701253408679175e95176995c437f8e00d4 +commit: f443736b9a9493d983e010fcf2b78dcc58ef5344 repo: Azure/azure-rest-api-specs additionalDirectories: