diff --git a/sdk/ai/azure-ai-inference/_meta.json b/sdk/ai/azure-ai-inference/_meta.json new file mode 100644 index 000000000000..f38f24453b26 --- /dev/null +++ b/sdk/ai/azure-ai-inference/_meta.json @@ -0,0 +1,6 @@ +{ + "commit": "c45b367efe1b10e82c6ac75fed19e0a91f9e3a8a", + "repository_url": "https://github.com/Azure/azure-rest-api-specs", + "typespec_src": "specification/ai/ModelClient", + "@azure-tools/typespec-python": "0.37.2" +} \ No newline at end of file diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/__init__.py b/sdk/ai/azure-ai-inference/azure/ai/inference/__init__.py index ff62b276a309..b7537d16cab3 100644 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/__init__.py +++ b/sdk/ai/azure-ai-inference/azure/ai/inference/__init__.py @@ -5,24 +5,32 @@ # Code generated by Microsoft (R) Python Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- +# pylint: disable=wrong-import-position -from ._patch import ChatCompletionsClient -from ._patch import EmbeddingsClient -from ._patch import ImageEmbeddingsClient +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from ._patch import * # pylint: disable=unused-wildcard-import + +from ._client import ChatCompletionsClient # type: ignore +from ._client import EmbeddingsClient # type: ignore +from ._client import ImageEmbeddingsClient # type: ignore from ._version import VERSION __version__ = VERSION - -from ._patch import load_client +try: + from ._patch import __all__ as _patch_all + from ._patch import * +except ImportError: + _patch_all = [] from ._patch import patch_sdk as _patch_sdk __all__ = [ - "load_client", "ChatCompletionsClient", "EmbeddingsClient", "ImageEmbeddingsClient", ] - +__all__.extend([p for p in _patch_all if p not in __all__]) # pyright: ignore _patch_sdk() diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/_client.py b/sdk/ai/azure-ai-inference/azure/ai/inference/_client.py index 25f4b3746e76..5e73e91ea2b2 100644 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/_client.py +++ b/sdk/ai/azure-ai-inference/azure/ai/inference/_client.py @@ -28,11 +28,10 @@ from ._serialization import Deserializer, Serializer if TYPE_CHECKING: - # pylint: disable=unused-import,ungrouped-imports from azure.core.credentials import TokenCredential -class ChatCompletionsClient(ChatCompletionsClientOperationsMixin): # pylint: disable=client-accepts-api-version-keyword +class ChatCompletionsClient(ChatCompletionsClientOperationsMixin): """ChatCompletionsClient. :param endpoint: Service host. Required. @@ -110,7 +109,7 @@ def __exit__(self, *exc_details: Any) -> None: self._client.__exit__(*exc_details) -class EmbeddingsClient(EmbeddingsClientOperationsMixin): # pylint: disable=client-accepts-api-version-keyword +class EmbeddingsClient(EmbeddingsClientOperationsMixin): """EmbeddingsClient. :param endpoint: Service host. Required. @@ -188,7 +187,7 @@ def __exit__(self, *exc_details: Any) -> None: self._client.__exit__(*exc_details) -class ImageEmbeddingsClient(ImageEmbeddingsClientOperationsMixin): # pylint: disable=client-accepts-api-version-keyword +class ImageEmbeddingsClient(ImageEmbeddingsClientOperationsMixin): """ImageEmbeddingsClient. :param endpoint: Service host. Required. diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/_configuration.py b/sdk/ai/azure-ai-inference/azure/ai/inference/_configuration.py index 9393659fb910..8158dd310196 100644 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/_configuration.py +++ b/sdk/ai/azure-ai-inference/azure/ai/inference/_configuration.py @@ -14,11 +14,10 @@ from ._version import VERSION if TYPE_CHECKING: - # pylint: disable=unused-import,ungrouped-imports from azure.core.credentials import TokenCredential -class ChatCompletionsClientConfiguration: # pylint: disable=too-many-instance-attributes,name-too-long +class ChatCompletionsClientConfiguration: # pylint: disable=too-many-instance-attributes """Configuration for ChatCompletionsClient. Note that all parameters used to create this instance are saved as instance @@ -75,7 +74,7 @@ def _configure(self, **kwargs: Any) -> None: self.authentication_policy = self._infer_policy(**kwargs) -class EmbeddingsClientConfiguration: # pylint: disable=too-many-instance-attributes,name-too-long +class EmbeddingsClientConfiguration: # pylint: disable=too-many-instance-attributes """Configuration for EmbeddingsClient. Note that all parameters used to create this instance are saved as instance @@ -132,7 +131,7 @@ def _configure(self, **kwargs: Any) -> None: self.authentication_policy = self._infer_policy(**kwargs) -class ImageEmbeddingsClientConfiguration: # pylint: disable=too-many-instance-attributes,name-too-long +class ImageEmbeddingsClientConfiguration: # pylint: disable=too-many-instance-attributes """Configuration for ImageEmbeddingsClient. Note that all parameters used to create this instance are saved as instance diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/_model_base.py b/sdk/ai/azure-ai-inference/azure/ai/inference/_model_base.py index 53305e2213a7..7f73b97b23ef 100644 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/_model_base.py +++ b/sdk/ai/azure-ai-inference/azure/ai/inference/_model_base.py @@ -1,10 +1,11 @@ +# pylint: disable=too-many-lines # coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- -# pylint: disable=protected-access, arguments-differ, signature-differs, broad-except +# pylint: disable=protected-access, broad-except import copy import calendar @@ -19,6 +20,7 @@ import email.utils from datetime import datetime, date, time, timedelta, timezone from json import JSONEncoder +import xml.etree.ElementTree as ET from typing_extensions import Self import isodate from azure.core.exceptions import DeserializationError @@ -123,7 +125,7 @@ def _serialize_datetime(o, format: typing.Optional[str] = None): def _is_readonly(p): try: - return p._visibility == ["read"] # pylint: disable=protected-access + return p._visibility == ["read"] except AttributeError: return False @@ -286,6 +288,12 @@ def _deserialize_decimal(attr): return decimal.Decimal(str(attr)) +def _deserialize_int_as_str(attr): + if isinstance(attr, int): + return attr + return int(attr) + + _DESERIALIZE_MAPPING = { datetime: _deserialize_datetime, date: _deserialize_date, @@ -307,9 +315,11 @@ def _deserialize_decimal(attr): def get_deserializer(annotation: typing.Any, rf: typing.Optional["_RestField"] = None): + if annotation is int and rf and rf._format == "str": + return _deserialize_int_as_str if rf and rf._format: return _DESERIALIZE_MAPPING_WITHFORMAT.get(rf._format) - return _DESERIALIZE_MAPPING.get(annotation) + return _DESERIALIZE_MAPPING.get(annotation) # pyright: ignore def _get_type_alias_type(module_name: str, alias_name: str): @@ -441,6 +451,10 @@ def _serialize(o, format: typing.Optional[str] = None): # pylint: disable=too-m return float(o) if isinstance(o, enum.Enum): return o.value + if isinstance(o, int): + if format == "str": + return str(o) + return o try: # First try datetime.datetime return _serialize_datetime(o, format) @@ -471,6 +485,8 @@ def _create_value(rf: typing.Optional["_RestField"], value: typing.Any) -> typin return value if rf._is_model: return _deserialize(rf._type, value) + if isinstance(value, ET.Element): + value = _deserialize(rf._type, value) return _serialize(value, rf._format) @@ -489,10 +505,58 @@ def __init__(self, *args: typing.Any, **kwargs: typing.Any) -> None: for rest_field in self._attr_to_rest_field.values() if rest_field._default is not _UNSET } - if args: - dict_to_pass.update( - {k: _create_value(_get_rest_field(self._attr_to_rest_field, k), v) for k, v in args[0].items()} - ) + if args: # pylint: disable=too-many-nested-blocks + if isinstance(args[0], ET.Element): + existed_attr_keys = [] + model_meta = getattr(self, "_xml", {}) + + for rf in self._attr_to_rest_field.values(): + prop_meta = getattr(rf, "_xml", {}) + xml_name = prop_meta.get("name", rf._rest_name) + xml_ns = prop_meta.get("ns", model_meta.get("ns", None)) + if xml_ns: + xml_name = "{" + xml_ns + "}" + xml_name + + # attribute + if prop_meta.get("attribute", False) and args[0].get(xml_name) is not None: + existed_attr_keys.append(xml_name) + dict_to_pass[rf._rest_name] = _deserialize(rf._type, args[0].get(xml_name)) + continue + + # unwrapped element is array + if prop_meta.get("unwrapped", False): + # unwrapped array could either use prop items meta/prop meta + if prop_meta.get("itemsName"): + xml_name = prop_meta.get("itemsName") + xml_ns = prop_meta.get("itemNs") + if xml_ns: + xml_name = "{" + xml_ns + "}" + xml_name + items = args[0].findall(xml_name) # pyright: ignore + if len(items) > 0: + existed_attr_keys.append(xml_name) + dict_to_pass[rf._rest_name] = _deserialize(rf._type, items) + continue + + # text element is primitive type + if prop_meta.get("text", False): + if args[0].text is not None: + dict_to_pass[rf._rest_name] = _deserialize(rf._type, args[0].text) + continue + + # wrapped element could be normal property or array, it should only have one element + item = args[0].find(xml_name) + if item is not None: + existed_attr_keys.append(xml_name) + dict_to_pass[rf._rest_name] = _deserialize(rf._type, item) + + # rest thing is additional properties + for e in args[0]: + if e.tag not in existed_attr_keys: + dict_to_pass[e.tag] = _convert_element(e) + else: + dict_to_pass.update( + {k: _create_value(_get_rest_field(self._attr_to_rest_field, k), v) for k, v in args[0].items()} + ) else: non_attr_kwargs = [k for k in kwargs if k not in self._attr_to_rest_field] if non_attr_kwargs: @@ -510,7 +574,7 @@ def __init__(self, *args: typing.Any, **kwargs: typing.Any) -> None: def copy(self) -> "Model": return Model(self.__dict__) - def __new__(cls, *args: typing.Any, **kwargs: typing.Any) -> Self: # pylint: disable=unused-argument + def __new__(cls, *args: typing.Any, **kwargs: typing.Any) -> Self: if f"{cls.__module__}.{cls.__qualname__}" not in cls._calculated: # we know the last nine classes in mro are going to be 'Model', '_MyMutableMapping', 'MutableMapping', # 'Mapping', 'Collection', 'Sized', 'Iterable', 'Container' and 'object' @@ -521,8 +585,8 @@ def __new__(cls, *args: typing.Any, **kwargs: typing.Any) -> Self: # pylint: di annotations = { k: v for mro_class in mros - if hasattr(mro_class, "__annotations__") # pylint: disable=no-member - for k, v in mro_class.__annotations__.items() # pylint: disable=no-member + if hasattr(mro_class, "__annotations__") + for k, v in mro_class.__annotations__.items() } for attr, rf in attr_to_rest_field.items(): rf._module = cls.__module__ @@ -537,31 +601,43 @@ def __new__(cls, *args: typing.Any, **kwargs: typing.Any) -> Self: # pylint: di def __init_subclass__(cls, discriminator: typing.Optional[str] = None) -> None: for base in cls.__bases__: - if hasattr(base, "__mapping__"): # pylint: disable=no-member - base.__mapping__[discriminator or cls.__name__] = cls # type: ignore # pylint: disable=no-member + if hasattr(base, "__mapping__"): + base.__mapping__[discriminator or cls.__name__] = cls # type: ignore @classmethod - def _get_discriminator(cls, exist_discriminators) -> typing.Optional[str]: + def _get_discriminator(cls, exist_discriminators) -> typing.Optional["_RestField"]: for v in cls.__dict__.values(): - if ( - isinstance(v, _RestField) and v._is_discriminator and v._rest_name not in exist_discriminators - ): # pylint: disable=protected-access - return v._rest_name # pylint: disable=protected-access + if isinstance(v, _RestField) and v._is_discriminator and v._rest_name not in exist_discriminators: + return v return None @classmethod def _deserialize(cls, data, exist_discriminators): - if not hasattr(cls, "__mapping__"): # pylint: disable=no-member + if not hasattr(cls, "__mapping__"): return cls(data) discriminator = cls._get_discriminator(exist_discriminators) - exist_discriminators.append(discriminator) - mapped_cls = cls.__mapping__.get(data.get(discriminator), cls) # pyright: ignore # pylint: disable=no-member - if mapped_cls == cls: + if discriminator is None: return cls(data) - return mapped_cls._deserialize(data, exist_discriminators) # pylint: disable=protected-access + exist_discriminators.append(discriminator._rest_name) + if isinstance(data, ET.Element): + model_meta = getattr(cls, "_xml", {}) + prop_meta = getattr(discriminator, "_xml", {}) + xml_name = prop_meta.get("name", discriminator._rest_name) + xml_ns = prop_meta.get("ns", model_meta.get("ns", None)) + if xml_ns: + xml_name = "{" + xml_ns + "}" + xml_name + + if data.get(xml_name) is not None: + discriminator_value = data.get(xml_name) + else: + discriminator_value = data.find(xml_name).text # pyright: ignore + else: + discriminator_value = data.get(discriminator._rest_name) + mapped_cls = cls.__mapping__.get(discriminator_value, cls) # pyright: ignore + return mapped_cls._deserialize(data, exist_discriminators) def as_dict(self, *, exclude_readonly: bool = False) -> typing.Dict[str, typing.Any]: - """Return a dict that can be JSONify using json.dump. + """Return a dict that can be turned into json using json.dump. :keyword bool exclude_readonly: Whether to remove the readonly properties. :returns: A dict JSON compatible object @@ -624,6 +700,8 @@ def _deserialize_dict( ): if obj is None: return obj + if isinstance(obj, ET.Element): + obj = {child.tag: child for child in obj} return {k: _deserialize(value_deserializer, v, module) for k, v in obj.items()} @@ -644,6 +722,8 @@ def _deserialize_sequence( ): if obj is None: return obj + if isinstance(obj, ET.Element): + obj = list(obj) return type(obj)(_deserialize(deserializer, entry, module) for entry in obj) @@ -654,12 +734,12 @@ def _sorted_annotations(types: typing.List[typing.Any]) -> typing.List[typing.An ) -def _get_deserialize_callable_from_annotation( # pylint: disable=R0911, R0915, R0912 +def _get_deserialize_callable_from_annotation( # pylint: disable=too-many-return-statements, too-many-branches annotation: typing.Any, module: typing.Optional[str], rf: typing.Optional["_RestField"] = None, ) -> typing.Optional[typing.Callable[[typing.Any], typing.Any]]: - if not annotation or annotation in [int, float]: + if not annotation: return None # is it a type alias? @@ -734,7 +814,6 @@ def _get_deserialize_callable_from_annotation( # pylint: disable=R0911, R0915, try: if annotation._name in ["List", "Set", "Tuple", "Sequence"]: # pyright: ignore if len(annotation.__args__) > 1: # pyright: ignore - entry_deserializers = [ _get_deserialize_callable_from_annotation(dt, module, rf) for dt in annotation.__args__ # pyright: ignore @@ -769,12 +848,23 @@ def _deserialize_default( def _deserialize_with_callable( deserializer: typing.Optional[typing.Callable[[typing.Any], typing.Any]], value: typing.Any, -): +): # pylint: disable=too-many-return-statements try: if value is None or isinstance(value, _Null): return None + if isinstance(value, ET.Element): + if deserializer is str: + return value.text or "" + if deserializer is int: + return int(value.text) if value.text else None + if deserializer is float: + return float(value.text) if value.text else None + if deserializer is bool: + return value.text == "true" if value.text else None if deserializer is None: return value + if deserializer in [int, float, bool]: + return deserializer(value) if isinstance(deserializer, CaseInsensitiveEnumMeta): try: return deserializer(value) @@ -804,6 +894,22 @@ def _deserialize( return _deserialize_with_callable(deserializer, value) +def _failsafe_deserialize( + deserializer: typing.Any, + value: typing.Any, + module: typing.Optional[str] = None, + rf: typing.Optional["_RestField"] = None, + format: typing.Optional[str] = None, +) -> typing.Any: + try: + return _deserialize(deserializer, value, module, rf, format) + except DeserializationError: + _LOGGER.warning( + "Ran into a deserialization error. Ignoring since this is failsafe deserialization", exc_info=True + ) + return None + + class _RestField: def __init__( self, @@ -815,6 +921,7 @@ def __init__( default: typing.Any = _UNSET, format: typing.Optional[str] = None, is_multipart_file_input: bool = False, + xml: typing.Optional[typing.Dict[str, typing.Any]] = None, ): self._type = type self._rest_name_input = name @@ -825,6 +932,7 @@ def __init__( self._default = default self._format = format self._is_multipart_file_input = is_multipart_file_input + self._xml = xml if xml is not None else {} @property def _class_type(self) -> typing.Any: @@ -875,6 +983,7 @@ def rest_field( default: typing.Any = _UNSET, format: typing.Optional[str] = None, is_multipart_file_input: bool = False, + xml: typing.Optional[typing.Dict[str, typing.Any]] = None, ) -> typing.Any: return _RestField( name=name, @@ -883,6 +992,7 @@ def rest_field( default=default, format=format, is_multipart_file_input=is_multipart_file_input, + xml=xml, ) @@ -891,5 +1001,175 @@ def rest_discriminator( name: typing.Optional[str] = None, type: typing.Optional[typing.Callable] = None, # pylint: disable=redefined-builtin visibility: typing.Optional[typing.List[str]] = None, + xml: typing.Optional[typing.Dict[str, typing.Any]] = None, +) -> typing.Any: + return _RestField(name=name, type=type, is_discriminator=True, visibility=visibility, xml=xml) + + +def serialize_xml(model: Model, exclude_readonly: bool = False) -> str: + """Serialize a model to XML. + + :param Model model: The model to serialize. + :param bool exclude_readonly: Whether to exclude readonly properties. + :returns: The XML representation of the model. + :rtype: str + """ + return ET.tostring(_get_element(model, exclude_readonly), encoding="unicode") # type: ignore + + +def _get_element( + o: typing.Any, + exclude_readonly: bool = False, + parent_meta: typing.Optional[typing.Dict[str, typing.Any]] = None, + wrapped_element: typing.Optional[ET.Element] = None, +) -> typing.Union[ET.Element, typing.List[ET.Element]]: + if _is_model(o): + model_meta = getattr(o, "_xml", {}) + + # if prop is a model, then use the prop element directly, else generate a wrapper of model + if wrapped_element is None: + wrapped_element = _create_xml_element( + model_meta.get("name", o.__class__.__name__), + model_meta.get("prefix"), + model_meta.get("ns"), + ) + + readonly_props = [] + if exclude_readonly: + readonly_props = [p._rest_name for p in o._attr_to_rest_field.values() if _is_readonly(p)] + + for k, v in o.items(): + # do not serialize readonly properties + if exclude_readonly and k in readonly_props: + continue + + prop_rest_field = _get_rest_field(o._attr_to_rest_field, k) + if prop_rest_field: + prop_meta = getattr(prop_rest_field, "_xml").copy() + # use the wire name as xml name if no specific name is set + if prop_meta.get("name") is None: + prop_meta["name"] = k + else: + # additional properties will not have rest field, use the wire name as xml name + prop_meta = {"name": k} + + # if no ns for prop, use model's + if prop_meta.get("ns") is None and model_meta.get("ns"): + prop_meta["ns"] = model_meta.get("ns") + prop_meta["prefix"] = model_meta.get("prefix") + + if prop_meta.get("unwrapped", False): + # unwrapped could only set on array + wrapped_element.extend(_get_element(v, exclude_readonly, prop_meta)) + elif prop_meta.get("text", False): + # text could only set on primitive type + wrapped_element.text = _get_primitive_type_value(v) + elif prop_meta.get("attribute", False): + xml_name = prop_meta.get("name", k) + if prop_meta.get("ns"): + ET.register_namespace(prop_meta.get("prefix"), prop_meta.get("ns")) # pyright: ignore + xml_name = "{" + prop_meta.get("ns") + "}" + xml_name # pyright: ignore + # attribute should be primitive type + wrapped_element.set(xml_name, _get_primitive_type_value(v)) + else: + # other wrapped prop element + wrapped_element.append(_get_wrapped_element(v, exclude_readonly, prop_meta)) + return wrapped_element + if isinstance(o, list): + return [_get_element(x, exclude_readonly, parent_meta) for x in o] # type: ignore + if isinstance(o, dict): + result = [] + for k, v in o.items(): + result.append( + _get_wrapped_element( + v, + exclude_readonly, + { + "name": k, + "ns": parent_meta.get("ns") if parent_meta else None, + "prefix": parent_meta.get("prefix") if parent_meta else None, + }, + ) + ) + return result + + # primitive case need to create element based on parent_meta + if parent_meta: + return _get_wrapped_element( + o, + exclude_readonly, + { + "name": parent_meta.get("itemsName", parent_meta.get("name")), + "prefix": parent_meta.get("itemsPrefix", parent_meta.get("prefix")), + "ns": parent_meta.get("itemsNs", parent_meta.get("ns")), + }, + ) + + raise ValueError("Could not serialize value into xml: " + o) + + +def _get_wrapped_element( + v: typing.Any, + exclude_readonly: bool, + meta: typing.Optional[typing.Dict[str, typing.Any]], +) -> ET.Element: + wrapped_element = _create_xml_element( + meta.get("name") if meta else None, meta.get("prefix") if meta else None, meta.get("ns") if meta else None + ) + if isinstance(v, (dict, list)): + wrapped_element.extend(_get_element(v, exclude_readonly, meta)) + elif _is_model(v): + _get_element(v, exclude_readonly, meta, wrapped_element) + else: + wrapped_element.text = _get_primitive_type_value(v) + return wrapped_element + + +def _get_primitive_type_value(v) -> str: + if v is True: + return "true" + if v is False: + return "false" + if isinstance(v, _Null): + return "" + return str(v) + + +def _create_xml_element(tag, prefix=None, ns=None): + if prefix and ns: + ET.register_namespace(prefix, ns) + if ns: + return ET.Element("{" + ns + "}" + tag) + return ET.Element(tag) + + +def _deserialize_xml( + deserializer: typing.Any, + value: str, ) -> typing.Any: - return _RestField(name=name, type=type, is_discriminator=True, visibility=visibility) + element = ET.fromstring(value) # nosec + return _deserialize(deserializer, element) + + +def _convert_element(e: ET.Element): + # dict case + if len(e.attrib) > 0 or len({child.tag for child in e}) > 1: + dict_result: typing.Dict[str, typing.Any] = {} + for child in e: + if dict_result.get(child.tag) is not None: + if isinstance(dict_result[child.tag], list): + dict_result[child.tag].append(_convert_element(child)) + else: + dict_result[child.tag] = [dict_result[child.tag], _convert_element(child)] + else: + dict_result[child.tag] = _convert_element(child) + dict_result.update(e.attrib) + return dict_result + # array case + if len(e) > 0: + array_result: typing.List[typing.Any] = [] + for child in e: + array_result.append(_convert_element(child)) + return array_result + # primitive case + return e.text diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/_operations/__init__.py b/sdk/ai/azure-ai-inference/azure/ai/inference/_operations/__init__.py index d3ebd561f739..ab87088736aa 100644 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/_operations/__init__.py +++ b/sdk/ai/azure-ai-inference/azure/ai/inference/_operations/__init__.py @@ -5,13 +5,19 @@ # Code generated by Microsoft (R) Python Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- +# pylint: disable=wrong-import-position -from ._operations import ChatCompletionsClientOperationsMixin -from ._operations import EmbeddingsClientOperationsMixin -from ._operations import ImageEmbeddingsClientOperationsMixin +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from ._patch import * # pylint: disable=unused-wildcard-import + +from ._operations import ChatCompletionsClientOperationsMixin # type: ignore +from ._operations import EmbeddingsClientOperationsMixin # type: ignore +from ._operations import ImageEmbeddingsClientOperationsMixin # type: ignore from ._patch import __all__ as _patch_all -from ._patch import * # pylint: disable=unused-wildcard-import +from ._patch import * from ._patch import patch_sdk as _patch_sdk __all__ = [ @@ -19,5 +25,5 @@ "EmbeddingsClientOperationsMixin", "ImageEmbeddingsClientOperationsMixin", ] -__all__.extend([p for p in _patch_all if p not in __all__]) +__all__.extend([p for p in _patch_all if p not in __all__]) # pyright: ignore _patch_sdk() diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/_operations/_operations.py b/sdk/ai/azure-ai-inference/azure/ai/inference/_operations/_operations.py index 3a24ee5736d3..d79af04a49bf 100644 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/_operations/_operations.py +++ b/sdk/ai/azure-ai-inference/azure/ai/inference/_operations/_operations.py @@ -1,4 +1,3 @@ -# pylint: disable=too-many-lines,too-many-statements # coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. @@ -9,7 +8,7 @@ from io import IOBase import json import sys -from typing import Any, Callable, Dict, IO, List, Optional, Type, TypeVar, Union, overload +from typing import Any, Callable, Dict, IO, List, Optional, TypeVar, Union, overload from azure.core.exceptions import ( ClientAuthenticationError, @@ -34,7 +33,7 @@ if sys.version_info >= (3, 9): from collections.abc import MutableMapping else: - from typing import MutableMapping # type: ignore # pylint: disable=ungrouped-imports + from typing import MutableMapping # type: ignore JSON = MutableMapping[str, Any] # pylint: disable=unsubscriptable-object _Unset: Any = object() T = TypeVar("T") @@ -246,7 +245,6 @@ def _complete( model: Optional[str] = None, **kwargs: Any ) -> _models.ChatCompletions: - # pylint: disable=too-many-locals """Gets chat completions for the provided chat messages. Completions support a wide variety of tasks and generate text that continues from or "completes" @@ -303,11 +301,20 @@ def _complete( :paramtype top_p: float :keyword max_tokens: The maximum number of tokens to generate. Default value is None. :paramtype max_tokens: int - :keyword response_format: The format that the model must output. Use this to enable JSON mode - instead of the default text mode. - Note that to enable JSON mode, some AI models may also require you to instruct the model to - produce JSON - via a system or user message. Default value is None. + :keyword response_format: An object specifying the format that the model must output. + + Setting to ``{ "type": "json_schema", "json_schema": {...} }`` enables Structured Outputs + which ensures the model will match your supplied JSON schema. + + Setting to ``{ "type": "json_object" }`` enables JSON mode, which ensures the message the + model generates is valid JSON. + + **Important:** when using JSON mode, you **must** also instruct the model to produce JSON + yourself via a system or user message. Without this, the model may generate an unending stream + of whitespace until the generation reaches the token limit, resulting in a long-running and + seemingly "stuck" request. Also note that the message content may be partially cut off if + ``finish_reason="length"``\\ , which indicates the generation exceeded ``max_tokens`` or the + conversation exceeded the max context length. Default value is None. :paramtype response_format: ~azure.ai.inference.models.ChatCompletionsResponseFormat :keyword stop: A collection of textual sequences that will end completions generation. Default value is None. @@ -335,7 +342,7 @@ def _complete( :rtype: ~azure.ai.inference.models.ChatCompletions :raises ~azure.core.exceptions.HttpResponseError: """ - error_map: MutableMapping[int, Type[HttpResponseError]] = { + error_map: MutableMapping = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError, @@ -425,7 +432,7 @@ def _get_model_info(self, **kwargs: Any) -> _models.ModelInfo: :rtype: ~azure.ai.inference.models.ModelInfo :raises ~azure.core.exceptions.HttpResponseError: """ - error_map: MutableMapping[int, Type[HttpResponseError]] = { + error_map: MutableMapping = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError, @@ -556,7 +563,7 @@ def _embed( :rtype: ~azure.ai.inference.models.EmbeddingsResult :raises ~azure.core.exceptions.HttpResponseError: """ - error_map: MutableMapping[int, Type[HttpResponseError]] = { + error_map: MutableMapping = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError, @@ -638,7 +645,7 @@ def _get_model_info(self, **kwargs: Any) -> _models.ModelInfo: :rtype: ~azure.ai.inference.models.ModelInfo :raises ~azure.core.exceptions.HttpResponseError: """ - error_map: MutableMapping[int, Type[HttpResponseError]] = { + error_map: MutableMapping = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError, @@ -703,7 +710,7 @@ def _embed( def _embed( self, *, - input: List[_models.EmbeddingInput], + input: List[_models.ImageEmbeddingInput], extra_params: Optional[Union[str, _models._enums.ExtraParameters]] = None, content_type: str = "application/json", dimensions: Optional[int] = None, @@ -727,7 +734,7 @@ def _embed( self, body: Union[JSON, IO[bytes]] = _Unset, *, - input: List[_models.EmbeddingInput] = _Unset, + input: List[_models.ImageEmbeddingInput] = _Unset, extra_params: Optional[Union[str, _models._enums.ExtraParameters]] = None, dimensions: Optional[int] = None, encoding_format: Optional[Union[str, _models.EmbeddingEncodingFormat]] = None, @@ -743,7 +750,7 @@ def _embed( :keyword input: Input image to embed. To embed multiple inputs in a single request, pass an array. The input must not exceed the max input tokens for the model. Required. - :paramtype input: list[~azure.ai.inference.models.EmbeddingInput] + :paramtype input: list[~azure.ai.inference.models.ImageEmbeddingInput] :keyword extra_params: Controls what happens if extra parameters, undefined by the REST API, are passed in the JSON request payload. This sets the HTTP request header ``extra-parameters``. Known values are: "error", "drop", and @@ -772,7 +779,7 @@ def _embed( :rtype: ~azure.ai.inference.models.EmbeddingsResult :raises ~azure.core.exceptions.HttpResponseError: """ - error_map: MutableMapping[int, Type[HttpResponseError]] = { + error_map: MutableMapping = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError, @@ -854,7 +861,7 @@ def _get_model_info(self, **kwargs: Any) -> _models.ModelInfo: :rtype: ~azure.ai.inference.models.ModelInfo :raises ~azure.core.exceptions.HttpResponseError: """ - error_map: MutableMapping[int, Type[HttpResponseError]] = { + error_map: MutableMapping = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError, diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/_patch.py b/sdk/ai/azure-ai-inference/azure/ai/inference/_patch.py index 8d2ca4a4aaf1..f7dd32510333 100644 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/_patch.py +++ b/sdk/ai/azure-ai-inference/azure/ai/inference/_patch.py @@ -2,1284 +2,13 @@ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. # ------------------------------------ -# pylint: disable=too-many-lines) """Customize generated code here. Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize - -Why do we patch auto-generated code? -1. Add support for input argument `model_extras` (all clients) -2. Add support for function load_client -3. Add support for setting sticky chat completions/embeddings input arguments in the client constructor -4. Add support for get_model_info, while caching the result (all clients) -5. Add support for chat completion streaming (ChatCompletionsClient client only) -6. Add support for friendly print of result objects (__str__ method) (all clients) -7. Add support for load() method in ImageUrl class (see /models/_patch.py) -8. Add support for sending two auth headers for api-key auth (all clients) - """ -import json -import logging -import sys - -from io import IOBase -from typing import Any, Dict, Union, IO, List, Literal, Optional, overload, Type, TYPE_CHECKING, Iterable - -from azure.core.pipeline import PipelineResponse -from azure.core.credentials import AzureKeyCredential -from azure.core.tracing.decorator import distributed_trace -from azure.core.utils import case_insensitive_dict -from azure.core.exceptions import ( - ClientAuthenticationError, - HttpResponseError, - map_error, - ResourceExistsError, - ResourceNotFoundError, - ResourceNotModifiedError, -) -from . import models as _models -from ._model_base import SdkJSONEncoder, _deserialize -from ._serialization import Serializer -from ._operations._operations import ( - build_chat_completions_complete_request, - build_embeddings_embed_request, - build_image_embeddings_embed_request, -) -from ._client import ChatCompletionsClient as ChatCompletionsClientGenerated -from ._client import EmbeddingsClient as EmbeddingsClientGenerated -from ._client import ImageEmbeddingsClient as ImageEmbeddingsClientGenerated - -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 azure.core.credentials import TokenCredential - -JSON = MutableMapping[str, Any] # pylint: disable=unsubscriptable-object -_Unset: Any = object() - -_SERIALIZER = Serializer() -_SERIALIZER.client_side_validation = False - -_LOGGER = logging.getLogger(__name__) - - -def load_client( - endpoint: str, credential: Union[AzureKeyCredential, "TokenCredential"], **kwargs: Any -) -> Union["ChatCompletionsClient", "EmbeddingsClient", "ImageEmbeddingsClient"]: - """ - Load a client from a given endpoint URL. The method makes a REST API call to the `/info` route - on the given endpoint, to determine the model type and therefore which client to instantiate. - This method will only work when using Serverless API or Managed Compute endpoint. - It will not work for GitHub Models endpoint or Azure OpenAI endpoint. - - :param endpoint: Service host. Required. - :type endpoint: str - :param credential: Credential used to authenticate requests to the service. Is either a - AzureKeyCredential type or a TokenCredential type. Required. - :type credential: ~azure.core.credentials.AzureKeyCredential or - ~azure.core.credentials.TokenCredential - :keyword api_version: The API version to use for this operation. Default value is - "2024-05-01-preview". Note that overriding this default value may result in unsupported - behavior. - :paramtype api_version: str - :return: The appropriate synchronous client associated with the given endpoint - :rtype: ~azure.ai.inference.ChatCompletionsClient or ~azure.ai.inference.EmbeddingsClient - or ~azure.ai.inference.ImageEmbeddingsClient - :raises ~azure.core.exceptions.HttpResponseError: - """ - - with ChatCompletionsClient( - endpoint, credential, **kwargs - ) as client: # Pick any of the clients, it does not matter. - model_info = client.get_model_info() # type: ignore - - _LOGGER.info("model_info=%s", model_info) - if not model_info.model_type: - raise ValueError( - "The AI model information is missing a value for `model type`. Cannot create an appropriate client." - ) - - # TODO: Remove "completions", "chat-comletions" and "embedding" once Mistral Large and Cohere fixes their model type - if model_info.model_type in (_models.ModelType.CHAT, "completion", "chat-completion", "chat-completions"): - chat_completion_client = ChatCompletionsClient(endpoint, credential, **kwargs) - chat_completion_client._model_info = ( # pylint: disable=protected-access,attribute-defined-outside-init - model_info - ) - return chat_completion_client - - if model_info.model_type in (_models.ModelType.EMBEDDINGS, "embedding"): - embedding_client = EmbeddingsClient(endpoint, credential, **kwargs) - embedding_client._model_info = model_info # pylint: disable=protected-access,attribute-defined-outside-init - return embedding_client - - if model_info.model_type == _models.ModelType.IMAGE_EMBEDDINGS: - image_embedding_client = ImageEmbeddingsClient(endpoint, credential, **kwargs) - image_embedding_client._model_info = ( # pylint: disable=protected-access,attribute-defined-outside-init - model_info - ) - return image_embedding_client - - raise ValueError(f"No client available to support AI model type `{model_info.model_type}`") - - -class ChatCompletionsClient(ChatCompletionsClientGenerated): # pylint: disable=too-many-instance-attributes - """ChatCompletionsClient. - - :param endpoint: Service host. Required. - :type endpoint: str - :param credential: Credential used to authenticate requests to the service. Is either a - AzureKeyCredential type or a TokenCredential type. Required. - :type credential: ~azure.core.credentials.AzureKeyCredential or - ~azure.core.credentials.TokenCredential - :keyword frequency_penalty: A value that influences the probability of generated tokens - appearing based on their cumulative frequency in generated text. - Positive values will make tokens less likely to appear as their frequency increases and - decrease the likelihood of the model repeating the same statements verbatim. - Supported range is [-2, 2]. - Default value is None. - :paramtype frequency_penalty: float - :keyword presence_penalty: A value that influences the probability of generated tokens - appearing based on their existing - presence in generated text. - Positive values will make tokens less likely to appear when they already exist and increase - the model's likelihood to output new topics. - Supported range is [-2, 2]. - Default value is None. - :paramtype presence_penalty: float - :keyword temperature: The sampling temperature to use that controls the apparent creativity of - generated completions. - Higher values will make output more random while lower values will make results more focused - and deterministic. - It is not recommended to modify temperature and top_p for the same completions request as the - interaction of these two settings is difficult to predict. - Supported range is [0, 1]. - Default value is None. - :paramtype temperature: float - :keyword top_p: An alternative to sampling with temperature called nucleus sampling. This value - causes the - model to consider the results of tokens with the provided probability mass. As an example, a - value of 0.15 will cause only the tokens comprising the top 15% of probability mass to be - considered. - It is not recommended to modify temperature and top_p for the same completions request as the - interaction of these two settings is difficult to predict. - Supported range is [0, 1]. - Default value is None. - :paramtype top_p: float - :keyword max_tokens: The maximum number of tokens to generate. Default value is None. - :paramtype max_tokens: int - :keyword response_format: The format that the model must output. Use this to enable JSON mode - instead of the default text mode. - Note that to enable JSON mode, some AI models may also require you to instruct the model to - produce JSON via a system or user message. Default value is None. - :paramtype response_format: ~azure.ai.inference.models.ChatCompletionsResponseFormat - :keyword stop: A collection of textual sequences that will end completions generation. Default - value is None. - :paramtype stop: list[str] - :keyword tools: The available tool definitions that the chat completions request can use, - including caller-defined functions. Default value is None. - :paramtype tools: list[~azure.ai.inference.models.ChatCompletionsToolDefinition] - :keyword tool_choice: If specified, the model will configure which of the provided tools it can - use for the chat completions response. Is either a Union[str, - "_models.ChatCompletionsToolChoicePreset"] type or a ChatCompletionsNamedToolChoice type. - Default value is None. - :paramtype tool_choice: str or ~azure.ai.inference.models.ChatCompletionsToolChoicePreset or - ~azure.ai.inference.models.ChatCompletionsNamedToolChoice - :keyword seed: If specified, the system will make a best effort to sample deterministically - such that repeated requests with the - same seed and parameters should return the same result. Determinism is not guaranteed. - Default value is None. - :paramtype seed: int - :keyword model: ID of the specific AI model to use, if more than one model is available on the - endpoint. Default value is None. - :paramtype model: str - :keyword model_extras: Additional, model-specific parameters that are not in the - standard request payload. They will be added as-is to the root of the JSON in the request body. - How the service handles these extra parameters depends on the value of the - ``extra-parameters`` request header. Default value is None. - :paramtype model_extras: dict[str, Any] - :keyword api_version: The API version to use for this operation. Default value is - "2024-05-01-preview". Note that overriding this default value may result in unsupported - behavior. - :paramtype api_version: str - """ - - def __init__( - self, - endpoint: str, - credential: Union[AzureKeyCredential, "TokenCredential"], - *, - frequency_penalty: Optional[float] = None, - presence_penalty: Optional[float] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - max_tokens: Optional[int] = None, - response_format: Optional[_models.ChatCompletionsResponseFormat] = None, - stop: Optional[List[str]] = None, - tools: Optional[List[_models.ChatCompletionsToolDefinition]] = None, - tool_choice: Optional[ - Union[str, _models.ChatCompletionsToolChoicePreset, _models.ChatCompletionsNamedToolChoice] - ] = None, - seed: Optional[int] = None, - model: Optional[str] = None, - model_extras: Optional[Dict[str, Any]] = None, - **kwargs: Any, - ) -> None: - - self._model_info: Optional[_models.ModelInfo] = None - - # Store default chat completions settings, to be applied in all future service calls - # unless overridden by arguments in the `complete` method. - self._frequency_penalty = frequency_penalty - self._presence_penalty = presence_penalty - self._temperature = temperature - self._top_p = top_p - self._max_tokens = max_tokens - self._response_format = response_format - self._stop = stop - self._tools = tools - self._tool_choice = tool_choice - self._seed = seed - self._model = model - self._model_extras = model_extras - - # For Key auth, we need to send these two auth HTTP request headers simultaneously: - # 1. "Authorization: Bearer " - # 2. "api-key: " - # This is because Serverless API, Managed Compute and GitHub endpoints support the first header, - # and Azure OpenAI and the new Unified Inference endpoints support the second header. - # The first header will be taken care of by auto-generated code. - # The second one is added here. - if isinstance(credential, AzureKeyCredential): - headers = kwargs.pop("headers", {}) - if "api-key" not in headers: - headers["api-key"] = credential.key - kwargs["headers"] = headers - - super().__init__(endpoint, credential, **kwargs) - - @overload - def complete( - self, - *, - messages: Union[List[_models.ChatRequestMessage], List[Dict[str, Any]]], - stream: Literal[False] = False, - frequency_penalty: Optional[float] = None, - presence_penalty: Optional[float] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - max_tokens: Optional[int] = None, - response_format: Optional[_models.ChatCompletionsResponseFormat] = None, - stop: Optional[List[str]] = None, - tools: Optional[List[_models.ChatCompletionsToolDefinition]] = None, - tool_choice: Optional[ - Union[str, _models.ChatCompletionsToolChoicePreset, _models.ChatCompletionsNamedToolChoice] - ] = None, - seed: Optional[int] = None, - model: Optional[str] = None, - model_extras: Optional[Dict[str, Any]] = None, - **kwargs: Any, - ) -> _models.ChatCompletions: ... - - @overload - def complete( - self, - *, - messages: Union[List[_models.ChatRequestMessage], List[Dict[str, Any]]], - stream: Literal[True], - frequency_penalty: Optional[float] = None, - presence_penalty: Optional[float] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - max_tokens: Optional[int] = None, - response_format: Optional[_models.ChatCompletionsResponseFormat] = None, - stop: Optional[List[str]] = None, - tools: Optional[List[_models.ChatCompletionsToolDefinition]] = None, - tool_choice: Optional[ - Union[str, _models.ChatCompletionsToolChoicePreset, _models.ChatCompletionsNamedToolChoice] - ] = None, - seed: Optional[int] = None, - model: Optional[str] = None, - model_extras: Optional[Dict[str, Any]] = None, - **kwargs: Any, - ) -> Iterable[_models.StreamingChatCompletionsUpdate]: ... - - @overload - def complete( - self, - *, - messages: Union[List[_models.ChatRequestMessage], List[Dict[str, Any]]], - stream: Optional[bool] = None, - frequency_penalty: Optional[float] = None, - presence_penalty: Optional[float] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - max_tokens: Optional[int] = None, - response_format: Optional[_models.ChatCompletionsResponseFormat] = None, - stop: Optional[List[str]] = None, - tools: Optional[List[_models.ChatCompletionsToolDefinition]] = None, - tool_choice: Optional[ - Union[str, _models.ChatCompletionsToolChoicePreset, _models.ChatCompletionsNamedToolChoice] - ] = None, - seed: Optional[int] = None, - model: Optional[str] = None, - model_extras: Optional[Dict[str, Any]] = None, - **kwargs: Any, - ) -> Union[Iterable[_models.StreamingChatCompletionsUpdate], _models.ChatCompletions]: - # pylint: disable=line-too-long - """Gets chat completions for the provided chat messages. - Completions support a wide variety of tasks and generate text that continues from or - "completes" provided prompt data. The method makes a REST API call to the `/chat/completions` route - on the given endpoint. - When using this method with `stream=True`, the response is streamed - back to the client. Iterate over the resulting StreamingChatCompletions - object to get content updates as they arrive. By default, the response is a ChatCompletions object - (non-streaming). - - :keyword messages: The collection of context messages associated with this chat completions - request. - Typical usage begins with a chat message for the System role that provides instructions for - the behavior of the assistant, followed by alternating messages between the User and - Assistant roles. Required. - :paramtype messages: list[~azure.ai.inference.models.ChatRequestMessage] or list[dict[str, Any]] - :keyword stream: A value indicating whether chat completions should be streamed for this request. - Default value is False. If streaming is enabled, the response will be a StreamingChatCompletions. - Otherwise the response will be a ChatCompletions. - :paramtype stream: bool - :keyword frequency_penalty: A value that influences the probability of generated tokens - appearing based on their cumulative frequency in generated text. - Positive values will make tokens less likely to appear as their frequency increases and - decrease the likelihood of the model repeating the same statements verbatim. - Supported range is [-2, 2]. - Default value is None. - :paramtype frequency_penalty: float - :keyword presence_penalty: A value that influences the probability of generated tokens - appearing based on their existing - presence in generated text. - Positive values will make tokens less likely to appear when they already exist and increase - the model's likelihood to output new topics. - Supported range is [-2, 2]. - Default value is None. - :paramtype presence_penalty: float - :keyword temperature: The sampling temperature to use that controls the apparent creativity of - generated completions. - Higher values will make output more random while lower values will make results more focused - and deterministic. - It is not recommended to modify temperature and top_p for the same completions request as the - interaction of these two settings is difficult to predict. - Supported range is [0, 1]. - Default value is None. - :paramtype temperature: float - :keyword top_p: An alternative to sampling with temperature called nucleus sampling. This value - causes the - model to consider the results of tokens with the provided probability mass. As an example, a - value of 0.15 will cause only the tokens comprising the top 15% of probability mass to be - considered. - It is not recommended to modify temperature and top_p for the same completions request as the - interaction of these two settings is difficult to predict. - Supported range is [0, 1]. - Default value is None. - :paramtype top_p: float - :keyword max_tokens: The maximum number of tokens to generate. Default value is None. - :paramtype max_tokens: int - :keyword response_format: The format that the model must output. Use this to enable JSON mode - instead of the default text mode. - Note that to enable JSON mode, some AI models may also require you to instruct the model to - produce JSON via a system or user message. Default value is None. - :paramtype response_format: ~azure.ai.inference.models.ChatCompletionsResponseFormat - :keyword stop: A collection of textual sequences that will end completions generation. Default - value is None. - :paramtype stop: list[str] - :keyword tools: The available tool definitions that the chat completions request can use, - including caller-defined functions. Default value is None. - :paramtype tools: list[~azure.ai.inference.models.ChatCompletionsToolDefinition] - :keyword tool_choice: If specified, the model will configure which of the provided tools it can - use for the chat completions response. Is either a Union[str, - "_models.ChatCompletionsToolChoicePreset"] type or a ChatCompletionsNamedToolChoice type. - Default value is None. - :paramtype tool_choice: str or ~azure.ai.inference.models.ChatCompletionsToolChoicePreset or - ~azure.ai.inference.models.ChatCompletionsNamedToolChoice - :keyword seed: If specified, the system will make a best effort to sample deterministically - such that repeated requests with the - same seed and parameters should return the same result. Determinism is not guaranteed. - Default value is None. - :paramtype seed: int - :keyword model: ID of the specific AI model to use, if more than one model is available on the - endpoint. Default value is None. - :paramtype model: str - :keyword model_extras: Additional, model-specific parameters that are not in the - standard request payload. They will be added as-is to the root of the JSON in the request body. - How the service handles these extra parameters depends on the value of the - ``extra-parameters`` request header. Default value is None. - :paramtype model_extras: dict[str, Any] - :return: ChatCompletions for non-streaming, or Iterable[StreamingChatCompletionsUpdate] for streaming. - :rtype: ~azure.ai.inference.models.ChatCompletions or ~azure.ai.inference.models.StreamingChatCompletions - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def complete( - self, - body: JSON, - *, - content_type: str = "application/json", - **kwargs: Any, - ) -> Union[Iterable[_models.StreamingChatCompletionsUpdate], _models.ChatCompletions]: - # pylint: disable=line-too-long - """Gets chat completions for the provided chat messages. - Completions support a wide variety of tasks and generate text that continues from or - "completes" provided prompt data. - - :param body: An object of type MutableMapping[str, Any], such as a dictionary, that - specifies the full request payload. 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: ChatCompletions for non-streaming, or Iterable[StreamingChatCompletionsUpdate] for streaming. - :rtype: ~azure.ai.inference.models.ChatCompletions or ~azure.ai.inference.models.StreamingChatCompletions - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def complete( - self, - body: IO[bytes], - *, - content_type: str = "application/json", - **kwargs: Any, - ) -> Union[Iterable[_models.StreamingChatCompletionsUpdate], _models.ChatCompletions]: - # pylint: disable=line-too-long - # pylint: disable=too-many-locals - """Gets chat completions for the provided chat messages. - Completions support a wide variety of tasks and generate text that continues from or - "completes" provided prompt data. - - :param body: Specifies the full request payload. 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: ChatCompletions for non-streaming, or Iterable[StreamingChatCompletionsUpdate] for streaming. - :rtype: ~azure.ai.inference.models.ChatCompletions or ~azure.ai.inference.models.StreamingChatCompletions - :raises ~azure.core.exceptions.HttpResponseError: - """ - - # pylint:disable=client-method-missing-tracing-decorator - def complete( - self, - body: Union[JSON, IO[bytes]] = _Unset, - *, - messages: Union[List[_models.ChatRequestMessage], List[Dict[str, Any]]] = _Unset, - stream: Optional[bool] = None, - frequency_penalty: Optional[float] = None, - presence_penalty: Optional[float] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - max_tokens: Optional[int] = None, - response_format: Optional[_models.ChatCompletionsResponseFormat] = None, - stop: Optional[List[str]] = None, - tools: Optional[List[_models.ChatCompletionsToolDefinition]] = None, - tool_choice: Optional[ - Union[str, _models.ChatCompletionsToolChoicePreset, _models.ChatCompletionsNamedToolChoice] - ] = None, - seed: Optional[int] = None, - model: Optional[str] = None, - model_extras: Optional[Dict[str, Any]] = None, - **kwargs: Any, - ) -> Union[Iterable[_models.StreamingChatCompletionsUpdate], _models.ChatCompletions]: - # pylint: disable=line-too-long - # pylint: disable=too-many-locals - """Gets chat completions for the provided chat messages. - Completions support a wide variety of tasks and generate text that continues from or - "completes" provided prompt data. When using this method with `stream=True`, the response is streamed - back to the client. Iterate over the resulting :class:`~azure.ai.inference.models.StreamingChatCompletions` - object to get content updates as they arrive. - - :param body: Is either a MutableMapping[str, Any] type (like a dictionary) or a IO[bytes] type - that specifies the full request payload. Required. - :type body: JSON or IO[bytes] - :keyword messages: The collection of context messages associated with this chat completions - request. - Typical usage begins with a chat message for the System role that provides instructions for - the behavior of the assistant, followed by alternating messages between the User and - Assistant roles. Required. - :paramtype messages: list[~azure.ai.inference.models.ChatRequestMessage] or list[dict[str, Any]] - :keyword stream: A value indicating whether chat completions should be streamed for this request. - Default value is False. If streaming is enabled, the response will be a StreamingChatCompletions. - Otherwise the response will be a ChatCompletions. - :paramtype stream: bool - :keyword frequency_penalty: A value that influences the probability of generated tokens - appearing based on their cumulative frequency in generated text. - Positive values will make tokens less likely to appear as their frequency increases and - decrease the likelihood of the model repeating the same statements verbatim. - Supported range is [-2, 2]. - Default value is None. - :paramtype frequency_penalty: float - :keyword presence_penalty: A value that influences the probability of generated tokens - appearing based on their existing - presence in generated text. - Positive values will make tokens less likely to appear when they already exist and increase - the model's likelihood to output new topics. - Supported range is [-2, 2]. - Default value is None. - :paramtype presence_penalty: float - :keyword temperature: The sampling temperature to use that controls the apparent creativity of - generated completions. - Higher values will make output more random while lower values will make results more focused - and deterministic. - It is not recommended to modify temperature and top_p for the same completions request as the - interaction of these two settings is difficult to predict. - Supported range is [0, 1]. - Default value is None. - :paramtype temperature: float - :keyword top_p: An alternative to sampling with temperature called nucleus sampling. This value - causes the - model to consider the results of tokens with the provided probability mass. As an example, a - value of 0.15 will cause only the tokens comprising the top 15% of probability mass to be - considered. - It is not recommended to modify temperature and top_p for the same completions request as the - interaction of these two settings is difficult to predict. - Supported range is [0, 1]. - Default value is None. - :paramtype top_p: float - :keyword max_tokens: The maximum number of tokens to generate. Default value is None. - :paramtype max_tokens: int - :keyword response_format: The format that the model must output. Use this to enable JSON mode - instead of the default text mode. - Note that to enable JSON mode, some AI models may also require you to instruct the model to - produce JSON via a system or user message. Default value is None. - :paramtype response_format: ~azure.ai.inference.models.ChatCompletionsResponseFormat - :keyword stop: A collection of textual sequences that will end completions generation. Default - value is None. - :paramtype stop: list[str] - :keyword tools: The available tool definitions that the chat completions request can use, - including caller-defined functions. Default value is None. - :paramtype tools: list[~azure.ai.inference.models.ChatCompletionsToolDefinition] - :keyword tool_choice: If specified, the model will configure which of the provided tools it can - use for the chat completions response. Is either a Union[str, - "_models.ChatCompletionsToolChoicePreset"] type or a ChatCompletionsNamedToolChoice type. - Default value is None. - :paramtype tool_choice: str or ~azure.ai.inference.models.ChatCompletionsToolChoicePreset or - ~azure.ai.inference.models.ChatCompletionsNamedToolChoice - :keyword seed: If specified, the system will make a best effort to sample deterministically - such that repeated requests with the - same seed and parameters should return the same result. Determinism is not guaranteed. - Default value is None. - :paramtype seed: int - :keyword model: ID of the specific AI model to use, if more than one model is available on the - endpoint. Default value is None. - :paramtype model: str - :keyword model_extras: Additional, model-specific parameters that are not in the - standard request payload. They will be added as-is to the root of the JSON in the request body. - How the service handles these extra parameters depends on the value of the - ``extra-parameters`` request header. Default value is None. - :paramtype model_extras: dict[str, Any] - :return: ChatCompletions for non-streaming, or Iterable[StreamingChatCompletionsUpdate] for streaming. - :rtype: ~azure.ai.inference.models.ChatCompletions or ~azure.ai.inference.models.StreamingChatCompletions - :raises ~azure.core.exceptions.HttpResponseError: - """ - error_map = { - 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 {} - _extra_parameters: Union[_models._enums.ExtraParameters, None] = None - - content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) - - if body is _Unset: - if messages is _Unset: - raise TypeError("missing required argument: messages") - body = { - "messages": messages, - "stream": stream, - "frequency_penalty": frequency_penalty if frequency_penalty is not None else self._frequency_penalty, - "max_tokens": max_tokens if max_tokens is not None else self._max_tokens, - "model": model if model is not None else self._model, - "presence_penalty": presence_penalty if presence_penalty is not None else self._presence_penalty, - "response_format": response_format if response_format is not None else self._response_format, - "seed": seed if seed is not None else self._seed, - "stop": stop if stop is not None else self._stop, - "temperature": temperature if temperature is not None else self._temperature, - "tool_choice": tool_choice if tool_choice is not None else self._tool_choice, - "tools": tools if tools is not None else self._tools, - "top_p": top_p if top_p is not None else self._top_p, - } - if model_extras is not None and bool(model_extras): - body.update(model_extras) - _extra_parameters = _models._enums.ExtraParameters.PASS_THROUGH # pylint: disable=protected-access - elif self._model_extras is not None and bool(self._model_extras): - body.update(self._model_extras) - _extra_parameters = _models._enums.ExtraParameters.PASS_THROUGH # pylint: disable=protected-access - body = {k: v for k, v in body.items() if v is not None} - elif isinstance(body, dict) and "stream" in body and isinstance(body["stream"], bool): - stream = body["stream"] - 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_chat_completions_complete_request( - extra_params=_extra_parameters, - content_type=content_type, - api_version=self._config.api_version, - content=_content, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), - } - _request.url = self._client.format_url(_request.url, **path_format_arguments) - - _stream = stream or 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: - response.read() # Load the body in memory and close the socket - map_error(status_code=response.status_code, response=response, error_map=error_map) - raise HttpResponseError(response=response) - - if _stream: - return _models.StreamingChatCompletions(response) - - return _deserialize(_models._patch.ChatCompletions, response.json()) # pylint: disable=protected-access - - @distributed_trace - def get_model_info(self, **kwargs: Any) -> _models.ModelInfo: - # pylint: disable=line-too-long - """Returns information about the AI model. - The method makes a REST API call to the ``/info`` route on the given endpoint. - This method will only work when using Serverless API or Managed Compute endpoint. - It will not work for GitHub Models endpoint or Azure OpenAI endpoint. - - :return: ModelInfo. The ModelInfo is compatible with MutableMapping - :rtype: ~azure.ai.inference.models.ModelInfo - :raises ~azure.core.exceptions.HttpResponseError: - """ - if not self._model_info: - self._model_info = self._get_model_info(**kwargs) # pylint: disable=attribute-defined-outside-init - return self._model_info - - def __str__(self) -> str: - # pylint: disable=client-method-name-no-double-underscore - return super().__str__() + f"\n{self._model_info}" if self._model_info else super().__str__() - - -class EmbeddingsClient(EmbeddingsClientGenerated): - """EmbeddingsClient. - - :param endpoint: Service host. Required. - :type endpoint: str - :param credential: Credential used to authenticate requests to the service. Is either a - AzureKeyCredential type or a TokenCredential type. Required. - :type credential: ~azure.core.credentials.AzureKeyCredential or - ~azure.core.credentials.TokenCredential - :keyword dimensions: Optional. The number of dimensions the resulting output embeddings should - have. Default value is None. - :paramtype dimensions: int - :keyword encoding_format: Optional. The desired format for the returned embeddings. - Known values are: - "base64", "binary", "float", "int8", "ubinary", and "uint8". Default value is None. - :paramtype encoding_format: str or ~azure.ai.inference.models.EmbeddingEncodingFormat - :keyword input_type: Optional. The type of the input. Known values are: - "text", "query", and "document". Default value is None. - :paramtype input_type: str or ~azure.ai.inference.models.EmbeddingInputType - :keyword model: ID of the specific AI model to use, if more than one model is available on the - endpoint. Default value is None. - :paramtype model: str - :keyword model_extras: Additional, model-specific parameters that are not in the - standard request payload. They will be added as-is to the root of the JSON in the request body. - How the service handles these extra parameters depends on the value of the - ``extra-parameters`` request header. Default value is None. - :paramtype model_extras: dict[str, Any] - :keyword api_version: The API version to use for this operation. Default value is - "2024-05-01-preview". Note that overriding this default value may result in unsupported - behavior. - :paramtype api_version: str - """ - - def __init__( - self, - endpoint: str, - credential: Union[AzureKeyCredential, "TokenCredential"], - *, - dimensions: Optional[int] = None, - encoding_format: Optional[Union[str, _models.EmbeddingEncodingFormat]] = None, - input_type: Optional[Union[str, _models.EmbeddingInputType]] = None, - model: Optional[str] = None, - model_extras: Optional[Dict[str, Any]] = None, - **kwargs: Any, - ) -> None: - - self._model_info: Optional[_models.ModelInfo] = None - - # Store default embeddings settings, to be applied in all future service calls - # unless overridden by arguments in the `embed` method. - self._dimensions = dimensions - self._encoding_format = encoding_format - self._input_type = input_type - self._model = model - self._model_extras = model_extras - - # For Key auth, we need to send these two auth HTTP request headers simultaneously: - # 1. "Authorization: Bearer " - # 2. "api-key: " - # This is because Serverless API, Managed Compute and GitHub endpoints support the first header, - # and Azure OpenAI and the new Unified Inference endpoints support the second header. - # The first header will be taken care of by auto-generated code. - # The second one is added here. - if isinstance(credential, AzureKeyCredential): - headers = kwargs.pop("headers", {}) - if "api-key" not in headers: - headers["api-key"] = credential.key - kwargs["headers"] = headers - - super().__init__(endpoint, credential, **kwargs) - - @overload - def embed( - self, - *, - input: List[str], - dimensions: Optional[int] = None, - encoding_format: Optional[Union[str, _models.EmbeddingEncodingFormat]] = None, - input_type: Optional[Union[str, _models.EmbeddingInputType]] = None, - model: Optional[str] = None, - model_extras: Optional[Dict[str, Any]] = None, - **kwargs: Any, - ) -> _models.EmbeddingsResult: - """Return the embedding vectors for given text prompts. - The method makes a REST API call to the `/embeddings` route on the given endpoint. - - :keyword input: Input text to embed, encoded as a string or array of tokens. - To embed multiple inputs in a single request, pass an array - of strings or array of token arrays. Required. - :paramtype input: list[str] - :keyword dimensions: Optional. The number of dimensions the resulting output embeddings should - have. Default value is None. - :paramtype dimensions: int - :keyword encoding_format: Optional. The desired format for the returned embeddings. - Known values are: - "base64", "binary", "float", "int8", "ubinary", and "uint8". Default value is None. - :paramtype encoding_format: str or ~azure.ai.inference.models.EmbeddingEncodingFormat - :keyword input_type: Optional. The type of the input. Known values are: - "text", "query", and "document". Default value is None. - :paramtype input_type: str or ~azure.ai.inference.models.EmbeddingInputType - :keyword model: ID of the specific AI model to use, if more than one model is available on the - endpoint. Default value is None. - :paramtype model: str - :keyword model_extras: Additional, model-specific parameters that are not in the - standard request payload. They will be added as-is to the root of the JSON in the request body. - How the service handles these extra parameters depends on the value of the - ``extra-parameters`` request header. Default value is None. - :paramtype model_extras: dict[str, Any] - :return: EmbeddingsResult. The EmbeddingsResult is compatible with MutableMapping - :rtype: ~azure.ai.inference.models.EmbeddingsResult - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def embed( - self, - body: JSON, - *, - content_type: str = "application/json", - **kwargs: Any, - ) -> _models.EmbeddingsResult: - """Return the embedding vectors for given text prompts. - The method makes a REST API call to the `/embeddings` route on the given endpoint. - - :param body: An object of type MutableMapping[str, Any], such as a dictionary, that - specifies the full request payload. 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: EmbeddingsResult. The EmbeddingsResult is compatible with MutableMapping - :rtype: ~azure.ai.inference.models.EmbeddingsResult - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def embed( - self, - body: IO[bytes], - *, - content_type: str = "application/json", - **kwargs: Any, - ) -> _models.EmbeddingsResult: - """Return the embedding vectors for given text prompts. - The method makes a REST API call to the `/embeddings` route on the given endpoint. - - :param body: Specifies the full request payload. 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: EmbeddingsResult. The EmbeddingsResult is compatible with MutableMapping - :rtype: ~azure.ai.inference.models.EmbeddingsResult - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace - def embed( - self, - body: Union[JSON, IO[bytes]] = _Unset, - *, - input: List[str] = _Unset, - dimensions: Optional[int] = None, - encoding_format: Optional[Union[str, _models.EmbeddingEncodingFormat]] = None, - input_type: Optional[Union[str, _models.EmbeddingInputType]] = None, - model: Optional[str] = None, - model_extras: Optional[Dict[str, Any]] = None, - **kwargs: Any, - ) -> _models.EmbeddingsResult: - # pylint: disable=line-too-long - """Return the embedding vectors for given text prompts. - The method makes a REST API call to the `/embeddings` route on the given endpoint. - - :param body: Is either a MutableMapping[str, Any] type (like a dictionary) or a IO[bytes] type - that specifies the full request payload. Required. - :type body: JSON or IO[bytes] - :keyword input: Input text to embed, encoded as a string or array of tokens. - To embed multiple inputs in a single request, pass an array - of strings or array of token arrays. Required. - :paramtype input: list[str] - :keyword dimensions: Optional. The number of dimensions the resulting output embeddings should - have. Default value is None. - :paramtype dimensions: int - :keyword encoding_format: Optional. The desired format for the returned embeddings. - Known values are: - "base64", "binary", "float", "int8", "ubinary", and "uint8". Default value is None. - :paramtype encoding_format: str or ~azure.ai.inference.models.EmbeddingEncodingFormat - :keyword input_type: Optional. The type of the input. Known values are: - "text", "query", and "document". Default value is None. - :paramtype input_type: str or ~azure.ai.inference.models.EmbeddingInputType - :keyword model: ID of the specific AI model to use, if more than one model is available on the - endpoint. Default value is None. - :paramtype model: str - :keyword model_extras: Additional, model-specific parameters that are not in the - standard request payload. They will be added as-is to the root of the JSON in the request body. - How the service handles these extra parameters depends on the value of the - ``extra-parameters`` request header. Default value is None. - :paramtype model_extras: dict[str, Any] - :return: EmbeddingsResult. The EmbeddingsResult is compatible with MutableMapping - :rtype: ~azure.ai.inference.models.EmbeddingsResult - :raises ~azure.core.exceptions.HttpResponseError: - """ - error_map: MutableMapping[int, Type[HttpResponseError]] = { - 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 {} - _extra_parameters: Union[_models._enums.ExtraParameters, None] = None - - content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) - - if body is _Unset: - if input is _Unset: - raise TypeError("missing required argument: input") - body = { - "input": input, - "dimensions": dimensions if dimensions is not None else self._dimensions, - "encoding_format": encoding_format if encoding_format is not None else self._encoding_format, - "input_type": input_type if input_type is not None else self._input_type, - "model": model if model is not None else self._model, - } - if model_extras is not None and bool(model_extras): - body.update(model_extras) - _extra_parameters = _models._enums.ExtraParameters.PASS_THROUGH # pylint: disable=protected-access - elif self._model_extras is not None and bool(self._model_extras): - body.update(self._model_extras) - _extra_parameters = _models._enums.ExtraParameters.PASS_THROUGH # pylint: disable=protected-access - 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_embeddings_embed_request( - extra_params=_extra_parameters, - content_type=content_type, - api_version=self._config.api_version, - content=_content, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), - } - _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: - response.read() # Load the body in memory and close the socket - 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._patch.EmbeddingsResult, response.json() # pylint: disable=protected-access - ) - - return deserialized # type: ignore - - @distributed_trace - def get_model_info(self, **kwargs: Any) -> _models.ModelInfo: - # pylint: disable=line-too-long - """Returns information about the AI model. - The method makes a REST API call to the ``/info`` route on the given endpoint. - This method will only work when using Serverless API or Managed Compute endpoint. - It will not work for GitHub Models endpoint or Azure OpenAI endpoint. - - :return: ModelInfo. The ModelInfo is compatible with MutableMapping - :rtype: ~azure.ai.inference.models.ModelInfo - :raises ~azure.core.exceptions.HttpResponseError: - """ - if not self._model_info: - self._model_info = self._get_model_info(**kwargs) # pylint: disable=attribute-defined-outside-init - return self._model_info - - def __str__(self) -> str: - # pylint: disable=client-method-name-no-double-underscore - return super().__str__() + f"\n{self._model_info}" if self._model_info else super().__str__() - - -class ImageEmbeddingsClient(ImageEmbeddingsClientGenerated): - """ImageEmbeddingsClient. - - :param endpoint: Service host. Required. - :type endpoint: str - :param credential: Credential used to authenticate requests to the service. Is either a - AzureKeyCredential type or a TokenCredential type. Required. - :type credential: ~azure.core.credentials.AzureKeyCredential or - ~azure.core.credentials.TokenCredential - :keyword dimensions: Optional. The number of dimensions the resulting output embeddings should - have. Default value is None. - :paramtype dimensions: int - :keyword encoding_format: Optional. The desired format for the returned embeddings. - Known values are: - "base64", "binary", "float", "int8", "ubinary", and "uint8". Default value is None. - :paramtype encoding_format: str or ~azure.ai.inference.models.EmbeddingEncodingFormat - :keyword input_type: Optional. The type of the input. Known values are: - "text", "query", and "document". Default value is None. - :paramtype input_type: str or ~azure.ai.inference.models.EmbeddingInputType - :keyword model: ID of the specific AI model to use, if more than one model is available on the - endpoint. Default value is None. - :paramtype model: str - :keyword model_extras: Additional, model-specific parameters that are not in the - standard request payload. They will be added as-is to the root of the JSON in the request body. - How the service handles these extra parameters depends on the value of the - ``extra-parameters`` request header. Default value is None. - :paramtype model_extras: dict[str, Any] - :keyword api_version: The API version to use for this operation. Default value is - "2024-05-01-preview". Note that overriding this default value may result in unsupported - behavior. - :paramtype api_version: str - """ - - def __init__( - self, - endpoint: str, - credential: Union[AzureKeyCredential, "TokenCredential"], - *, - dimensions: Optional[int] = None, - encoding_format: Optional[Union[str, _models.EmbeddingEncodingFormat]] = None, - input_type: Optional[Union[str, _models.EmbeddingInputType]] = None, - model: Optional[str] = None, - model_extras: Optional[Dict[str, Any]] = None, - **kwargs: Any, - ) -> None: - - self._model_info: Optional[_models.ModelInfo] = None - - # Store default embeddings settings, to be applied in all future service calls - # unless overridden by arguments in the `embed` method. - self._dimensions = dimensions - self._encoding_format = encoding_format - self._input_type = input_type - self._model = model - self._model_extras = model_extras - - # For Key auth, we need to send these two auth HTTP request headers simultaneously: - # 1. "Authorization: Bearer " - # 2. "api-key: " - # This is because Serverless API, Managed Compute and GitHub endpoints support the first header, - # and Azure OpenAI and the new Unified Inference endpoints support the second header. - # The first header will be taken care of by auto-generated code. - # The second one is added here. - if isinstance(credential, AzureKeyCredential): - headers = kwargs.pop("headers", {}) - if "api-key" not in headers: - headers["api-key"] = credential.key - kwargs["headers"] = headers - - super().__init__(endpoint, credential, **kwargs) - - @overload - def embed( - self, - *, - input: List[_models.EmbeddingInput], - dimensions: Optional[int] = None, - encoding_format: Optional[Union[str, _models.EmbeddingEncodingFormat]] = None, - input_type: Optional[Union[str, _models.EmbeddingInputType]] = None, - model: Optional[str] = None, - model_extras: Optional[Dict[str, Any]] = None, - **kwargs: Any, - ) -> _models.EmbeddingsResult: - """Return the embedding vectors for given images. - The method makes a REST API call to the `/images/embeddings` route on the given endpoint. - - :keyword input: Input image to embed. To embed multiple inputs in a single request, pass an - array. - The input must not exceed the max input tokens for the model. Required. - :paramtype input: list[~azure.ai.inference.models.EmbeddingInput] - :keyword dimensions: Optional. The number of dimensions the resulting output embeddings should - have. Default value is None. - :paramtype dimensions: int - :keyword encoding_format: Optional. The desired format for the returned embeddings. - Known values are: - "base64", "binary", "float", "int8", "ubinary", and "uint8". Default value is None. - :paramtype encoding_format: str or ~azure.ai.inference.models.EmbeddingEncodingFormat - :keyword input_type: Optional. The type of the input. Known values are: - "text", "query", and "document". Default value is None. - :paramtype input_type: str or ~azure.ai.inference.models.EmbeddingInputType - :keyword model: ID of the specific AI model to use, if more than one model is available on the - endpoint. Default value is None. - :paramtype model: str - :keyword model_extras: Additional, model-specific parameters that are not in the - standard request payload. They will be added as-is to the root of the JSON in the request body. - How the service handles these extra parameters depends on the value of the - ``extra-parameters`` request header. Default value is None. - :paramtype model_extras: dict[str, Any] - :return: EmbeddingsResult. The EmbeddingsResult is compatible with MutableMapping - :rtype: ~azure.ai.inference.models.EmbeddingsResult - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def embed( - self, - body: JSON, - *, - content_type: str = "application/json", - **kwargs: Any, - ) -> _models.EmbeddingsResult: - """Return the embedding vectors for given images. - The method makes a REST API call to the `/images/embeddings` route on the given endpoint. - - :param body: An object of type MutableMapping[str, Any], such as a dictionary, that - specifies the full request payload. 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: EmbeddingsResult. The EmbeddingsResult is compatible with MutableMapping - :rtype: ~azure.ai.inference.models.EmbeddingsResult - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - def embed( - self, - body: IO[bytes], - *, - content_type: str = "application/json", - **kwargs: Any, - ) -> _models.EmbeddingsResult: - """Return the embedding vectors for given images. - The method makes a REST API call to the `/images/embeddings` route on the given endpoint. - - :param body: Specifies the full request payload. 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: EmbeddingsResult. The EmbeddingsResult is compatible with MutableMapping - :rtype: ~azure.ai.inference.models.EmbeddingsResult - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace - def embed( - self, - body: Union[JSON, IO[bytes]] = _Unset, - *, - input: List[_models.EmbeddingInput] = _Unset, - dimensions: Optional[int] = None, - encoding_format: Optional[Union[str, _models.EmbeddingEncodingFormat]] = None, - input_type: Optional[Union[str, _models.EmbeddingInputType]] = None, - model: Optional[str] = None, - model_extras: Optional[Dict[str, Any]] = None, - **kwargs: Any, - ) -> _models.EmbeddingsResult: - # pylint: disable=line-too-long - """Return the embedding vectors for given images. - The method makes a REST API call to the `/images/embeddings` route on the given endpoint. - - :param body: Is either a MutableMapping[str, Any] type (like a dictionary) or a IO[bytes] type - that specifies the full request payload. Required. - :type body: JSON or IO[bytes] - :keyword input: Input image to embed. To embed multiple inputs in a single request, pass an - array. - The input must not exceed the max input tokens for the model. Required. - :paramtype input: list[~azure.ai.inference.models.EmbeddingInput] - :keyword dimensions: Optional. The number of dimensions the resulting output embeddings should - have. Default value is None. - :paramtype dimensions: int - :keyword encoding_format: Optional. The desired format for the returned embeddings. - Known values are: - "base64", "binary", "float", "int8", "ubinary", and "uint8". Default value is None. - :paramtype encoding_format: str or ~azure.ai.inference.models.EmbeddingEncodingFormat - :keyword input_type: Optional. The type of the input. Known values are: - "text", "query", and "document". Default value is None. - :paramtype input_type: str or ~azure.ai.inference.models.EmbeddingInputType - :keyword model: ID of the specific AI model to use, if more than one model is available on the - endpoint. Default value is None. - :paramtype model: str - :keyword model_extras: Additional, model-specific parameters that are not in the - standard request payload. They will be added as-is to the root of the JSON in the request body. - How the service handles these extra parameters depends on the value of the - ``extra-parameters`` request header. Default value is None. - :paramtype model_extras: dict[str, Any] - :return: EmbeddingsResult. The EmbeddingsResult is compatible with MutableMapping - :rtype: ~azure.ai.inference.models.EmbeddingsResult - :raises ~azure.core.exceptions.HttpResponseError: - """ - error_map: MutableMapping[int, Type[HttpResponseError]] = { - 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 {} - _extra_parameters: Union[_models._enums.ExtraParameters, None] = None - - content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) - - if body is _Unset: - if input is _Unset: - raise TypeError("missing required argument: input") - body = { - "input": input, - "dimensions": dimensions if dimensions is not None else self._dimensions, - "encoding_format": encoding_format if encoding_format is not None else self._encoding_format, - "input_type": input_type if input_type is not None else self._input_type, - "model": model if model is not None else self._model, - } - if model_extras is not None and bool(model_extras): - body.update(model_extras) - _extra_parameters = _models._enums.ExtraParameters.PASS_THROUGH # pylint: disable=protected-access - elif self._model_extras is not None and bool(self._model_extras): - body.update(self._model_extras) - _extra_parameters = _models._enums.ExtraParameters.PASS_THROUGH # pylint: disable=protected-access - 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_image_embeddings_embed_request( - extra_params=_extra_parameters, - content_type=content_type, - api_version=self._config.api_version, - content=_content, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), - } - _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: - response.read() # Load the body in memory and close the socket - 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._patch.EmbeddingsResult, response.json() # pylint: disable=protected-access - ) - - return deserialized # type: ignore - - @distributed_trace - def get_model_info(self, **kwargs: Any) -> _models.ModelInfo: - # pylint: disable=line-too-long - """Returns information about the AI model. - The method makes a REST API call to the ``/info`` route on the given endpoint. - This method will only work when using Serverless API or Managed Compute endpoint. - It will not work for GitHub Models endpoint or Azure OpenAI endpoint. - - :return: ModelInfo. The ModelInfo is compatible with MutableMapping - :rtype: ~azure.ai.inference.models.ModelInfo - :raises ~azure.core.exceptions.HttpResponseError: - """ - if not self._model_info: - self._model_info = self._get_model_info(**kwargs) # pylint: disable=attribute-defined-outside-init - return self._model_info - - def __str__(self) -> str: - # pylint: disable=client-method-name-no-double-underscore - return super().__str__() + f"\n{self._model_info}" if self._model_info else super().__str__() - +from typing import List -__all__: List[str] = [ - "load_client", - "ChatCompletionsClient", - "EmbeddingsClient", - "ImageEmbeddingsClient", -] # 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-inference/azure/ai/inference/_serialization.py b/sdk/ai/azure-ai-inference/azure/ai/inference/_serialization.py index 8139854b97bb..b24ab2885450 100644 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/_serialization.py +++ b/sdk/ai/azure-ai-inference/azure/ai/inference/_serialization.py @@ -1,3 +1,4 @@ +# pylint: disable=too-many-lines # -------------------------------------------------------------------------- # # Copyright (c) Microsoft Corporation. All rights reserved. @@ -24,7 +25,6 @@ # # -------------------------------------------------------------------------- -# pylint: skip-file # pyright: reportUnnecessaryTypeIgnoreComment=false from base64 import b64decode, b64encode @@ -52,7 +52,6 @@ MutableMapping, Type, List, - Mapping, ) try: @@ -91,6 +90,8 @@ def deserialize_from_text(cls, data: Optional[Union[AnyStr, IO]], content_type: :param data: Input, could be bytes or stream (will be decoded with UTF8) or text :type data: str or bytes or IO :param str content_type: The content type. + :return: The deserialized data. + :rtype: object """ if hasattr(data, "read"): # Assume a stream @@ -112,7 +113,7 @@ def deserialize_from_text(cls, data: Optional[Union[AnyStr, IO]], content_type: try: return json.loads(data_as_str) except ValueError as err: - raise DeserializationError("JSON is invalid: {}".format(err), err) + raise DeserializationError("JSON is invalid: {}".format(err), err) from err elif "xml" in (content_type or []): try: @@ -155,6 +156,11 @@ def deserialize_from_http_generics(cls, body_bytes: Optional[Union[AnyStr, IO]], Use bytes and headers to NOT use any requests/aiohttp or whatever specific implementation. Headers will tested for "content-type" + + :param bytes body_bytes: The body of the response. + :param dict headers: The headers of the response. + :returns: The deserialized data. + :rtype: object """ # Try to use content-type from headers if available content_type = None @@ -184,15 +190,30 @@ class UTC(datetime.tzinfo): """Time Zone info for handling UTC""" def utcoffset(self, dt): - """UTF offset for UTC is 0.""" + """UTF offset for UTC is 0. + + :param datetime.datetime dt: The datetime + :returns: The offset + :rtype: datetime.timedelta + """ return datetime.timedelta(0) def tzname(self, dt): - """Timestamp representation.""" + """Timestamp representation. + + :param datetime.datetime dt: The datetime + :returns: The timestamp representation + :rtype: str + """ return "Z" def dst(self, dt): - """No daylight saving for UTC.""" + """No daylight saving for UTC. + + :param datetime.datetime dt: The datetime + :returns: The daylight saving time + :rtype: datetime.timedelta + """ return datetime.timedelta(hours=1) @@ -206,7 +227,7 @@ class _FixedOffset(datetime.tzinfo): # type: ignore :param datetime.timedelta offset: offset in timedelta format """ - def __init__(self, offset): + def __init__(self, offset) -> None: self.__offset = offset def utcoffset(self, dt): @@ -235,24 +256,26 @@ def __getinitargs__(self): _FLATTEN = re.compile(r"(? None: self.additional_properties: Optional[Dict[str, Any]] = {} - for k in kwargs: + for k in kwargs: # pylint: disable=consider-using-dict-items if k not in self._attribute_map: _LOGGER.warning("%s is not a known attribute of class %s and will be ignored", k, self.__class__) elif k in self._validation and self._validation[k].get("readonly", False): @@ -300,13 +330,23 @@ def __init__(self, **kwargs: Any) -> None: setattr(self, k, kwargs[k]) def __eq__(self, other: Any) -> bool: - """Compare objects by comparing all attributes.""" + """Compare objects by comparing all attributes. + + :param object other: The object to compare + :returns: True if objects are equal + :rtype: bool + """ if isinstance(other, self.__class__): return self.__dict__ == other.__dict__ return False def __ne__(self, other: Any) -> bool: - """Compare objects by comparing all attributes.""" + """Compare objects by comparing all attributes. + + :param object other: The object to compare + :returns: True if objects are not equal + :rtype: bool + """ return not self.__eq__(other) def __str__(self) -> str: @@ -326,7 +366,11 @@ def is_xml_model(cls) -> bool: @classmethod def _create_xml_node(cls): - """Create XML node.""" + """Create XML node. + + :returns: The XML node + :rtype: xml.etree.ElementTree.Element + """ try: xml_map = cls._xml_map # type: ignore except AttributeError: @@ -346,7 +390,9 @@ def serialize(self, keep_readonly: bool = False, **kwargs: Any) -> JSON: :rtype: dict """ serializer = Serializer(self._infer_class_models()) - return serializer._serialize(self, keep_readonly=keep_readonly, **kwargs) # type: ignore + return serializer._serialize( # type: ignore # pylint: disable=protected-access + self, keep_readonly=keep_readonly, **kwargs + ) def as_dict( self, @@ -380,12 +426,15 @@ def my_key_transformer(key, attr_desc, value): If you want XML serialization, you can pass the kwargs is_xml=True. + :param bool keep_readonly: If you want to serialize the readonly attributes :param function key_transformer: A key transformer function. :returns: A dict JSON compatible object :rtype: dict """ serializer = Serializer(self._infer_class_models()) - return serializer._serialize(self, key_transformer=key_transformer, keep_readonly=keep_readonly, **kwargs) # type: ignore + return serializer._serialize( # type: ignore # pylint: disable=protected-access + self, key_transformer=key_transformer, keep_readonly=keep_readonly, **kwargs + ) @classmethod def _infer_class_models(cls): @@ -395,7 +444,7 @@ def _infer_class_models(cls): client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)} if cls.__name__ not in client_models: raise ValueError("Not Autorest generated code") - except Exception: + except Exception: # pylint: disable=broad-exception-caught # Assume it's not Autorest generated (tests?). Add ourselves as dependencies. client_models = {cls.__name__: cls} return client_models @@ -408,6 +457,7 @@ def deserialize(cls: Type[ModelType], data: Any, content_type: Optional[str] = N :param str content_type: JSON by default, set application/xml if XML. :returns: An instance of this model :raises: DeserializationError if something went wrong + :rtype: ModelType """ deserializer = Deserializer(cls._infer_class_models()) return deserializer(cls.__name__, data, content_type=content_type) # type: ignore @@ -426,9 +476,11 @@ def from_dict( and last_rest_key_case_insensitive_extractor) :param dict data: A dict using RestAPI structure + :param function key_extractors: A key extractor function. :param str content_type: JSON by default, set application/xml if XML. :returns: An instance of this model :raises: DeserializationError if something went wrong + :rtype: ModelType """ deserializer = Deserializer(cls._infer_class_models()) deserializer.key_extractors = ( # type: ignore @@ -448,21 +500,25 @@ def _flatten_subtype(cls, key, objects): return {} result = dict(cls._subtype_map[key]) for valuetype in cls._subtype_map[key].values(): - result.update(objects[valuetype]._flatten_subtype(key, objects)) + result.update(objects[valuetype]._flatten_subtype(key, objects)) # pylint: disable=protected-access return result @classmethod def _classify(cls, response, objects): """Check the class _subtype_map for any child classes. We want to ignore any inherited _subtype_maps. - Remove the polymorphic key from the initial data. + + :param dict response: The initial data + :param dict objects: The class objects + :returns: The class to be used + :rtype: class """ for subtype_key in cls.__dict__.get("_subtype_map", {}).keys(): subtype_value = None if not isinstance(response, ET.Element): rest_api_response_key = cls._get_rest_key_parts(subtype_key)[-1] - subtype_value = response.pop(rest_api_response_key, None) or response.pop(subtype_key, None) + subtype_value = response.get(rest_api_response_key, None) or response.get(subtype_key, None) else: subtype_value = xml_key_extractor(subtype_key, cls._attribute_map[subtype_key], response) if subtype_value: @@ -501,11 +557,13 @@ def _decode_attribute_map_key(key): inside the received data. :param str key: A key string from the generated code + :returns: The decoded key + :rtype: str """ return key.replace("\\.", ".") -class Serializer(object): +class Serializer: # pylint: disable=too-many-public-methods """Request object model serializer.""" basic_types = {str: "str", int: "int", bool: "bool", float: "float"} @@ -540,7 +598,7 @@ class Serializer(object): "multiple": lambda x, y: x % y != 0, } - def __init__(self, classes: Optional[Mapping[str, type]] = None): + def __init__(self, classes: Optional[Mapping[str, type]] = None) -> None: self.serialize_type = { "iso-8601": Serializer.serialize_iso, "rfc-1123": Serializer.serialize_rfc, @@ -560,13 +618,16 @@ def __init__(self, classes: Optional[Mapping[str, type]] = None): self.key_transformer = full_restapi_key_transformer self.client_side_validation = True - def _serialize(self, target_obj, data_type=None, **kwargs): + def _serialize( # pylint: disable=too-many-nested-blocks, too-many-branches, too-many-statements, too-many-locals + self, target_obj, data_type=None, **kwargs + ): """Serialize data into a string according to type. - :param target_obj: The data to be serialized. + :param object target_obj: The data to be serialized. :param str data_type: The type to be serialized from. :rtype: str, dict :raises: SerializationError if serialization fails. + :returns: The serialized data. """ key_transformer = kwargs.get("key_transformer", self.key_transformer) keep_readonly = kwargs.get("keep_readonly", False) @@ -592,12 +653,14 @@ def _serialize(self, target_obj, data_type=None, **kwargs): serialized = {} if is_xml_model_serialization: - serialized = target_obj._create_xml_node() + serialized = target_obj._create_xml_node() # pylint: disable=protected-access try: - attributes = target_obj._attribute_map + attributes = target_obj._attribute_map # pylint: disable=protected-access for attr, attr_desc in attributes.items(): attr_name = attr - if not keep_readonly and target_obj._validation.get(attr_name, {}).get("readonly", False): + if not keep_readonly and target_obj._validation.get( # pylint: disable=protected-access + attr_name, {} + ).get("readonly", False): continue if attr_name == "additional_properties" and attr_desc["key"] == "": @@ -633,7 +696,8 @@ def _serialize(self, target_obj, data_type=None, **kwargs): if isinstance(new_attr, list): serialized.extend(new_attr) # type: ignore elif isinstance(new_attr, ET.Element): - # If the down XML has no XML/Name, we MUST replace the tag with the local tag. But keeping the namespaces. + # If the down XML has no XML/Name, + # we MUST replace the tag with the local tag. But keeping the namespaces. if "name" not in getattr(orig_attr, "_xml_map", {}): splitted_tag = new_attr.tag.split("}") if len(splitted_tag) == 2: # Namespace @@ -664,17 +728,17 @@ def _serialize(self, target_obj, data_type=None, **kwargs): except (AttributeError, KeyError, TypeError) as err: msg = "Attribute {} in object {} cannot be serialized.\n{}".format(attr_name, class_name, str(target_obj)) raise SerializationError(msg) from err - else: - return serialized + return serialized def body(self, data, data_type, **kwargs): """Serialize data intended for a request body. - :param data: The data to be serialized. + :param object data: The data to be serialized. :param str data_type: The type to be serialized from. :rtype: dict :raises: SerializationError if serialization fails. :raises: ValueError if data is None + :returns: The serialized request body """ # Just in case this is a dict @@ -703,7 +767,7 @@ def body(self, data, data_type, **kwargs): attribute_key_case_insensitive_extractor, last_rest_key_case_insensitive_extractor, ] - data = deserializer._deserialize(data_type, data) + data = deserializer._deserialize(data_type, data) # pylint: disable=protected-access except DeserializationError as err: raise SerializationError("Unable to build a model: " + str(err)) from err @@ -712,9 +776,11 @@ def body(self, data, data_type, **kwargs): def url(self, name, data, data_type, **kwargs): """Serialize data intended for a URL path. - :param data: The data to be serialized. + :param str name: The name of the URL path parameter. + :param object data: The data to be serialized. :param str data_type: The type to be serialized from. :rtype: str + :returns: The serialized URL path :raises: TypeError if serialization fails. :raises: ValueError if data is None """ @@ -728,21 +794,20 @@ def url(self, name, data, data_type, **kwargs): output = output.replace("{", quote("{")).replace("}", quote("}")) else: output = quote(str(output), safe="") - except SerializationError: - raise TypeError("{} must be type {}.".format(name, data_type)) - else: - return output + except SerializationError as exc: + raise TypeError("{} must be type {}.".format(name, data_type)) from exc + return output def query(self, name, data, data_type, **kwargs): """Serialize data intended for a URL query. - :param data: The data to be serialized. + :param str name: The name of the query parameter. + :param object data: The data to be serialized. :param str data_type: The type to be serialized from. - :keyword bool skip_quote: Whether to skip quote the serialized result. - Defaults to False. :rtype: str, list :raises: TypeError if serialization fails. :raises: ValueError if data is None + :returns: The serialized query parameter """ try: # Treat the list aside, since we don't want to encode the div separator @@ -759,19 +824,20 @@ def query(self, name, data, data_type, **kwargs): output = str(output) else: output = quote(str(output), safe="") - except SerializationError: - raise TypeError("{} must be type {}.".format(name, data_type)) - else: - return str(output) + except SerializationError as exc: + raise TypeError("{} must be type {}.".format(name, data_type)) from exc + return str(output) def header(self, name, data, data_type, **kwargs): """Serialize data intended for a request header. - :param data: The data to be serialized. + :param str name: The name of the header. + :param object data: The data to be serialized. :param str data_type: The type to be serialized from. :rtype: str :raises: TypeError if serialization fails. :raises: ValueError if data is None + :returns: The serialized header """ try: if data_type in ["[str]"]: @@ -780,21 +846,20 @@ def header(self, name, data, data_type, **kwargs): output = self.serialize_data(data, data_type, **kwargs) if data_type == "bool": output = json.dumps(output) - except SerializationError: - raise TypeError("{} must be type {}.".format(name, data_type)) - else: - return str(output) + except SerializationError as exc: + raise TypeError("{} must be type {}.".format(name, data_type)) from exc + return str(output) def serialize_data(self, data, data_type, **kwargs): """Serialize generic data according to supplied data type. - :param data: The data to be serialized. + :param object data: The data to be serialized. :param str data_type: The type to be serialized from. - :param bool required: Whether it's essential that the data not be - empty or None :raises: AttributeError if required data is None. :raises: ValueError if data is None :raises: SerializationError if serialization fails. + :returns: The serialized data. + :rtype: str, int, float, bool, dict, list """ if data is None: raise ValueError("No value for given attribute") @@ -805,7 +870,7 @@ def serialize_data(self, data, data_type, **kwargs): if data_type in self.basic_types.values(): return self.serialize_basic(data, data_type, **kwargs) - elif data_type in self.serialize_type: + if data_type in self.serialize_type: return self.serialize_type[data_type](data, **kwargs) # If dependencies is empty, try with current data class @@ -821,11 +886,10 @@ def serialize_data(self, data, data_type, **kwargs): except (ValueError, TypeError) as err: msg = "Unable to serialize value: {!r} as type: {!r}." raise SerializationError(msg.format(data, data_type)) from err - else: - return self._serialize(data, **kwargs) + return self._serialize(data, **kwargs) @classmethod - def _get_custom_serializers(cls, data_type, **kwargs): + def _get_custom_serializers(cls, data_type, **kwargs): # pylint: disable=inconsistent-return-statements custom_serializer = kwargs.get("basic_types_serializers", {}).get(data_type) if custom_serializer: return custom_serializer @@ -841,23 +905,26 @@ def serialize_basic(cls, data, data_type, **kwargs): - basic_types_serializers dict[str, callable] : If set, use the callable as serializer - is_xml bool : If set, use xml_basic_types_serializers - :param data: Object to be serialized. + :param obj data: Object to be serialized. :param str data_type: Type of object in the iterable. + :rtype: str, int, float, bool + :return: serialized object """ custom_serializer = cls._get_custom_serializers(data_type, **kwargs) if custom_serializer: return custom_serializer(data) if data_type == "str": return cls.serialize_unicode(data) - return eval(data_type)(data) # nosec + return eval(data_type)(data) # nosec # pylint: disable=eval-used @classmethod def serialize_unicode(cls, data): """Special handling for serializing unicode strings in Py2. Encode to UTF-8 if unicode, otherwise handle as a str. - :param data: Object to be serialized. + :param str data: Object to be serialized. :rtype: str + :return: serialized object """ try: # If I received an enum, return its value return data.value @@ -871,8 +938,7 @@ def serialize_unicode(cls, data): return data except NameError: return str(data) - else: - return str(data) + return str(data) def serialize_iter(self, data, iter_type, div=None, **kwargs): """Serialize iterable. @@ -882,15 +948,13 @@ def serialize_iter(self, data, iter_type, div=None, **kwargs): serialization_ctxt['type'] should be same as data_type. - is_xml bool : If set, serialize as XML - :param list attr: Object to be serialized. + :param list data: Object to be serialized. :param str iter_type: Type of object in the iterable. - :param bool required: Whether the objects in the iterable must - not be None or empty. :param str div: If set, this str will be used to combine the elements in the iterable into a combined string. Default is 'None'. - :keyword bool do_quote: Whether to quote the serialized result of each iterable element. Defaults to False. :rtype: list, str + :return: serialized iterable """ if isinstance(data, str): raise SerializationError("Refuse str type as a valid iter type.") @@ -945,9 +1009,8 @@ def serialize_dict(self, attr, dict_type, **kwargs): :param dict attr: Object to be serialized. :param str dict_type: Type of object in the dictionary. - :param bool required: Whether the objects in the dictionary must - not be None or empty. :rtype: dict + :return: serialized dictionary """ serialization_ctxt = kwargs.get("serialization_ctxt", {}) serialized = {} @@ -971,7 +1034,7 @@ def serialize_dict(self, attr, dict_type, **kwargs): return serialized - def serialize_object(self, attr, **kwargs): + def serialize_object(self, attr, **kwargs): # pylint: disable=too-many-return-statements """Serialize a generic object. This will be handled as a dictionary. If object passed in is not a basic type (str, int, float, dict, list) it will simply be @@ -979,6 +1042,7 @@ def serialize_object(self, attr, **kwargs): :param dict attr: Object to be serialized. :rtype: dict or str + :return: serialized object """ if attr is None: return None @@ -1003,7 +1067,7 @@ def serialize_object(self, attr, **kwargs): return self.serialize_decimal(attr) # If it's a model or I know this dependency, serialize as a Model - elif obj_type in self.dependencies.values() or isinstance(attr, Model): + if obj_type in self.dependencies.values() or isinstance(attr, Model): return self._serialize(attr) if obj_type == dict: @@ -1034,56 +1098,61 @@ def serialize_enum(attr, enum_obj=None): try: enum_obj(result) # type: ignore return result - except ValueError: + except ValueError as exc: for enum_value in enum_obj: # type: ignore if enum_value.value.lower() == str(attr).lower(): return enum_value.value error = "{!r} is not valid value for enum {!r}" - raise SerializationError(error.format(attr, enum_obj)) + raise SerializationError(error.format(attr, enum_obj)) from exc @staticmethod - def serialize_bytearray(attr, **kwargs): + def serialize_bytearray(attr, **kwargs): # pylint: disable=unused-argument """Serialize bytearray into base-64 string. - :param attr: Object to be serialized. + :param str attr: Object to be serialized. :rtype: str + :return: serialized base64 """ return b64encode(attr).decode() @staticmethod - def serialize_base64(attr, **kwargs): + def serialize_base64(attr, **kwargs): # pylint: disable=unused-argument """Serialize str into base-64 string. - :param attr: Object to be serialized. + :param str attr: Object to be serialized. :rtype: str + :return: serialized base64 """ encoded = b64encode(attr).decode("ascii") return encoded.strip("=").replace("+", "-").replace("/", "_") @staticmethod - def serialize_decimal(attr, **kwargs): + def serialize_decimal(attr, **kwargs): # pylint: disable=unused-argument """Serialize Decimal object to float. - :param attr: Object to be serialized. + :param decimal attr: Object to be serialized. :rtype: float + :return: serialized decimal """ return float(attr) @staticmethod - def serialize_long(attr, **kwargs): + def serialize_long(attr, **kwargs): # pylint: disable=unused-argument """Serialize long (Py2) or int (Py3). - :param attr: Object to be serialized. + :param int attr: Object to be serialized. :rtype: int/long + :return: serialized long """ return _long_type(attr) @staticmethod - def serialize_date(attr, **kwargs): + def serialize_date(attr, **kwargs): # pylint: disable=unused-argument """Serialize Date object into ISO-8601 formatted string. :param Date attr: Object to be serialized. :rtype: str + :return: serialized date """ if isinstance(attr, str): attr = isodate.parse_date(attr) @@ -1091,11 +1160,12 @@ def serialize_date(attr, **kwargs): return t @staticmethod - def serialize_time(attr, **kwargs): + def serialize_time(attr, **kwargs): # pylint: disable=unused-argument """Serialize Time object into ISO-8601 formatted string. :param datetime.time attr: Object to be serialized. :rtype: str + :return: serialized time """ if isinstance(attr, str): attr = isodate.parse_time(attr) @@ -1105,30 +1175,32 @@ def serialize_time(attr, **kwargs): return t @staticmethod - def serialize_duration(attr, **kwargs): + def serialize_duration(attr, **kwargs): # pylint: disable=unused-argument """Serialize TimeDelta object into ISO-8601 formatted string. :param TimeDelta attr: Object to be serialized. :rtype: str + :return: serialized duration """ if isinstance(attr, str): attr = isodate.parse_duration(attr) return isodate.duration_isoformat(attr) @staticmethod - def serialize_rfc(attr, **kwargs): + def serialize_rfc(attr, **kwargs): # pylint: disable=unused-argument """Serialize Datetime object into RFC-1123 formatted string. :param Datetime attr: Object to be serialized. :rtype: str :raises: TypeError if format invalid. + :return: serialized rfc """ try: if not attr.tzinfo: _LOGGER.warning("Datetime with no tzinfo will be considered UTC.") utc = attr.utctimetuple() - except AttributeError: - raise TypeError("RFC1123 object must be valid Datetime object.") + except AttributeError as exc: + raise TypeError("RFC1123 object must be valid Datetime object.") from exc return "{}, {:02} {} {:04} {:02}:{:02}:{:02} GMT".format( Serializer.days[utc.tm_wday], @@ -1141,12 +1213,13 @@ def serialize_rfc(attr, **kwargs): ) @staticmethod - def serialize_iso(attr, **kwargs): + def serialize_iso(attr, **kwargs): # pylint: disable=unused-argument """Serialize Datetime object into ISO-8601 formatted string. :param Datetime attr: Object to be serialized. :rtype: str :raises: SerializationError if format invalid. + :return: serialized iso """ if isinstance(attr, str): attr = isodate.parse_datetime(attr) @@ -1172,13 +1245,14 @@ def serialize_iso(attr, **kwargs): raise TypeError(msg) from err @staticmethod - def serialize_unix(attr, **kwargs): + def serialize_unix(attr, **kwargs): # pylint: disable=unused-argument """Serialize Datetime object into IntTime format. This is represented as seconds. :param Datetime attr: Object to be serialized. :rtype: int :raises: SerializationError if format invalid + :return: serialied unix """ if isinstance(attr, int): return attr @@ -1186,11 +1260,11 @@ def serialize_unix(attr, **kwargs): if not attr.tzinfo: _LOGGER.warning("Datetime with no tzinfo will be considered UTC.") return int(calendar.timegm(attr.utctimetuple())) - except AttributeError: - raise TypeError("Unix time object must be valid Datetime object.") + except AttributeError as exc: + raise TypeError("Unix time object must be valid Datetime object.") from exc -def rest_key_extractor(attr, attr_desc, data): +def rest_key_extractor(attr, attr_desc, data): # pylint: disable=unused-argument key = attr_desc["key"] working_data = data @@ -1211,7 +1285,9 @@ def rest_key_extractor(attr, attr_desc, data): return working_data.get(key) -def rest_key_case_insensitive_extractor(attr, attr_desc, data): +def rest_key_case_insensitive_extractor( # pylint: disable=unused-argument, inconsistent-return-statements + attr, attr_desc, data +): key = attr_desc["key"] working_data = data @@ -1232,17 +1308,29 @@ def rest_key_case_insensitive_extractor(attr, attr_desc, data): return attribute_key_case_insensitive_extractor(key, None, working_data) -def last_rest_key_extractor(attr, attr_desc, data): - """Extract the attribute in "data" based on the last part of the JSON path key.""" +def last_rest_key_extractor(attr, attr_desc, data): # pylint: disable=unused-argument + """Extract the attribute in "data" based on the last part of the JSON path key. + + :param str attr: The attribute to extract + :param dict attr_desc: The attribute description + :param dict data: The data to extract from + :rtype: object + :returns: The extracted attribute + """ key = attr_desc["key"] dict_keys = _FLATTEN.split(key) return attribute_key_extractor(dict_keys[-1], None, data) -def last_rest_key_case_insensitive_extractor(attr, attr_desc, data): +def last_rest_key_case_insensitive_extractor(attr, attr_desc, data): # pylint: disable=unused-argument """Extract the attribute in "data" based on the last part of the JSON path key. This is the case insensitive version of "last_rest_key_extractor" + :param str attr: The attribute to extract + :param dict attr_desc: The attribute description + :param dict data: The data to extract from + :rtype: object + :returns: The extracted attribute """ key = attr_desc["key"] dict_keys = _FLATTEN.split(key) @@ -1279,7 +1367,7 @@ def _extract_name_from_internal_type(internal_type): return xml_name -def xml_key_extractor(attr, attr_desc, data): +def xml_key_extractor(attr, attr_desc, data): # pylint: disable=unused-argument,too-many-return-statements if isinstance(data, dict): return None @@ -1331,22 +1419,21 @@ def xml_key_extractor(attr, attr_desc, data): if is_iter_type: if is_wrapped: return None # is_wrapped no node, we want None - else: - return [] # not wrapped, assume empty list + return [] # not wrapped, assume empty list return None # Assume it's not there, maybe an optional node. # If is_iter_type and not wrapped, return all found children if is_iter_type: if not is_wrapped: return children - else: # 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( - xml_name - ) + # Iter and wrapped, should have found one node only (the wrap one) + if len(children) != 1: + raise DeserializationError( + "Tried to deserialize an array not wrapped, and found several nodes '{}'. Maybe you should declare this array as wrapped?".format( # pylint: disable=line-too-long + xml_name ) - return list(children[0]) # Might be empty list and that's ok. + ) + return list(children[0]) # Might be empty list and that's ok. # Here it's not a itertype, we should have found one element only or empty if len(children) > 1: @@ -1354,7 +1441,7 @@ def xml_key_extractor(attr, attr_desc, data): return children[0] -class Deserializer(object): +class Deserializer: """Response object model deserializer. :param dict classes: Class type dictionary for deserializing complex types. @@ -1363,9 +1450,9 @@ class Deserializer(object): basic_types = {str: "str", int: "int", bool: "bool", float: "float"} - valid_date = re.compile(r"\d{4}[-]\d{2}[-]\d{2}T\d{2}:\d{2}:\d{2}" r"\.?\d*Z?[-+]?[\d{2}]?:?[\d{2}]?") + valid_date = re.compile(r"\d{4}[-]\d{2}[-]\d{2}T\d{2}:\d{2}:\d{2}\.?\d*Z?[-+]?[\d{2}]?:?[\d{2}]?") - def __init__(self, classes: Optional[Mapping[str, type]] = None): + def __init__(self, classes: Optional[Mapping[str, type]] = None) -> None: self.deserialize_type = { "iso-8601": Deserializer.deserialize_iso, "rfc-1123": Deserializer.deserialize_rfc, @@ -1403,11 +1490,12 @@ def __call__(self, target_obj, response_data, content_type=None): :param str content_type: Swagger "produces" if available. :raises: DeserializationError if deserialization fails. :return: Deserialized object. + :rtype: object """ data = self._unpack_content(response_data, content_type) return self._deserialize(target_obj, data) - def _deserialize(self, target_obj, data): + def _deserialize(self, target_obj, data): # pylint: disable=inconsistent-return-statements """Call the deserializer on a model. Data needs to be already deserialized as JSON or XML ElementTree @@ -1416,12 +1504,13 @@ def _deserialize(self, target_obj, data): :param object data: Object to deserialize. :raises: DeserializationError if deserialization fails. :return: Deserialized object. + :rtype: object """ # This is already a model, go recursive just in case if hasattr(data, "_attribute_map"): constants = [name for name, config in getattr(data, "_validation", {}).items() if config.get("constant")] try: - for attr, mapconfig in data._attribute_map.items(): + for attr, mapconfig in data._attribute_map.items(): # pylint: disable=protected-access if attr in constants: continue value = getattr(data, attr) @@ -1440,13 +1529,13 @@ def _deserialize(self, target_obj, data): if isinstance(response, str): return self.deserialize_data(data, response) - elif isinstance(response, type) and issubclass(response, Enum): + if isinstance(response, type) and issubclass(response, Enum): return self.deserialize_enum(data, response) if data is None or data is CoreNull: return data try: - attributes = response._attribute_map # type: ignore + attributes = response._attribute_map # type: ignore # pylint: disable=protected-access d_attrs = {} for attr, attr_desc in attributes.items(): # Check empty string. If it's not empty, someone has a real "additionalProperties"... @@ -1476,9 +1565,8 @@ def _deserialize(self, target_obj, data): except (AttributeError, TypeError, KeyError) as err: msg = "Unable to deserialize to object: " + class_name # type: ignore raise DeserializationError(msg) from err - else: - additional_properties = self._build_additional_properties(attributes, data) - return self._instantiate_model(response, d_attrs, additional_properties) + additional_properties = self._build_additional_properties(attributes, data) + return self._instantiate_model(response, d_attrs, additional_properties) def _build_additional_properties(self, attribute_map, data): if not self.additional_properties_detection: @@ -1505,6 +1593,8 @@ def _classify_target(self, target, data): :param str target: The target object type to deserialize to. :param str/dict data: The response data to deserialize. + :return: The classified target object and its class name. + :rtype: tuple """ if target is None: return None, None @@ -1516,7 +1606,7 @@ def _classify_target(self, target, data): return target, target try: - target = target._classify(data, self.dependencies) # type: ignore + target = target._classify(data, self.dependencies) # type: ignore # pylint: disable=protected-access except AttributeError: pass # Target is not a Model, no classify return target, target.__class__.__name__ # type: ignore @@ -1531,10 +1621,12 @@ def failsafe_deserialize(self, target_obj, data, content_type=None): :param str target_obj: The target object type to deserialize to. :param str/dict data: The response data to deserialize. :param str content_type: Swagger "produces" if available. + :return: Deserialized object. + :rtype: object """ try: return self(target_obj, data, content_type=content_type) - except: + except: # pylint: disable=bare-except _LOGGER.debug( "Ran into a deserialization error. Ignoring since this is failsafe deserialization", exc_info=True ) @@ -1552,10 +1644,12 @@ def _unpack_content(raw_data, content_type=None): If raw_data is something else, bypass all logic and return it directly. - :param raw_data: Data to be processed. - :param content_type: How to parse if raw_data is a string/bytes. + :param obj raw_data: Data to be processed. + :param str content_type: How to parse if raw_data is a string/bytes. :raises JSONDecodeError: If JSON is requested and parsing is impossible. :raises UnicodeDecodeError: If bytes is not UTF8 + :rtype: object + :return: Unpacked content. """ # Assume this is enough to detect a Pipeline Response without importing it context = getattr(raw_data, "context", {}) @@ -1579,24 +1673,35 @@ def _unpack_content(raw_data, content_type=None): def _instantiate_model(self, response, attrs, additional_properties=None): """Instantiate a response model passing in deserialized args. - :param response: The response model class. - :param d_attrs: The deserialized response attributes. + :param Response response: The response model class. + :param dict attrs: The deserialized response attributes. + :param dict additional_properties: Additional properties to be set. + :rtype: Response + :return: The instantiated response model. """ if callable(response): subtype = getattr(response, "_subtype_map", {}) try: - readonly = [k for k, v in response._validation.items() if v.get("readonly")] - const = [k for k, v in response._validation.items() if v.get("constant")] + readonly = [ + k + for k, v in response._validation.items() # pylint: disable=protected-access # type: ignore + if v.get("readonly") + ] + const = [ + k + for k, v in response._validation.items() # pylint: disable=protected-access # type: ignore + if v.get("constant") + ] kwargs = {k: v for k, v in attrs.items() if k not in subtype and k not in readonly + const} response_obj = response(**kwargs) for attr in readonly: setattr(response_obj, attr, attrs.get(attr)) if additional_properties: - response_obj.additional_properties = additional_properties + response_obj.additional_properties = additional_properties # type: ignore return response_obj except TypeError as err: msg = "Unable to deserialize {} into model {}. ".format(kwargs, response) # type: ignore - raise DeserializationError(msg + str(err)) + raise DeserializationError(msg + str(err)) from err else: try: for attr, value in attrs.items(): @@ -1605,15 +1710,16 @@ def _instantiate_model(self, response, attrs, additional_properties=None): except Exception as exp: msg = "Unable to populate response model. " msg += "Type: {}, Error: {}".format(type(response), exp) - raise DeserializationError(msg) + raise DeserializationError(msg) from exp - def deserialize_data(self, data, data_type): + def deserialize_data(self, data, data_type): # pylint: disable=too-many-return-statements """Process data for deserialization according to data type. :param str data: The response string to be deserialized. :param str data_type: The type to deserialize to. :raises: DeserializationError if deserialization fails. :return: Deserialized object. + :rtype: object """ if data is None: return data @@ -1627,7 +1733,11 @@ def deserialize_data(self, data, data_type): if isinstance(data, self.deserialize_expected_types.get(data_type, tuple())): return data - is_a_text_parsing_type = lambda x: x not in ["object", "[]", r"{}"] + is_a_text_parsing_type = lambda x: x not in [ # pylint: disable=unnecessary-lambda-assignment + "object", + "[]", + r"{}", + ] if isinstance(data, ET.Element) and is_a_text_parsing_type(data_type) and not data.text: return None data_val = self.deserialize_type[data_type](data) @@ -1647,14 +1757,14 @@ def deserialize_data(self, data, data_type): msg = "Unable to deserialize response data." msg += " Data: {}, {}".format(data, data_type) raise DeserializationError(msg) from err - else: - return self._deserialize(obj_type, data) + return self._deserialize(obj_type, data) def deserialize_iter(self, attr, iter_type): """Deserialize an iterable. :param list attr: Iterable to be deserialized. :param str iter_type: The type of object in the iterable. + :return: Deserialized iterable. :rtype: list """ if attr is None: @@ -1671,6 +1781,7 @@ def deserialize_dict(self, attr, dict_type): :param dict/list attr: Dictionary to be deserialized. Also accepts a list of key, value pairs. :param str dict_type: The object type of the items in the dictionary. + :return: Deserialized dictionary. :rtype: dict """ if isinstance(attr, list): @@ -1681,11 +1792,12 @@ def deserialize_dict(self, attr, dict_type): attr = {el.tag: el.text for el in attr} return {k: self.deserialize_data(v, dict_type) for k, v in attr.items()} - def deserialize_object(self, attr, **kwargs): + def deserialize_object(self, attr, **kwargs): # pylint: disable=too-many-return-statements """Deserialize a generic object. This will be handled as a dictionary. :param dict attr: Dictionary to be deserialized. + :return: Deserialized object. :rtype: dict :raises: TypeError if non-builtin datatype encountered. """ @@ -1720,11 +1832,10 @@ def deserialize_object(self, attr, **kwargs): pass return deserialized - else: - error = "Cannot deserialize generic object with type: " - raise TypeError(error + str(obj_type)) + error = "Cannot deserialize generic object with type: " + raise TypeError(error + str(obj_type)) - def deserialize_basic(self, attr, data_type): + def deserialize_basic(self, attr, data_type): # pylint: disable=too-many-return-statements """Deserialize basic builtin data type from string. Will attempt to convert to str, int, float and bool. This function will also accept '1', '0', 'true' and 'false' as @@ -1732,6 +1843,7 @@ def deserialize_basic(self, attr, data_type): :param str attr: response string to be deserialized. :param str data_type: deserialization data type. + :return: Deserialized basic type. :rtype: str, int, float or bool :raises: TypeError if string format is not valid. """ @@ -1743,24 +1855,23 @@ def deserialize_basic(self, attr, data_type): if data_type == "str": # None or '', node is empty string. return "" - else: - # None or '', node with a strong type is None. - # Don't try to model "empty bool" or "empty int" - return None + # None or '', node with a strong type is None. + # Don't try to model "empty bool" or "empty int" + return None if data_type == "bool": if attr in [True, False, 1, 0]: return bool(attr) - elif isinstance(attr, str): + if isinstance(attr, str): if attr.lower() in ["true", "1"]: return True - elif attr.lower() in ["false", "0"]: + if attr.lower() in ["false", "0"]: return False raise TypeError("Invalid boolean value: {}".format(attr)) if data_type == "str": return self.deserialize_unicode(attr) - return eval(data_type)(attr) # nosec + return eval(data_type)(attr) # nosec # pylint: disable=eval-used @staticmethod def deserialize_unicode(data): @@ -1768,6 +1879,7 @@ def deserialize_unicode(data): as a string. :param str data: response string to be deserialized. + :return: Deserialized string. :rtype: str or unicode """ # We might be here because we have an enum modeled as string, @@ -1781,8 +1893,7 @@ def deserialize_unicode(data): return data except NameError: return str(data) - else: - return str(data) + return str(data) @staticmethod def deserialize_enum(data, enum_obj): @@ -1794,6 +1905,7 @@ def deserialize_enum(data, enum_obj): :param str data: Response string to be deserialized. If this value is None or invalid it will be returned as-is. :param Enum enum_obj: Enum object to deserialize to. + :return: Deserialized enum object. :rtype: Enum """ if isinstance(data, enum_obj) or data is None: @@ -1804,9 +1916,9 @@ def deserialize_enum(data, enum_obj): # Workaround. We might consider remove it in the future. try: return list(enum_obj.__members__.values())[data] - except IndexError: + except IndexError as exc: error = "{!r} is not a valid index for enum {!r}" - raise DeserializationError(error.format(data, enum_obj)) + raise DeserializationError(error.format(data, enum_obj)) from exc try: return enum_obj(str(data)) except ValueError: @@ -1822,6 +1934,7 @@ def deserialize_bytearray(attr): """Deserialize string into bytearray. :param str attr: response string to be deserialized. + :return: Deserialized bytearray :rtype: bytearray :raises: TypeError if string format invalid. """ @@ -1834,6 +1947,7 @@ def deserialize_base64(attr): """Deserialize base64 encoded string into string. :param str attr: response string to be deserialized. + :return: Deserialized base64 string :rtype: bytearray :raises: TypeError if string format invalid. """ @@ -1849,8 +1963,9 @@ def deserialize_decimal(attr): """Deserialize string into Decimal object. :param str attr: response string to be deserialized. - :rtype: Decimal + :return: Deserialized decimal :raises: DeserializationError if string format invalid. + :rtype: decimal """ if isinstance(attr, ET.Element): attr = attr.text @@ -1865,6 +1980,7 @@ def deserialize_long(attr): """Deserialize string into long (Py2) or int (Py3). :param str attr: response string to be deserialized. + :return: Deserialized int :rtype: long or int :raises: ValueError if string format invalid. """ @@ -1877,6 +1993,7 @@ def deserialize_duration(attr): """Deserialize ISO-8601 formatted string into TimeDelta object. :param str attr: response string to be deserialized. + :return: Deserialized duration :rtype: TimeDelta :raises: DeserializationError if string format invalid. """ @@ -1887,14 +2004,14 @@ def deserialize_duration(attr): except (ValueError, OverflowError, AttributeError) as err: msg = "Cannot deserialize duration object." raise DeserializationError(msg) from err - else: - return duration + return duration @staticmethod def deserialize_date(attr): """Deserialize ISO-8601 formatted string into Date object. :param str attr: response string to be deserialized. + :return: Deserialized date :rtype: Date :raises: DeserializationError if string format invalid. """ @@ -1910,6 +2027,7 @@ def deserialize_time(attr): """Deserialize ISO-8601 formatted string into time object. :param str attr: response string to be deserialized. + :return: Deserialized time :rtype: datetime.time :raises: DeserializationError if string format invalid. """ @@ -1924,6 +2042,7 @@ def deserialize_rfc(attr): """Deserialize RFC-1123 formatted string into Datetime object. :param str attr: response string to be deserialized. + :return: Deserialized RFC datetime :rtype: Datetime :raises: DeserializationError if string format invalid. """ @@ -1939,14 +2058,14 @@ def deserialize_rfc(attr): except ValueError as err: msg = "Cannot deserialize to rfc datetime object." raise DeserializationError(msg) from err - else: - return date_obj + return date_obj @staticmethod def deserialize_iso(attr): """Deserialize ISO-8601 formatted string into Datetime object. :param str attr: response string to be deserialized. + :return: Deserialized ISO datetime :rtype: Datetime :raises: DeserializationError if string format invalid. """ @@ -1976,8 +2095,7 @@ def deserialize_iso(attr): except (ValueError, OverflowError, AttributeError) as err: msg = "Cannot deserialize datetime object." raise DeserializationError(msg) from err - else: - return date_obj + return date_obj @staticmethod def deserialize_unix(attr): @@ -1985,6 +2103,7 @@ def deserialize_unix(attr): This is represented as seconds. :param int attr: Object to be serialized. + :return: Deserialized datetime :rtype: Datetime :raises: DeserializationError if format invalid """ @@ -1996,5 +2115,4 @@ def deserialize_unix(attr): except ValueError as err: msg = "Cannot deserialize to unix datetime object." raise DeserializationError(msg) from err - else: - return date_obj + return date_obj diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/_vendor.py b/sdk/ai/azure-ai-inference/azure/ai/inference/_vendor.py index 8ea240fb008b..147e96be133e 100644 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/_vendor.py +++ b/sdk/ai/azure-ai-inference/azure/ai/inference/_vendor.py @@ -15,7 +15,6 @@ ) if TYPE_CHECKING: - # pylint: disable=unused-import,ungrouped-imports from azure.core import PipelineClient from ._serialization import Deserializer, Serializer diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/_version.py b/sdk/ai/azure-ai-inference/azure/ai/inference/_version.py index 84058978c521..be71c81bd282 100644 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/_version.py +++ b/sdk/ai/azure-ai-inference/azure/ai/inference/_version.py @@ -6,4 +6,4 @@ # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- -VERSION = "1.0.0b7" +VERSION = "1.0.0b1" diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/aio/__init__.py b/sdk/ai/azure-ai-inference/azure/ai/inference/aio/__init__.py index c31764c00803..668f989a5838 100644 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/aio/__init__.py +++ b/sdk/ai/azure-ai-inference/azure/ai/inference/aio/__init__.py @@ -5,21 +5,29 @@ # Code generated by Microsoft (R) Python Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- +# pylint: disable=wrong-import-position -from ._patch import ChatCompletionsClient -from ._patch import EmbeddingsClient -from ._patch import ImageEmbeddingsClient +from typing import TYPE_CHECKING +if TYPE_CHECKING: + from ._patch import * # pylint: disable=unused-wildcard-import -from ._patch import load_client +from ._client import ChatCompletionsClient # type: ignore +from ._client import EmbeddingsClient # type: ignore +from ._client import ImageEmbeddingsClient # type: ignore + +try: + from ._patch import __all__ as _patch_all + from ._patch import * +except ImportError: + _patch_all = [] from ._patch import patch_sdk as _patch_sdk __all__ = [ - "load_client", "ChatCompletionsClient", "EmbeddingsClient", "ImageEmbeddingsClient", ] - +__all__.extend([p for p in _patch_all if p not in __all__]) # pyright: ignore _patch_sdk() diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/aio/_client.py b/sdk/ai/azure-ai-inference/azure/ai/inference/aio/_client.py index 30c7afbfbd91..7cea61120519 100644 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/aio/_client.py +++ b/sdk/ai/azure-ai-inference/azure/ai/inference/aio/_client.py @@ -28,11 +28,10 @@ ) if TYPE_CHECKING: - # pylint: disable=unused-import,ungrouped-imports from azure.core.credentials_async import AsyncTokenCredential -class ChatCompletionsClient(ChatCompletionsClientOperationsMixin): # pylint: disable=client-accepts-api-version-keyword +class ChatCompletionsClient(ChatCompletionsClientOperationsMixin): """ChatCompletionsClient. :param endpoint: Service host. Required. @@ -115,7 +114,7 @@ async def __aexit__(self, *exc_details: Any) -> None: await self._client.__aexit__(*exc_details) -class EmbeddingsClient(EmbeddingsClientOperationsMixin): # pylint: disable=client-accepts-api-version-keyword +class EmbeddingsClient(EmbeddingsClientOperationsMixin): """EmbeddingsClient. :param endpoint: Service host. Required. @@ -198,7 +197,7 @@ async def __aexit__(self, *exc_details: Any) -> None: await self._client.__aexit__(*exc_details) -class ImageEmbeddingsClient(ImageEmbeddingsClientOperationsMixin): # pylint: disable=client-accepts-api-version-keyword +class ImageEmbeddingsClient(ImageEmbeddingsClientOperationsMixin): """ImageEmbeddingsClient. :param endpoint: Service host. Required. diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/aio/_configuration.py b/sdk/ai/azure-ai-inference/azure/ai/inference/aio/_configuration.py index e4c5d7111d22..2eee5cfe60cb 100644 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/aio/_configuration.py +++ b/sdk/ai/azure-ai-inference/azure/ai/inference/aio/_configuration.py @@ -14,11 +14,10 @@ from .._version import VERSION if TYPE_CHECKING: - # pylint: disable=unused-import,ungrouped-imports from azure.core.credentials_async import AsyncTokenCredential -class ChatCompletionsClientConfiguration: # pylint: disable=too-many-instance-attributes,name-too-long +class ChatCompletionsClientConfiguration: # pylint: disable=too-many-instance-attributes """Configuration for ChatCompletionsClient. Note that all parameters used to create this instance are saved as instance @@ -78,7 +77,7 @@ def _configure(self, **kwargs: Any) -> None: self.authentication_policy = self._infer_policy(**kwargs) -class EmbeddingsClientConfiguration: # pylint: disable=too-many-instance-attributes,name-too-long +class EmbeddingsClientConfiguration: # pylint: disable=too-many-instance-attributes """Configuration for EmbeddingsClient. Note that all parameters used to create this instance are saved as instance @@ -138,7 +137,7 @@ def _configure(self, **kwargs: Any) -> None: self.authentication_policy = self._infer_policy(**kwargs) -class ImageEmbeddingsClientConfiguration: # pylint: disable=too-many-instance-attributes,name-too-long +class ImageEmbeddingsClientConfiguration: # pylint: disable=too-many-instance-attributes """Configuration for ImageEmbeddingsClient. Note that all parameters used to create this instance are saved as instance diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/aio/_operations/__init__.py b/sdk/ai/azure-ai-inference/azure/ai/inference/aio/_operations/__init__.py index d3ebd561f739..ab87088736aa 100644 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/aio/_operations/__init__.py +++ b/sdk/ai/azure-ai-inference/azure/ai/inference/aio/_operations/__init__.py @@ -5,13 +5,19 @@ # Code generated by Microsoft (R) Python Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- +# pylint: disable=wrong-import-position -from ._operations import ChatCompletionsClientOperationsMixin -from ._operations import EmbeddingsClientOperationsMixin -from ._operations import ImageEmbeddingsClientOperationsMixin +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from ._patch import * # pylint: disable=unused-wildcard-import + +from ._operations import ChatCompletionsClientOperationsMixin # type: ignore +from ._operations import EmbeddingsClientOperationsMixin # type: ignore +from ._operations import ImageEmbeddingsClientOperationsMixin # type: ignore from ._patch import __all__ as _patch_all -from ._patch import * # pylint: disable=unused-wildcard-import +from ._patch import * from ._patch import patch_sdk as _patch_sdk __all__ = [ @@ -19,5 +25,5 @@ "EmbeddingsClientOperationsMixin", "ImageEmbeddingsClientOperationsMixin", ] -__all__.extend([p for p in _patch_all if p not in __all__]) +__all__.extend([p for p in _patch_all if p not in __all__]) # pyright: ignore _patch_sdk() diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/aio/_operations/_operations.py b/sdk/ai/azure-ai-inference/azure/ai/inference/aio/_operations/_operations.py index 0be948bd275d..8a346ca68cd4 100644 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/aio/_operations/_operations.py +++ b/sdk/ai/azure-ai-inference/azure/ai/inference/aio/_operations/_operations.py @@ -1,4 +1,3 @@ -# pylint: disable=too-many-lines,too-many-statements # coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. @@ -9,7 +8,7 @@ from io import IOBase import json import sys -from typing import Any, Callable, Dict, IO, List, Optional, Type, TypeVar, Union, overload +from typing import Any, Callable, Dict, IO, List, Optional, TypeVar, Union, overload from azure.core.exceptions import ( ClientAuthenticationError, @@ -41,7 +40,7 @@ if sys.version_info >= (3, 9): from collections.abc import MutableMapping else: - from typing import MutableMapping # type: ignore # pylint: disable=ungrouped-imports + from typing import MutableMapping # type: ignore JSON = MutableMapping[str, Any] # pylint: disable=unsubscriptable-object _Unset: Any = object() T = TypeVar("T") @@ -115,7 +114,6 @@ async def _complete( model: Optional[str] = None, **kwargs: Any ) -> _models.ChatCompletions: - # pylint: disable=too-many-locals """Gets chat completions for the provided chat messages. Completions support a wide variety of tasks and generate text that continues from or "completes" @@ -172,11 +170,20 @@ async def _complete( :paramtype top_p: float :keyword max_tokens: The maximum number of tokens to generate. Default value is None. :paramtype max_tokens: int - :keyword response_format: The format that the model must output. Use this to enable JSON mode - instead of the default text mode. - Note that to enable JSON mode, some AI models may also require you to instruct the model to - produce JSON - via a system or user message. Default value is None. + :keyword response_format: An object specifying the format that the model must output. + + Setting to ``{ "type": "json_schema", "json_schema": {...} }`` enables Structured Outputs + which ensures the model will match your supplied JSON schema. + + Setting to ``{ "type": "json_object" }`` enables JSON mode, which ensures the message the + model generates is valid JSON. + + **Important:** when using JSON mode, you **must** also instruct the model to produce JSON + yourself via a system or user message. Without this, the model may generate an unending stream + of whitespace until the generation reaches the token limit, resulting in a long-running and + seemingly "stuck" request. Also note that the message content may be partially cut off if + ``finish_reason="length"``\\ , which indicates the generation exceeded ``max_tokens`` or the + conversation exceeded the max context length. Default value is None. :paramtype response_format: ~azure.ai.inference.models.ChatCompletionsResponseFormat :keyword stop: A collection of textual sequences that will end completions generation. Default value is None. @@ -204,7 +211,7 @@ async def _complete( :rtype: ~azure.ai.inference.models.ChatCompletions :raises ~azure.core.exceptions.HttpResponseError: """ - error_map: MutableMapping[int, Type[HttpResponseError]] = { + error_map: MutableMapping = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError, @@ -294,7 +301,7 @@ async def _get_model_info(self, **kwargs: Any) -> _models.ModelInfo: :rtype: ~azure.ai.inference.models.ModelInfo :raises ~azure.core.exceptions.HttpResponseError: """ - error_map: MutableMapping[int, Type[HttpResponseError]] = { + error_map: MutableMapping = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError, @@ -425,7 +432,7 @@ async def _embed( :rtype: ~azure.ai.inference.models.EmbeddingsResult :raises ~azure.core.exceptions.HttpResponseError: """ - error_map: MutableMapping[int, Type[HttpResponseError]] = { + error_map: MutableMapping = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError, @@ -507,7 +514,7 @@ async def _get_model_info(self, **kwargs: Any) -> _models.ModelInfo: :rtype: ~azure.ai.inference.models.ModelInfo :raises ~azure.core.exceptions.HttpResponseError: """ - error_map: MutableMapping[int, Type[HttpResponseError]] = { + error_map: MutableMapping = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError, @@ -572,7 +579,7 @@ async def _embed( async def _embed( self, *, - input: List[_models.EmbeddingInput], + input: List[_models.ImageEmbeddingInput], extra_params: Optional[Union[str, _models._enums.ExtraParameters]] = None, content_type: str = "application/json", dimensions: Optional[int] = None, @@ -596,7 +603,7 @@ async def _embed( self, body: Union[JSON, IO[bytes]] = _Unset, *, - input: List[_models.EmbeddingInput] = _Unset, + input: List[_models.ImageEmbeddingInput] = _Unset, extra_params: Optional[Union[str, _models._enums.ExtraParameters]] = None, dimensions: Optional[int] = None, encoding_format: Optional[Union[str, _models.EmbeddingEncodingFormat]] = None, @@ -612,7 +619,7 @@ async def _embed( :keyword input: Input image to embed. To embed multiple inputs in a single request, pass an array. The input must not exceed the max input tokens for the model. Required. - :paramtype input: list[~azure.ai.inference.models.EmbeddingInput] + :paramtype input: list[~azure.ai.inference.models.ImageEmbeddingInput] :keyword extra_params: Controls what happens if extra parameters, undefined by the REST API, are passed in the JSON request payload. This sets the HTTP request header ``extra-parameters``. Known values are: "error", "drop", and @@ -641,7 +648,7 @@ async def _embed( :rtype: ~azure.ai.inference.models.EmbeddingsResult :raises ~azure.core.exceptions.HttpResponseError: """ - error_map: MutableMapping[int, Type[HttpResponseError]] = { + error_map: MutableMapping = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError, @@ -723,7 +730,7 @@ async def _get_model_info(self, **kwargs: Any) -> _models.ModelInfo: :rtype: ~azure.ai.inference.models.ModelInfo :raises ~azure.core.exceptions.HttpResponseError: """ - error_map: MutableMapping[int, Type[HttpResponseError]] = { + error_map: MutableMapping = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError, diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/aio/_patch.py b/sdk/ai/azure-ai-inference/azure/ai/inference/aio/_patch.py index 2bdfd67a40cb..f7dd32510333 100644 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/aio/_patch.py +++ b/sdk/ai/azure-ai-inference/azure/ai/inference/aio/_patch.py @@ -2,1266 +2,13 @@ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. # ------------------------------------ -# pylint: disable=too-many-lines) """Customize generated code here. Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize """ -import json -import logging -import sys +from typing import List -from io import IOBase -from typing import Any, Dict, Union, IO, List, Literal, Optional, overload, Type, TYPE_CHECKING, AsyncIterable - -from azure.core.pipeline import PipelineResponse -from azure.core.credentials import AzureKeyCredential -from azure.core.tracing.decorator_async import distributed_trace_async -from azure.core.utils import case_insensitive_dict -from azure.core.exceptions import ( - ClientAuthenticationError, - HttpResponseError, - map_error, - ResourceExistsError, - ResourceNotFoundError, - ResourceNotModifiedError, -) -from .. import models as _models -from .._model_base import SdkJSONEncoder, _deserialize -from ._client import ChatCompletionsClient as ChatCompletionsClientGenerated -from ._client import EmbeddingsClient as EmbeddingsClientGenerated -from ._client import ImageEmbeddingsClient as ImageEmbeddingsClientGenerated -from .._operations._operations import ( - build_chat_completions_complete_request, - build_embeddings_embed_request, - build_image_embeddings_embed_request, -) - -if TYPE_CHECKING: - # pylint: disable=unused-import,ungrouped-imports - from azure.core.credentials_async import AsyncTokenCredential - -if sys.version_info >= (3, 9): - from collections.abc import MutableMapping -else: - from typing import MutableMapping # type: ignore # pylint: disable=ungrouped-imports - -JSON = MutableMapping[str, Any] # pylint: disable=unsubscriptable-object -_Unset: Any = object() -_LOGGER = logging.getLogger(__name__) - - -async def load_client( - endpoint: str, credential: Union[AzureKeyCredential, "AsyncTokenCredential"], **kwargs: Any -) -> Union["ChatCompletionsClient", "EmbeddingsClient", "ImageEmbeddingsClient"]: - # pylint: disable=line-too-long - """ - Load a client from a given endpoint URL. The method makes a REST API call to the `/info` route - on the given endpoint, to determine the model type and therefore which client to instantiate. - - :param endpoint: Service host. Required. - :type endpoint: str - :param credential: Credential used to authenticate requests to the service. Is either a - AzureKeyCredential type or a AsyncTokenCredential type. Required. - :type credential: ~azure.core.credentials.AzureKeyCredential or - ~azure.core.credentials_async.AsyncTokenCredential - :keyword api_version: The API version to use for this operation. Default value is - "2024-05-01-preview". Note that overriding this default value may result in unsupported - behavior. - :paramtype api_version: str - :return: The appropriate asynchronous client associated with the given endpoint - :rtype: ~azure.ai.inference.aio.ChatCompletionsClient or ~azure.ai.inference.aio.EmbeddingsClient - or ~azure.ai.inference.aio.ImageEmbeddingsClient - :raises ~azure.core.exceptions.HttpResponseError: - """ - - async with ChatCompletionsClient( - endpoint, credential, **kwargs - ) as client: # Pick any of the clients, it does not matter. - model_info = await client.get_model_info() # type: ignore - - _LOGGER.info("model_info=%s", model_info) - if not model_info.model_type: - raise ValueError( - "The AI model information is missing a value for `model type`. Cannot create an appropriate client." - ) - - # TODO: Remove "completions" and "embedding" once Mistral Large and Cohere fixes their model type - if model_info.model_type in (_models.ModelType.CHAT, "completion", "chat-completion", "chat-completions"): - chat_completion_client = ChatCompletionsClient(endpoint, credential, **kwargs) - chat_completion_client._model_info = ( # pylint: disable=protected-access,attribute-defined-outside-init - model_info - ) - return chat_completion_client - - if model_info.model_type in (_models.ModelType.EMBEDDINGS, "embedding"): - embedding_client = EmbeddingsClient(endpoint, credential, **kwargs) - embedding_client._model_info = model_info # pylint: disable=protected-access,attribute-defined-outside-init - return embedding_client - - if model_info.model_type == _models.ModelType.IMAGE_EMBEDDINGS: - image_embedding_client = ImageEmbeddingsClient(endpoint, credential, **kwargs) - image_embedding_client._model_info = ( # pylint: disable=protected-access,attribute-defined-outside-init - model_info - ) - return image_embedding_client - - raise ValueError(f"No client available to support AI model type `{model_info.model_type}`") - - -class ChatCompletionsClient(ChatCompletionsClientGenerated): # pylint: disable=too-many-instance-attributes - """ChatCompletionsClient. - - :param endpoint: Service host. Required. - :type endpoint: str - :param credential: Credential used to authenticate requests to the service. Is either a - AzureKeyCredential type or a AsyncTokenCredential type. Required. - :type credential: ~azure.core.credentials.AzureKeyCredential or - ~azure.core.credentials_async.AsyncTokenCredential - :keyword frequency_penalty: A value that influences the probability of generated tokens - appearing based on their cumulative frequency in generated text. - Positive values will make tokens less likely to appear as their frequency increases and - decrease the likelihood of the model repeating the same statements verbatim. - Supported range is [-2, 2]. - Default value is None. - :paramtype frequency_penalty: float - :keyword presence_penalty: A value that influences the probability of generated tokens - appearing based on their existing - presence in generated text. - Positive values will make tokens less likely to appear when they already exist and increase - the model's likelihood to output new topics. - Supported range is [-2, 2]. - Default value is None. - :paramtype presence_penalty: float - :keyword temperature: The sampling temperature to use that controls the apparent creativity of - generated completions. - Higher values will make output more random while lower values will make results more focused - and deterministic. - It is not recommended to modify temperature and top_p for the same completions request as the - interaction of these two settings is difficult to predict. - Supported range is [0, 1]. - Default value is None. - :paramtype temperature: float - :keyword top_p: An alternative to sampling with temperature called nucleus sampling. This value - causes the - model to consider the results of tokens with the provided probability mass. As an example, a - value of 0.15 will cause only the tokens comprising the top 15% of probability mass to be - considered. - It is not recommended to modify temperature and top_p for the same completions request as the - interaction of these two settings is difficult to predict. - Supported range is [0, 1]. - Default value is None. - :paramtype top_p: float - :keyword max_tokens: The maximum number of tokens to generate. Default value is None. - :paramtype max_tokens: int - :keyword response_format: The format that the model must output. Use this to enable JSON mode - instead of the default text mode. - Note that to enable JSON mode, some AI models may also require you to instruct the model to - produce JSON via a system or user message. Default value is None. - :paramtype response_format: ~azure.ai.inference.models.ChatCompletionsResponseFormat - :keyword stop: A collection of textual sequences that will end completions generation. Default - value is None. - :paramtype stop: list[str] - :keyword tools: The available tool definitions that the chat completions request can use, - including caller-defined functions. Default value is None. - :paramtype tools: list[~azure.ai.inference.models.ChatCompletionsToolDefinition] - :keyword tool_choice: If specified, the model will configure which of the provided tools it can - use for the chat completions response. Is either a Union[str, - "_models.ChatCompletionsToolChoicePreset"] type or a ChatCompletionsNamedToolChoice type. - Default value is None. - :paramtype tool_choice: str or ~azure.ai.inference.models.ChatCompletionsToolChoicePreset or - ~azure.ai.inference.models.ChatCompletionsNamedToolChoice - :keyword seed: If specified, the system will make a best effort to sample deterministically - such that repeated requests with the - same seed and parameters should return the same result. Determinism is not guaranteed. - Default value is None. - :paramtype seed: int - :keyword model: ID of the specific AI model to use, if more than one model is available on the - endpoint. Default value is None. - :paramtype model: str - :keyword model_extras: Additional, model-specific parameters that are not in the - standard request payload. They will be added as-is to the root of the JSON in the request body. - How the service handles these extra parameters depends on the value of the - ``extra-parameters`` request header. Default value is None. - :paramtype model_extras: dict[str, Any] - :keyword api_version: The API version to use for this operation. Default value is - "2024-05-01-preview". Note that overriding this default value may result in unsupported - behavior. - :paramtype api_version: str - """ - - def __init__( - self, - endpoint: str, - credential: Union[AzureKeyCredential, "AsyncTokenCredential"], - *, - frequency_penalty: Optional[float] = None, - presence_penalty: Optional[float] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - max_tokens: Optional[int] = None, - response_format: Optional[_models.ChatCompletionsResponseFormat] = None, - stop: Optional[List[str]] = None, - tools: Optional[List[_models.ChatCompletionsToolDefinition]] = None, - tool_choice: Optional[ - Union[str, _models.ChatCompletionsToolChoicePreset, _models.ChatCompletionsNamedToolChoice] - ] = None, - seed: Optional[int] = None, - model: Optional[str] = None, - model_extras: Optional[Dict[str, Any]] = None, - **kwargs: Any, - ) -> None: - - self._model_info: Optional[_models.ModelInfo] = None - - # Store default chat completions settings, to be applied in all future service calls - # unless overridden by arguments in the `complete` method. - self._frequency_penalty = frequency_penalty - self._presence_penalty = presence_penalty - self._temperature = temperature - self._top_p = top_p - self._max_tokens = max_tokens - self._response_format = response_format - self._stop = stop - self._tools = tools - self._tool_choice = tool_choice - self._seed = seed - self._model = model - self._model_extras = model_extras - - # For Key auth, we need to send these two auth HTTP request headers simultaneously: - # 1. "Authorization: Bearer " - # 2. "api-key: " - # This is because Serverless API, Managed Compute and GitHub endpoints support the first header, - # and Azure OpenAI and the new Unified Inference endpoints support the second header. - # The first header will be taken care of by auto-generated code. - # The second one is added here. - if isinstance(credential, AzureKeyCredential): - headers = kwargs.pop("headers", {}) - if "api-key" not in headers: - headers["api-key"] = credential.key - kwargs["headers"] = headers - - super().__init__(endpoint, credential, **kwargs) - - @overload - async def complete( - self, - *, - messages: List[_models.ChatRequestMessage], - stream: Literal[False] = False, - frequency_penalty: Optional[float] = None, - presence_penalty: Optional[float] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - max_tokens: Optional[int] = None, - response_format: Optional[_models.ChatCompletionsResponseFormat] = None, - stop: Optional[List[str]] = None, - tools: Optional[List[_models.ChatCompletionsToolDefinition]] = None, - tool_choice: Optional[ - Union[str, _models.ChatCompletionsToolChoicePreset, _models.ChatCompletionsNamedToolChoice] - ] = None, - seed: Optional[int] = None, - model: Optional[str] = None, - model_extras: Optional[Dict[str, Any]] = None, - **kwargs: Any, - ) -> _models.ChatCompletions: ... - - @overload - async def complete( - self, - *, - messages: List[_models.ChatRequestMessage], - stream: Literal[True], - frequency_penalty: Optional[float] = None, - presence_penalty: Optional[float] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - max_tokens: Optional[int] = None, - response_format: Optional[_models.ChatCompletionsResponseFormat] = None, - stop: Optional[List[str]] = None, - tools: Optional[List[_models.ChatCompletionsToolDefinition]] = None, - tool_choice: Optional[ - Union[str, _models.ChatCompletionsToolChoicePreset, _models.ChatCompletionsNamedToolChoice] - ] = None, - seed: Optional[int] = None, - model: Optional[str] = None, - model_extras: Optional[Dict[str, Any]] = None, - **kwargs: Any, - ) -> AsyncIterable[_models.StreamingChatCompletionsUpdate]: ... - - @overload - async def complete( - self, - *, - messages: List[_models.ChatRequestMessage], - stream: Optional[bool] = None, - frequency_penalty: Optional[float] = None, - presence_penalty: Optional[float] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - max_tokens: Optional[int] = None, - response_format: Optional[_models.ChatCompletionsResponseFormat] = None, - stop: Optional[List[str]] = None, - tools: Optional[List[_models.ChatCompletionsToolDefinition]] = None, - tool_choice: Optional[ - Union[str, _models.ChatCompletionsToolChoicePreset, _models.ChatCompletionsNamedToolChoice] - ] = None, - seed: Optional[int] = None, - model: Optional[str] = None, - model_extras: Optional[Dict[str, Any]] = None, - **kwargs: Any, - ) -> Union[AsyncIterable[_models.StreamingChatCompletionsUpdate], _models.ChatCompletions]: - # pylint: disable=line-too-long - """Gets chat completions for the provided chat messages. - Completions support a wide variety of tasks and generate text that continues from or - "completes" provided prompt data. The method makes a REST API call to the `/chat/completions` route - on the given endpoint. - When using this method with `stream=True`, the response is streamed - back to the client. Iterate over the resulting StreamingChatCompletions - object to get content updates as they arrive. By default, the response is a ChatCompletions object - (non-streaming). - - :keyword messages: The collection of context messages associated with this chat completions - request. - Typical usage begins with a chat message for the System role that provides instructions for - the behavior of the assistant, followed by alternating messages between the User and - Assistant roles. Required. - :paramtype messages: list[~azure.ai.inference.models.ChatRequestMessage] - :keyword stream: A value indicating whether chat completions should be streamed for this request. - Default value is False. If streaming is enabled, the response will be a StreamingChatCompletions. - Otherwise the response will be a ChatCompletions. - :paramtype stream: bool - :keyword frequency_penalty: A value that influences the probability of generated tokens - appearing based on their cumulative frequency in generated text. - Positive values will make tokens less likely to appear as their frequency increases and - decrease the likelihood of the model repeating the same statements verbatim. - Supported range is [-2, 2]. - Default value is None. - :paramtype frequency_penalty: float - :keyword presence_penalty: A value that influences the probability of generated tokens - appearing based on their existing - presence in generated text. - Positive values will make tokens less likely to appear when they already exist and increase - the model's likelihood to output new topics. - Supported range is [-2, 2]. - Default value is None. - :paramtype presence_penalty: float - :keyword temperature: The sampling temperature to use that controls the apparent creativity of - generated completions. - Higher values will make output more random while lower values will make results more focused - and deterministic. - It is not recommended to modify temperature and top_p for the same completions request as the - interaction of these two settings is difficult to predict. - Supported range is [0, 1]. - Default value is None. - :paramtype temperature: float - :keyword top_p: An alternative to sampling with temperature called nucleus sampling. This value - causes the - model to consider the results of tokens with the provided probability mass. As an example, a - value of 0.15 will cause only the tokens comprising the top 15% of probability mass to be - considered. - It is not recommended to modify temperature and top_p for the same completions request as the - interaction of these two settings is difficult to predict. - Supported range is [0, 1]. - Default value is None. - :paramtype top_p: float - :keyword max_tokens: The maximum number of tokens to generate. Default value is None. - :paramtype max_tokens: int - :keyword response_format: The format that the model must output. Use this to enable JSON mode - instead of the default text mode. - Note that to enable JSON mode, some AI models may also require you to instruct the model to - produce JSON via a system or user message. Default value is None. - :paramtype response_format: ~azure.ai.inference.models.ChatCompletionsResponseFormat - :keyword stop: A collection of textual sequences that will end completions generation. Default - value is None. - :paramtype stop: list[str] - :keyword tools: The available tool definitions that the chat completions request can use, - including caller-defined functions. Default value is None. - :paramtype tools: list[~azure.ai.inference.models.ChatCompletionsToolDefinition] - :keyword tool_choice: If specified, the model will configure which of the provided tools it can - use for the chat completions response. Is either a Union[str, - "_models.ChatCompletionsToolChoicePreset"] type or a ChatCompletionsNamedToolChoice type. - Default value is None. - :paramtype tool_choice: str or ~azure.ai.inference.models.ChatCompletionsToolChoicePreset or - ~azure.ai.inference.models.ChatCompletionsNamedToolChoice - :keyword seed: If specified, the system will make a best effort to sample deterministically - such that repeated requests with the - same seed and parameters should return the same result. Determinism is not guaranteed. - Default value is None. - :paramtype seed: int - :keyword model: ID of the specific AI model to use, if more than one model is available on the - endpoint. Default value is None. - :paramtype model: str - :keyword model_extras: Additional, model-specific parameters that are not in the - standard request payload. They will be added as-is to the root of the JSON in the request body. - How the service handles these extra parameters depends on the value of the - ``extra-parameters`` request header. Default value is None. - :paramtype model_extras: dict[str, Any] - :return: ChatCompletions for non-streaming, or AsyncIterable[StreamingChatCompletionsUpdate] for streaming. - :rtype: ~azure.ai.inference.models.ChatCompletions or ~azure.ai.inference.models.AsyncStreamingChatCompletions - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def complete( - self, - body: JSON, - *, - content_type: str = "application/json", - **kwargs: Any, - ) -> Union[AsyncIterable[_models.StreamingChatCompletionsUpdate], _models.ChatCompletions]: - # pylint: disable=line-too-long - """Gets chat completions for the provided chat messages. - Completions support a wide variety of tasks and generate text that continues from or - "completes" provided prompt data. - - :param body: An object of type MutableMapping[str, Any], such as a dictionary, that - specifies the full request payload. 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: ChatCompletions for non-streaming, or AsyncIterable[StreamingChatCompletionsUpdate] for streaming. - :rtype: ~azure.ai.inference.models.ChatCompletions or ~azure.ai.inference.models.AsyncStreamingChatCompletions - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def complete( - self, - body: IO[bytes], - *, - content_type: str = "application/json", - **kwargs: Any, - ) -> Union[AsyncIterable[_models.StreamingChatCompletionsUpdate], _models.ChatCompletions]: - # pylint: disable=line-too-long - """Gets chat completions for the provided chat messages. - Completions support a wide variety of tasks and generate text that continues from or - "completes" provided prompt data. - - :param body: Specifies the full request payload. 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: ChatCompletions for non-streaming, or AsyncIterable[StreamingChatCompletionsUpdate] for streaming. - :rtype: ~azure.ai.inference.models.ChatCompletions or ~azure.ai.inference.models.AsyncStreamingChatCompletions - :raises ~azure.core.exceptions.HttpResponseError: - """ - - # pylint:disable=client-method-missing-tracing-decorator-async - async def complete( - self, - body: Union[JSON, IO[bytes]] = _Unset, - *, - messages: List[_models.ChatRequestMessage] = _Unset, - stream: Optional[bool] = None, - frequency_penalty: Optional[float] = None, - presence_penalty: Optional[float] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - max_tokens: Optional[int] = None, - response_format: Optional[_models.ChatCompletionsResponseFormat] = None, - stop: Optional[List[str]] = None, - tools: Optional[List[_models.ChatCompletionsToolDefinition]] = None, - tool_choice: Optional[ - Union[str, _models.ChatCompletionsToolChoicePreset, _models.ChatCompletionsNamedToolChoice] - ] = None, - seed: Optional[int] = None, - model: Optional[str] = None, - model_extras: Optional[Dict[str, Any]] = None, - **kwargs: Any, - ) -> Union[AsyncIterable[_models.StreamingChatCompletionsUpdate], _models.ChatCompletions]: - # pylint: disable=line-too-long - # pylint: disable=too-many-locals - """Gets chat completions for the provided chat messages. - Completions support a wide variety of tasks and generate text that continues from or - "completes" provided prompt data. When using this method with `stream=True`, the response is streamed - back to the client. Iterate over the resulting :class:`~azure.ai.inference.models.StreamingChatCompletions` - object to get content updates as they arrive. - - :param body: Is either a MutableMapping[str, Any] type (like a dictionary) or a IO[bytes] type - that specifies the full request payload. Required. - :type body: JSON or IO[bytes] - :keyword messages: The collection of context messages associated with this chat completions - request. - Typical usage begins with a chat message for the System role that provides instructions for - the behavior of the assistant, followed by alternating messages between the User and - Assistant roles. Required. - :paramtype messages: list[~azure.ai.inference.models.ChatRequestMessage] - :keyword stream: A value indicating whether chat completions should be streamed for this request. - Default value is False. If streaming is enabled, the response will be a StreamingChatCompletions. - Otherwise the response will be a ChatCompletions. - :paramtype stream: bool - :keyword frequency_penalty: A value that influences the probability of generated tokens - appearing based on their cumulative frequency in generated text. - Positive values will make tokens less likely to appear as their frequency increases and - decrease the likelihood of the model repeating the same statements verbatim. - Supported range is [-2, 2]. - Default value is None. - :paramtype frequency_penalty: float - :keyword presence_penalty: A value that influences the probability of generated tokens - appearing based on their existing - presence in generated text. - Positive values will make tokens less likely to appear when they already exist and increase - the model's likelihood to output new topics. - Supported range is [-2, 2]. - Default value is None. - :paramtype presence_penalty: float - :keyword temperature: The sampling temperature to use that controls the apparent creativity of - generated completions. - Higher values will make output more random while lower values will make results more focused - and deterministic. - It is not recommended to modify temperature and top_p for the same completions request as the - interaction of these two settings is difficult to predict. - Supported range is [0, 1]. - Default value is None. - :paramtype temperature: float - :keyword top_p: An alternative to sampling with temperature called nucleus sampling. This value - causes the - model to consider the results of tokens with the provided probability mass. As an example, a - value of 0.15 will cause only the tokens comprising the top 15% of probability mass to be - considered. - It is not recommended to modify temperature and top_p for the same completions request as the - interaction of these two settings is difficult to predict. - Supported range is [0, 1]. - Default value is None. - :paramtype top_p: float - :keyword max_tokens: The maximum number of tokens to generate. Default value is None. - :paramtype max_tokens: int - :keyword response_format: The format that the model must output. Use this to enable JSON mode - instead of the default text mode. - Note that to enable JSON mode, some AI models may also require you to instruct the model to - produce JSON via a system or user message. Default value is None. - :paramtype response_format: ~azure.ai.inference.models.ChatCompletionsResponseFormat - :keyword stop: A collection of textual sequences that will end completions generation. Default - value is None. - :paramtype stop: list[str] - :keyword tools: The available tool definitions that the chat completions request can use, - including caller-defined functions. Default value is None. - :paramtype tools: list[~azure.ai.inference.models.ChatCompletionsToolDefinition] - :keyword tool_choice: If specified, the model will configure which of the provided tools it can - use for the chat completions response. Is either a Union[str, - "_models.ChatCompletionsToolChoicePreset"] type or a ChatCompletionsNamedToolChoice type. - Default value is None. - :paramtype tool_choice: str or ~azure.ai.inference.models.ChatCompletionsToolChoicePreset or - ~azure.ai.inference.models.ChatCompletionsNamedToolChoice - :keyword seed: If specified, the system will make a best effort to sample deterministically - such that repeated requests with the - same seed and parameters should return the same result. Determinism is not guaranteed. - Default value is None. - :paramtype seed: int - :keyword model: ID of the specific AI model to use, if more than one model is available on the - endpoint. Default value is None. - :paramtype model: str - :keyword model_extras: Additional, model-specific parameters that are not in the - standard request payload. They will be added as-is to the root of the JSON in the request body. - How the service handles these extra parameters depends on the value of the - ``extra-parameters`` request header. Default value is None. - :paramtype model_extras: dict[str, Any] - :return: ChatCompletions for non-streaming, or AsyncIterable[StreamingChatCompletionsUpdate] for streaming. - :rtype: ~azure.ai.inference.models.ChatCompletions or ~azure.ai.inference.models.AsyncStreamingChatCompletions - :raises ~azure.core.exceptions.HttpResponseError: - """ - error_map = { - 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 {} - _extra_parameters: Union[_models._enums.ExtraParameters, None] = None - - content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) - - if body is _Unset: - if messages is _Unset: - raise TypeError("missing required argument: messages") - body = { - "messages": messages, - "stream": stream, - "frequency_penalty": frequency_penalty if frequency_penalty is not None else self._frequency_penalty, - "max_tokens": max_tokens if max_tokens is not None else self._max_tokens, - "model": model if model is not None else self._model, - "presence_penalty": presence_penalty if presence_penalty is not None else self._presence_penalty, - "response_format": response_format if response_format is not None else self._response_format, - "seed": seed if seed is not None else self._seed, - "stop": stop if stop is not None else self._stop, - "temperature": temperature if temperature is not None else self._temperature, - "tool_choice": tool_choice if tool_choice is not None else self._tool_choice, - "tools": tools if tools is not None else self._tools, - "top_p": top_p if top_p is not None else self._top_p, - } - if model_extras is not None and bool(model_extras): - body.update(model_extras) - _extra_parameters = _models._enums.ExtraParameters.PASS_THROUGH # pylint: disable=protected-access - elif self._model_extras is not None and bool(self._model_extras): - body.update(self._model_extras) - _extra_parameters = _models._enums.ExtraParameters.PASS_THROUGH # pylint: disable=protected-access - body = {k: v for k, v in body.items() if v is not None} - elif isinstance(body, dict) and "stream" in body and isinstance(body["stream"], bool): - stream = body["stream"] - 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_chat_completions_complete_request( - extra_params=_extra_parameters, - content_type=content_type, - api_version=self._config.api_version, - content=_content, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), - } - _request.url = self._client.format_url(_request.url, **path_format_arguments) - - _stream = stream or False - pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access - _request, stream=_stream, **kwargs - ) - - response = pipeline_response.http_response - - if response.status_code not in [200]: - if _stream: - await response.read() # Load the body in memory and close the socket - map_error(status_code=response.status_code, response=response, error_map=error_map) - raise HttpResponseError(response=response) - - if _stream: - return _models.AsyncStreamingChatCompletions(response) - - return _deserialize(_models._patch.ChatCompletions, response.json()) # pylint: disable=protected-access - - @distributed_trace_async - async def get_model_info(self, **kwargs: Any) -> _models.ModelInfo: - # pylint: disable=line-too-long - """Returns information about the AI model. - The method makes a REST API call to the ``/info`` route on the given endpoint. - This method will only work when using Serverless API or Managed Compute endpoint. - It will not work for GitHub Models endpoint or Azure OpenAI endpoint. - - :return: ModelInfo. The ModelInfo is compatible with MutableMapping - :rtype: ~azure.ai.inference.models.ModelInfo - :raises ~azure.core.exceptions.HttpResponseError: - """ - if not self._model_info: - self._model_info = await self._get_model_info(**kwargs) # pylint: disable=attribute-defined-outside-init - return self._model_info - - def __str__(self) -> str: - # pylint: disable=client-method-name-no-double-underscore - return super().__str__() + f"\n{self._model_info}" if self._model_info else super().__str__() - - -class EmbeddingsClient(EmbeddingsClientGenerated): - """EmbeddingsClient. - - :param endpoint: Service host. Required. - :type endpoint: str - :param credential: Credential used to authenticate requests to the service. Is either a - AzureKeyCredential type or a AsyncTokenCredential type. Required. - :type credential: ~azure.core.credentials.AzureKeyCredential or - ~azure.core.credentials_async.AsyncTokenCredential - :keyword dimensions: Optional. The number of dimensions the resulting output embeddings should - have. Default value is None. - :paramtype dimensions: int - :keyword encoding_format: Optional. The desired format for the returned embeddings. - Known values are: - "base64", "binary", "float", "int8", "ubinary", and "uint8". Default value is None. - :paramtype encoding_format: str or ~azure.ai.inference.models.EmbeddingEncodingFormat - :keyword input_type: Optional. The type of the input. Known values are: - "text", "query", and "document". Default value is None. - :paramtype input_type: str or ~azure.ai.inference.models.EmbeddingInputType - :keyword model: ID of the specific AI model to use, if more than one model is available on the - endpoint. Default value is None. - :paramtype model: str - :keyword model_extras: Additional, model-specific parameters that are not in the - standard request payload. They will be added as-is to the root of the JSON in the request body. - How the service handles these extra parameters depends on the value of the - ``extra-parameters`` request header. Default value is None. - :paramtype model_extras: dict[str, Any] - :keyword api_version: The API version to use for this operation. Default value is - "2024-05-01-preview". Note that overriding this default value may result in unsupported - behavior. - :paramtype api_version: str - """ - - def __init__( - self, - endpoint: str, - credential: Union[AzureKeyCredential, "AsyncTokenCredential"], - *, - dimensions: Optional[int] = None, - encoding_format: Optional[Union[str, _models.EmbeddingEncodingFormat]] = None, - input_type: Optional[Union[str, _models.EmbeddingInputType]] = None, - model: Optional[str] = None, - model_extras: Optional[Dict[str, Any]] = None, - **kwargs: Any, - ) -> None: - - self._model_info: Optional[_models.ModelInfo] = None - - # Store default embeddings settings, to be applied in all future service calls - # unless overridden by arguments in the `embed` method. - self._dimensions = dimensions - self._encoding_format = encoding_format - self._input_type = input_type - self._model = model - self._model_extras = model_extras - - # For Key auth, we need to send these two auth HTTP request headers simultaneously: - # 1. "Authorization: Bearer " - # 2. "api-key: " - # This is because Serverless API, Managed Compute and GitHub endpoints support the first header, - # and Azure OpenAI and the new Unified Inference endpoints support the second header. - # The first header will be taken care of by auto-generated code. - # The second one is added here. - if isinstance(credential, AzureKeyCredential): - headers = kwargs.pop("headers", {}) - if "api-key" not in headers: - headers["api-key"] = credential.key - kwargs["headers"] = headers - - super().__init__(endpoint, credential, **kwargs) - - @overload - async def embed( - self, - *, - input: List[str], - dimensions: Optional[int] = None, - encoding_format: Optional[Union[str, _models.EmbeddingEncodingFormat]] = None, - input_type: Optional[Union[str, _models.EmbeddingInputType]] = None, - model: Optional[str] = None, - model_extras: Optional[Dict[str, Any]] = None, - **kwargs: Any, - ) -> _models.EmbeddingsResult: - """Return the embedding vectors for given text prompts. - The method makes a REST API call to the `/embeddings` route on the given endpoint. - - :keyword input: Input text to embed, encoded as a string or array of tokens. - To embed multiple inputs in a single request, pass an array - of strings or array of token arrays. Required. - :paramtype input: list[str] - :keyword dimensions: Optional. The number of dimensions the resulting output embeddings should - have. Default value is None. - :paramtype dimensions: int - :keyword encoding_format: Optional. The desired format for the returned embeddings. - Known values are: - "base64", "binary", "float", "int8", "ubinary", and "uint8". Default value is None. - :paramtype encoding_format: str or ~azure.ai.inference.models.EmbeddingEncodingFormat - :keyword input_type: Optional. The type of the input. Known values are: - "text", "query", and "document". Default value is None. - :paramtype input_type: str or ~azure.ai.inference.models.EmbeddingInputType - :keyword model: ID of the specific AI model to use, if more than one model is available on the - endpoint. Default value is None. - :paramtype model: str - :keyword model_extras: Additional, model-specific parameters that are not in the - standard request payload. They will be added as-is to the root of the JSON in the request body. - How the service handles these extra parameters depends on the value of the - ``extra-parameters`` request header. Default value is None. - :paramtype model_extras: dict[str, Any] - :return: EmbeddingsResult. The EmbeddingsResult is compatible with MutableMapping - :rtype: ~azure.ai.inference.models.EmbeddingsResult - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def embed( - self, - body: JSON, - *, - content_type: str = "application/json", - **kwargs: Any, - ) -> _models.EmbeddingsResult: - """Return the embedding vectors for given text prompts. - The method makes a REST API call to the `/embeddings` route on the given endpoint. - - :param body: An object of type MutableMapping[str, Any], such as a dictionary, that - specifies the full request payload. 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: EmbeddingsResult. The EmbeddingsResult is compatible with MutableMapping - :rtype: ~azure.ai.inference.models.EmbeddingsResult - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def embed( - self, - body: IO[bytes], - *, - content_type: str = "application/json", - **kwargs: Any, - ) -> _models.EmbeddingsResult: - """Return the embedding vectors for given text prompts. - The method makes a REST API call to the `/embeddings` route on the given endpoint. - - :param body: Specifies the full request payload. 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: EmbeddingsResult. The EmbeddingsResult is compatible with MutableMapping - :rtype: ~azure.ai.inference.models.EmbeddingsResult - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace_async - async def embed( - self, - body: Union[JSON, IO[bytes]] = _Unset, - *, - input: List[str] = _Unset, - dimensions: Optional[int] = None, - encoding_format: Optional[Union[str, _models.EmbeddingEncodingFormat]] = None, - input_type: Optional[Union[str, _models.EmbeddingInputType]] = None, - model: Optional[str] = None, - model_extras: Optional[Dict[str, Any]] = None, - **kwargs: Any, - ) -> _models.EmbeddingsResult: - # pylint: disable=line-too-long - """Return the embedding vectors for given text prompts. - The method makes a REST API call to the `/embeddings` route on the given endpoint. - - :param body: Is either a MutableMapping[str, Any] type (like a dictionary) or a IO[bytes] type - that specifies the full request payload. Required. - :type body: JSON or IO[bytes] - :keyword input: Input text to embed, encoded as a string or array of tokens. - To embed multiple inputs in a single request, pass an array - of strings or array of token arrays. Required. - :paramtype input: list[str] - :keyword dimensions: Optional. The number of dimensions the resulting output embeddings should - have. Default value is None. - :paramtype dimensions: int - :keyword encoding_format: Optional. The desired format for the returned embeddings. - Known values are: - "base64", "binary", "float", "int8", "ubinary", and "uint8". Default value is None. - :paramtype encoding_format: str or ~azure.ai.inference.models.EmbeddingEncodingFormat - :keyword input_type: Optional. The type of the input. Known values are: - "text", "query", and "document". Default value is None. - :paramtype input_type: str or ~azure.ai.inference.models.EmbeddingInputType - :keyword model: ID of the specific AI model to use, if more than one model is available on the - endpoint. Default value is None. - :paramtype model: str - :keyword model_extras: Additional, model-specific parameters that are not in the - standard request payload. They will be added as-is to the root of the JSON in the request body. - How the service handles these extra parameters depends on the value of the - ``extra-parameters`` request header. Default value is None. - :paramtype model_extras: dict[str, Any] - :return: EmbeddingsResult. The EmbeddingsResult is compatible with MutableMapping - :rtype: ~azure.ai.inference.models.EmbeddingsResult - :raises ~azure.core.exceptions.HttpResponseError: - """ - error_map: MutableMapping[int, Type[HttpResponseError]] = { - 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 {} - _extra_parameters: Union[_models._enums.ExtraParameters, None] = None - - content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) - - if body is _Unset: - if input is _Unset: - raise TypeError("missing required argument: input") - body = { - "input": input, - "dimensions": dimensions if dimensions is not None else self._dimensions, - "encoding_format": encoding_format if encoding_format is not None else self._encoding_format, - "input_type": input_type if input_type is not None else self._input_type, - "model": model if model is not None else self._model, - } - if model_extras is not None and bool(model_extras): - body.update(model_extras) - _extra_parameters = _models._enums.ExtraParameters.PASS_THROUGH # pylint: disable=protected-access - elif self._model_extras is not None and bool(self._model_extras): - body.update(self._model_extras) - _extra_parameters = _models._enums.ExtraParameters.PASS_THROUGH # pylint: disable=protected-access - 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_embeddings_embed_request( - extra_params=_extra_parameters, - content_type=content_type, - api_version=self._config.api_version, - content=_content, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), - } - _request.url = self._client.format_url(_request.url, **path_format_arguments) - - _stream = kwargs.pop("stream", False) - pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access - _request, stream=_stream, **kwargs - ) - - response = pipeline_response.http_response - - if response.status_code not in [200]: - if _stream: - await response.read() # Load the body in memory and close the socket - 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._patch.EmbeddingsResult, response.json() # pylint: disable=protected-access - ) - - return deserialized # type: ignore - - @distributed_trace_async - async def get_model_info(self, **kwargs: Any) -> _models.ModelInfo: - # pylint: disable=line-too-long - """Returns information about the AI model. - The method makes a REST API call to the ``/info`` route on the given endpoint. - This method will only work when using Serverless API or Managed Compute endpoint. - It will not work for GitHub Models endpoint or Azure OpenAI endpoint. - - :return: ModelInfo. The ModelInfo is compatible with MutableMapping - :rtype: ~azure.ai.inference.models.ModelInfo - :raises ~azure.core.exceptions.HttpResponseError: - """ - if not self._model_info: - self._model_info = await self._get_model_info(**kwargs) # pylint: disable=attribute-defined-outside-init - return self._model_info - - def __str__(self) -> str: - # pylint: disable=client-method-name-no-double-underscore - return super().__str__() + f"\n{self._model_info}" if self._model_info else super().__str__() - - -class ImageEmbeddingsClient(ImageEmbeddingsClientGenerated): - """ImageEmbeddingsClient. - - :param endpoint: Service host. Required. - :type endpoint: str - :param credential: Credential used to authenticate requests to the service. Is either a - AzureKeyCredential type or a AsyncTokenCredential type. Required. - :type credential: ~azure.core.credentials.AzureKeyCredential or - ~azure.core.credentials_async.AsyncTokenCredential - :keyword dimensions: Optional. The number of dimensions the resulting output embeddings should - have. Default value is None. - :paramtype dimensions: int - :keyword encoding_format: Optional. The desired format for the returned embeddings. - Known values are: - "base64", "binary", "float", "int8", "ubinary", and "uint8". Default value is None. - :paramtype encoding_format: str or ~azure.ai.inference.models.EmbeddingEncodingFormat - :keyword input_type: Optional. The type of the input. Known values are: - "text", "query", and "document". Default value is None. - :paramtype input_type: str or ~azure.ai.inference.models.EmbeddingInputType - :keyword model: ID of the specific AI model to use, if more than one model is available on the - endpoint. Default value is None. - :paramtype model: str - :keyword model_extras: Additional, model-specific parameters that are not in the - standard request payload. They will be added as-is to the root of the JSON in the request body. - How the service handles these extra parameters depends on the value of the - ``extra-parameters`` request header. Default value is None. - :paramtype model_extras: dict[str, Any] - :keyword api_version: The API version to use for this operation. Default value is - "2024-05-01-preview". Note that overriding this default value may result in unsupported - behavior. - :paramtype api_version: str - """ - - def __init__( - self, - endpoint: str, - credential: Union[AzureKeyCredential, "AsyncTokenCredential"], - *, - dimensions: Optional[int] = None, - encoding_format: Optional[Union[str, _models.EmbeddingEncodingFormat]] = None, - input_type: Optional[Union[str, _models.EmbeddingInputType]] = None, - model: Optional[str] = None, - model_extras: Optional[Dict[str, Any]] = None, - **kwargs: Any, - ) -> None: - - self._model_info: Optional[_models.ModelInfo] = None - - # Store default embeddings settings, to be applied in all future service calls - # unless overridden by arguments in the `embed` method. - self._dimensions = dimensions - self._encoding_format = encoding_format - self._input_type = input_type - self._model = model - self._model_extras = model_extras - - # For Key auth, we need to send these two auth HTTP request headers simultaneously: - # 1. "Authorization: Bearer " - # 2. "api-key: " - # This is because Serverless API, Managed Compute and GitHub endpoints support the first header, - # and Azure OpenAI and the new Unified Inference endpoints support the second header. - # The first header will be taken care of by auto-generated code. - # The second one is added here. - if isinstance(credential, AzureKeyCredential): - headers = kwargs.pop("headers", {}) - if "api-key" not in headers: - headers["api-key"] = credential.key - kwargs["headers"] = headers - - super().__init__(endpoint, credential, **kwargs) - - @overload - async def embed( - self, - *, - input: List[_models.EmbeddingInput], - dimensions: Optional[int] = None, - encoding_format: Optional[Union[str, _models.EmbeddingEncodingFormat]] = None, - input_type: Optional[Union[str, _models.EmbeddingInputType]] = None, - model: Optional[str] = None, - model_extras: Optional[Dict[str, Any]] = None, - **kwargs: Any, - ) -> _models.EmbeddingsResult: - """Return the embedding vectors for given images. - The method makes a REST API call to the `/images/embeddings` route on the given endpoint. - - :keyword input: Input image to embed. To embed multiple inputs in a single request, pass an - array. - The input must not exceed the max input tokens for the model. Required. - :paramtype input: list[~azure.ai.inference.models.EmbeddingInput] - :keyword dimensions: Optional. The number of dimensions the resulting output embeddings should - have. Default value is None. - :paramtype dimensions: int - :keyword encoding_format: Optional. The desired format for the returned embeddings. - Known values are: - "base64", "binary", "float", "int8", "ubinary", and "uint8". Default value is None. - :paramtype encoding_format: str or ~azure.ai.inference.models.EmbeddingEncodingFormat - :keyword input_type: Optional. Known values are: - "text", "query", and "document". Default value is None. - :paramtype input_type: str or ~azure.ai.inference.models.EmbeddingInputType - :keyword model: ID of the specific AI model to use, if more than one model is available on the - endpoint. Default value is None. - :paramtype model: str - :keyword model_extras: Additional, model-specific parameters that are not in the - standard request payload. They will be added as-is to the root of the JSON in the request body. - How the service handles these extra parameters depends on the value of the - ``extra-parameters`` request header. Default value is None. - :paramtype model_extras: dict[str, Any] - :return: EmbeddingsResult. The EmbeddingsResult is compatible with MutableMapping - :rtype: ~azure.ai.inference.models.EmbeddingsResult - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def embed( - self, - body: JSON, - *, - content_type: str = "application/json", - **kwargs: Any, - ) -> _models.EmbeddingsResult: - """Return the embedding vectors for given images. - The method makes a REST API call to the `/images/embeddings` route on the given endpoint. - - :param body: An object of type MutableMapping[str, Any], such as a dictionary, that - specifies the full request payload. 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: EmbeddingsResult. The EmbeddingsResult is compatible with MutableMapping - :rtype: ~azure.ai.inference.models.EmbeddingsResult - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @overload - async def embed( - self, - body: IO[bytes], - *, - content_type: str = "application/json", - **kwargs: Any, - ) -> _models.EmbeddingsResult: - """Return the embedding vectors for given images. - The method makes a REST API call to the `/images/embeddings` route on the given endpoint. - - :param body: Specifies the full request payload. 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: EmbeddingsResult. The EmbeddingsResult is compatible with MutableMapping - :rtype: ~azure.ai.inference.models.EmbeddingsResult - :raises ~azure.core.exceptions.HttpResponseError: - """ - - @distributed_trace_async - async def embed( - self, - body: Union[JSON, IO[bytes]] = _Unset, - *, - input: List[_models.EmbeddingInput] = _Unset, - dimensions: Optional[int] = None, - encoding_format: Optional[Union[str, _models.EmbeddingEncodingFormat]] = None, - input_type: Optional[Union[str, _models.EmbeddingInputType]] = None, - model: Optional[str] = None, - model_extras: Optional[Dict[str, Any]] = None, - **kwargs: Any, - ) -> _models.EmbeddingsResult: - # pylint: disable=line-too-long - """Return the embedding vectors for given images. - The method makes a REST API call to the `/images/embeddings` route on the given endpoint. - - :param body: Is either a MutableMapping[str, Any] type (like a dictionary) or a IO[bytes] type - that specifies the full request payload. Required. - :type body: JSON or IO[bytes] - :keyword input: Input image to embed. To embed multiple inputs in a single request, pass an - array. - The input must not exceed the max input tokens for the model. Required. - :paramtype input: list[~azure.ai.inference.models.EmbeddingInput] - :keyword dimensions: Optional. The number of dimensions the resulting output embeddings should - have. Default value is None. - :paramtype dimensions: int - :keyword encoding_format: Optional. The desired format for the returned embeddings. - Known values are: - "base64", "binary", "float", "int8", "ubinary", and "uint8". Default value is None. - :paramtype encoding_format: str or ~azure.ai.inference.models.EmbeddingEncodingFormat - :keyword input_type: Optional. The type of the input. Known values are: - "text", "query", and "document". Default value is None. - :paramtype input_type: str or ~azure.ai.inference.models.EmbeddingInputType - :keyword model: ID of the specific AI model to use, if more than one model is available on the - endpoint. Default value is None. - :paramtype model: str - :keyword model_extras: Additional, model-specific parameters that are not in the - standard request payload. They will be added as-is to the root of the JSON in the request body. - How the service handles these extra parameters depends on the value of the - ``extra-parameters`` request header. Default value is None. - :paramtype model_extras: dict[str, Any] - :return: EmbeddingsResult. The EmbeddingsResult is compatible with MutableMapping - :rtype: ~azure.ai.inference.models.EmbeddingsResult - :raises ~azure.core.exceptions.HttpResponseError: - """ - error_map: MutableMapping[int, Type[HttpResponseError]] = { - 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 {} - _extra_parameters: Union[_models._enums.ExtraParameters, None] = None - - content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) - - if body is _Unset: - if input is _Unset: - raise TypeError("missing required argument: input") - body = { - "input": input, - "dimensions": dimensions if dimensions is not None else self._dimensions, - "encoding_format": encoding_format if encoding_format is not None else self._encoding_format, - "input_type": input_type if input_type is not None else self._input_type, - "model": model if model is not None else self._model, - } - if model_extras is not None and bool(model_extras): - body.update(model_extras) - _extra_parameters = _models._enums.ExtraParameters.PASS_THROUGH # pylint: disable=protected-access - elif self._model_extras is not None and bool(self._model_extras): - body.update(self._model_extras) - _extra_parameters = _models._enums.ExtraParameters.PASS_THROUGH # pylint: disable=protected-access - 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_image_embeddings_embed_request( - extra_params=_extra_parameters, - content_type=content_type, - api_version=self._config.api_version, - content=_content, - headers=_headers, - params=_params, - ) - path_format_arguments = { - "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), - } - _request.url = self._client.format_url(_request.url, **path_format_arguments) - - _stream = kwargs.pop("stream", False) - pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access - _request, stream=_stream, **kwargs - ) - - response = pipeline_response.http_response - - if response.status_code not in [200]: - if _stream: - await response.read() # Load the body in memory and close the socket - 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._patch.EmbeddingsResult, response.json() # pylint: disable=protected-access - ) - - return deserialized # type: ignore - - @distributed_trace_async - async def get_model_info(self, **kwargs: Any) -> _models.ModelInfo: - # pylint: disable=line-too-long - """Returns information about the AI model. - The method makes a REST API call to the ``/info`` route on the given endpoint. - This method will only work when using Serverless API or Managed Compute endpoint. - It will not work for GitHub Models endpoint or Azure OpenAI endpoint. - - :return: ModelInfo. The ModelInfo is compatible with MutableMapping - :rtype: ~azure.ai.inference.models.ModelInfo - :raises ~azure.core.exceptions.HttpResponseError: - """ - if not self._model_info: - self._model_info = await self._get_model_info(**kwargs) # pylint: disable=attribute-defined-outside-init - return self._model_info - - def __str__(self) -> str: - # pylint: disable=client-method-name-no-double-underscore - return super().__str__() + f"\n{self._model_info}" if self._model_info else super().__str__() - - -__all__: List[str] = [ - "load_client", - "ChatCompletionsClient", - "EmbeddingsClient", - "ImageEmbeddingsClient", -] # 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-inference/azure/ai/inference/aio/_vendor.py b/sdk/ai/azure-ai-inference/azure/ai/inference/aio/_vendor.py index dd91e1ea130f..b430582ca1fc 100644 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/aio/_vendor.py +++ b/sdk/ai/azure-ai-inference/azure/ai/inference/aio/_vendor.py @@ -15,7 +15,6 @@ ) if TYPE_CHECKING: - # pylint: disable=unused-import,ungrouped-imports from azure.core import AsyncPipelineClient from .._serialization import Deserializer, Serializer diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/models/__init__.py b/sdk/ai/azure-ai-inference/azure/ai/inference/models/__init__.py index 1832edc83399..c13771ddd1b3 100644 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/models/__init__.py +++ b/sdk/ai/azure-ai-inference/azure/ai/inference/models/__init__.py @@ -5,61 +5,73 @@ # Code generated by Microsoft (R) Python Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- +# pylint: disable=wrong-import-position -from ._models import AssistantMessage -from ._models import ChatChoice -from ._patch import ChatCompletions -from ._models import ChatCompletionsNamedToolChoice -from ._models import ChatCompletionsNamedToolChoiceFunction -from ._models import ChatCompletionsResponseFormat -from ._models import ChatCompletionsResponseFormatJSON -from ._models import ChatCompletionsResponseFormatText -from ._models import ChatCompletionsToolCall -from ._models import ChatCompletionsToolDefinition -from ._models import ChatRequestMessage -from ._models import ChatResponseMessage -from ._models import CompletionsUsage -from ._models import ContentItem -from ._models import EmbeddingInput -from ._models import EmbeddingItem -from ._patch import EmbeddingsResult -from ._models import EmbeddingsUsage -from ._models import FunctionCall -from ._models import FunctionDefinition -from ._models import ImageContentItem -from ._patch import ImageUrl -from ._models import ModelInfo -from ._models import StreamingChatChoiceUpdate -from ._models import StreamingChatCompletionsUpdate -from ._models import StreamingChatResponseMessageUpdate -from ._models import StreamingChatResponseToolCallUpdate -from ._models import SystemMessage -from ._models import TextContentItem -from ._models import ToolMessage -from ._models import UserMessage +from typing import TYPE_CHECKING -from ._enums import ChatCompletionsToolChoicePreset -from ._enums import ChatRole -from ._enums import CompletionsFinishReason -from ._enums import EmbeddingEncodingFormat -from ._enums import EmbeddingInputType -from ._enums import ImageDetailLevel -from ._enums import ModelType +if TYPE_CHECKING: + from ._patch import * # pylint: disable=unused-wildcard-import -from ._patch import StreamingChatCompletions -from ._patch import AsyncStreamingChatCompletions + +from ._models import ( # type: ignore + AssistantMessage, + ChatChoice, + ChatCompletions, + ChatCompletionsNamedToolChoice, + ChatCompletionsNamedToolChoiceFunction, + ChatCompletionsResponseFormat, + ChatCompletionsResponseFormatJsonObject, + ChatCompletionsResponseFormatJsonSchema, + ChatCompletionsResponseFormatJsonSchemaJsonSchema, + ChatCompletionsResponseFormatText, + ChatCompletionsToolCall, + ChatCompletionsToolDefinition, + ChatRequestMessage, + ChatResponseMessage, + CompletionsUsage, + ContentItem, + EmbeddingItem, + EmbeddingsResult, + EmbeddingsUsage, + FunctionCall, + FunctionDefinition, + ImageContentItem, + ImageEmbeddingInput, + ImageUrl, + ModelInfo, + StreamingChatChoiceUpdate, + StreamingChatCompletionsUpdate, + StreamingChatResponseMessageUpdate, + StreamingChatResponseToolCallUpdate, + SystemMessage, + TextContentItem, + ToolMessage, + UserMessage, +) + +from ._enums import ( # type: ignore + ChatCompletionsToolChoicePreset, + ChatRole, + CompletionsFinishReason, + EmbeddingEncodingFormat, + EmbeddingInputType, + ImageDetailLevel, + ModelType, +) +from ._patch import __all__ as _patch_all +from ._patch import * from ._patch import patch_sdk as _patch_sdk __all__ = [ - "StreamingChatCompletions", - "AsyncStreamingChatCompletions", "AssistantMessage", "ChatChoice", "ChatCompletions", "ChatCompletionsNamedToolChoice", "ChatCompletionsNamedToolChoiceFunction", "ChatCompletionsResponseFormat", - "ChatCompletionsResponseFormatJSON", + "ChatCompletionsResponseFormatJsonObject", + "ChatCompletionsResponseFormatJsonSchema", + "ChatCompletionsResponseFormatJsonSchemaJsonSchema", "ChatCompletionsResponseFormatText", "ChatCompletionsToolCall", "ChatCompletionsToolDefinition", @@ -67,13 +79,13 @@ "ChatResponseMessage", "CompletionsUsage", "ContentItem", - "EmbeddingInput", "EmbeddingItem", "EmbeddingsResult", "EmbeddingsUsage", "FunctionCall", "FunctionDefinition", "ImageContentItem", + "ImageEmbeddingInput", "ImageUrl", "ModelInfo", "StreamingChatChoiceUpdate", @@ -92,5 +104,5 @@ "ImageDetailLevel", "ModelType", ] - +__all__.extend([p for p in _patch_all if p not in __all__]) # pyright: ignore _patch_sdk() diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/models/_enums.py b/sdk/ai/azure-ai-inference/azure/ai/inference/models/_enums.py index 830a93f75472..61443cbfbb85 100644 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/models/_enums.py +++ b/sdk/ai/azure-ai-inference/azure/ai/inference/models/_enums.py @@ -121,14 +121,14 @@ class ModelType(str, Enum, metaclass=CaseInsensitiveEnumMeta): """The type of AI model.""" EMBEDDINGS = "embeddings" - """Embeddings.""" + """A model capable of generating embeddings from a text""" IMAGE_GENERATION = "image_generation" - """Image generation""" + """A model capable of generating images from an image and text description""" TEXT_GENERATION = "text_generation" - """Text generation""" + """A text generation model""" IMAGE_EMBEDDINGS = "image_embeddings" - """Image embeddings""" + """A model capable of generating embeddings from an image""" AUDIO_GENERATION = "audio_generation" - """Audio generation""" - CHAT = "chat" - """Chat completions""" + """A text-to-audio generative model""" + CHAT_COMPLETION = "chat_completion" + """A model capable of taking chat-formatted messages and generate responses""" diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/models/_models.py b/sdk/ai/azure-ai-inference/azure/ai/inference/models/_models.py index 4ac8f16f94d1..8bd60981bb26 100644 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/models/_models.py +++ b/sdk/ai/azure-ai-inference/azure/ai/inference/models/_models.py @@ -1,11 +1,12 @@ -# coding=utf-8 # pylint: disable=too-many-lines +# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) Python Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- +# pylint: disable=useless-super-delegation import datetime from typing import Any, Dict, List, Literal, Mapping, Optional, TYPE_CHECKING, Union, overload @@ -15,7 +16,6 @@ from ._enums import ChatRole if TYPE_CHECKING: - # pylint: disable=unused-import,ungrouped-imports from .. import models as _models @@ -42,16 +42,16 @@ def __init__( self, *, role: str, - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) @@ -88,16 +88,16 @@ def __init__( *, content: Optional[str] = None, tool_calls: Optional[List["_models.ChatCompletionsToolCall"]] = None, - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, role=ChatRole.ASSISTANT, **kwargs) @@ -132,16 +132,16 @@ def __init__( index: int, finish_reason: Union[str, "_models.CompletionsFinishReason"], message: "_models.ChatResponseMessage", - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) @@ -193,16 +193,16 @@ def __init__( model: str, usage: "_models.CompletionsUsage", choices: List["_models.ChatChoice"], - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) @@ -232,10 +232,10 @@ def __init__( self, *, function: "_models.ChatCompletionsNamedToolChoiceFunction", - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] @@ -264,16 +264,16 @@ def __init__( self, *, name: str, - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) @@ -285,7 +285,8 @@ class ChatCompletionsResponseFormat(_model_base.Model): via a system or user message. You probably want to use the sub-classes and not this class directly. Known sub-classes are: - ChatCompletionsResponseFormatJSON, ChatCompletionsResponseFormatText + ChatCompletionsResponseFormatJsonObject, ChatCompletionsResponseFormatJsonSchema, + ChatCompletionsResponseFormatText All required parameters must be populated in order to send to server. @@ -303,20 +304,20 @@ def __init__( self, *, type: str, - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) -class ChatCompletionsResponseFormatJSON(ChatCompletionsResponseFormat, discriminator="json_object"): +class ChatCompletionsResponseFormatJsonObject(ChatCompletionsResponseFormat, discriminator="json_object"): """A response format for Chat Completions that restricts responses to emitting valid JSON objects. Note that to enable JSON mode, some AI models may also require you to instruct the model to produce JSON @@ -336,19 +337,108 @@ class ChatCompletionsResponseFormatJSON(ChatCompletionsResponseFormat, discrimin @overload def __init__( self, - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, type="json_object", **kwargs) +class ChatCompletionsResponseFormatJsonSchema(ChatCompletionsResponseFormat, discriminator="json_schema"): + """ChatCompletionsResponseFormatJsonSchema. + + All required parameters must be populated in order to send to server. + + :ivar type: The type of response format being defined: ``json_schema``. Required. Default value + is "json_schema". + :vartype type: str + :ivar json_schema: Required. + :vartype json_schema: + ~azure.ai.inference.models.ChatCompletionsResponseFormatJsonSchemaJsonSchema + """ + + type: Literal["json_schema"] = rest_discriminator(name="type") # type: ignore + """The type of response format being defined: ``json_schema``. Required. Default value is + \"json_schema\".""" + json_schema: "_models.ChatCompletionsResponseFormatJsonSchemaJsonSchema" = rest_field() + """Required.""" + + @overload + def __init__( + self, + *, + json_schema: "_models.ChatCompletionsResponseFormatJsonSchemaJsonSchema", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :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="json_schema", **kwargs) + + +class ChatCompletionsResponseFormatJsonSchemaJsonSchema(_model_base.Model): # pylint: disable=name-too-long + """ChatCompletionsResponseFormatJsonSchemaJsonSchema. + + All required parameters must be populated in order to send to server. + + :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 the response format. Must be a-z, A-Z, 0-9, or contain underscores and + dashes, with a maximum length of 64. Required. + :vartype name: str + :ivar schema: + :vartype schema: dict[str, any] + :ivar strict: Whether to enable strict schema adherence when generating the output. If set to + true, the model will always follow the exact schema defined in the ``schema`` field. Only a + subset of JSON Schema is supported when ``strict`` is ``true``. + :vartype strict: bool + """ + + 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 the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with + a maximum length of 64. Required.""" + schema: Optional[Dict[str, Any]] = rest_field() + strict: Optional[bool] = rest_field() + """Whether to enable strict schema adherence when generating the output. If set to true, the model + will always follow the exact schema defined in the ``schema`` field. Only a subset of JSON + Schema is supported when ``strict`` is ``true``.""" + + @overload + def __init__( + self, + *, + name: str, + description: Optional[str] = None, + schema: Optional[Dict[str, Any]] = None, + strict: Optional[bool] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + class ChatCompletionsResponseFormatText(ChatCompletionsResponseFormat, discriminator="text"): """A response format for Chat Completions that emits text responses. This is the default response format. @@ -366,16 +456,16 @@ class ChatCompletionsResponseFormatText(ChatCompletionsResponseFormat, discrimin @overload def __init__( self, - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, type="text", **kwargs) @@ -408,10 +498,10 @@ def __init__( *, id: str, # pylint: disable=redefined-builtin function: "_models.FunctionCall", - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] @@ -447,10 +537,10 @@ def __init__( self, *, function: "_models.FunctionDefinition", - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] @@ -493,16 +583,16 @@ def __init__( role: Union[str, "_models.ChatRole"], content: str, tool_calls: Optional[List["_models.ChatCompletionsToolCall"]] = None, - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) @@ -537,16 +627,16 @@ def __init__( completion_tokens: int, prompt_tokens: int, total_tokens: int, - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) @@ -571,53 +661,16 @@ def __init__( self, *, type: str, - ): ... - - @overload - def __init__(self, mapping: Mapping[str, Any]): - """ - :param mapping: raw JSON to initialize the model. - :type mapping: Mapping[str, Any] - """ - - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation - super().__init__(*args, **kwargs) - - -class EmbeddingInput(_model_base.Model): - """Represents an image with optional text. - - All required parameters must be populated in order to send to server. - - :ivar image: The input image, in PNG format. Required. - :vartype image: str - :ivar text: Optional. The text input to feed into the model (like DINO, CLIP). - Returns a 422 error if the model doesn't support the value or parameter. - :vartype text: str - """ - - image: str = rest_field() - """The input image, in PNG format. Required.""" - text: Optional[str] = rest_field() - """Optional. The text input to feed into the model (like DINO, CLIP). - Returns a 422 error if the model doesn't support the value or parameter.""" + ) -> None: ... @overload - def __init__( - self, - *, - image: str, - text: Optional[str] = None, - ): ... - - @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) @@ -634,7 +687,7 @@ class EmbeddingItem(_model_base.Model): :vartype index: int """ - embedding: Union["str", List[float]] = rest_field() + embedding: Union[str, List[float]] = rest_field() """List of embedding values for the input prompt. These represent a measurement of the vector-based relatedness of the provided input. Or a base64 encoded string of the embedding vector. Required. Is either a str type or a [float] type.""" @@ -647,16 +700,16 @@ def __init__( *, embedding: Union[str, List[float]], index: int, - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) @@ -694,16 +747,16 @@ def __init__( data: List["_models.EmbeddingItem"], usage: "_models.EmbeddingsUsage", model: str, - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) @@ -731,16 +784,16 @@ def __init__( *, prompt_tokens: int, total_tokens: int, - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) @@ -772,16 +825,16 @@ def __init__( *, name: str, arguments: str, - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) @@ -817,16 +870,16 @@ def __init__( name: str, description: Optional[str] = None, parameters: Optional[Any] = None, - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) @@ -855,19 +908,58 @@ def __init__( self, *, image_url: "_models.ImageUrl", - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, type="image_url", **kwargs) +class ImageEmbeddingInput(_model_base.Model): + """Represents an image with optional text. + + All required parameters must be populated in order to send to server. + + :ivar image: The input image encoded in base64 string as a data URL. Example: + ``data:image/{format};base64,{data}``. Required. + :vartype image: str + :ivar text: Optional. The text input to feed into the model (like DINO, CLIP). + Returns a 422 error if the model doesn't support the value or parameter. + :vartype text: str + """ + + image: str = rest_field() + """The input image encoded in base64 string as a data URL. Example: + ``data:image/{format};base64,{data}``. Required.""" + text: Optional[str] = rest_field() + """Optional. The text input to feed into the model (like DINO, CLIP). + Returns a 422 error if the model doesn't support the value or parameter.""" + + @overload + def __init__( + self, + *, + image: str, + 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, **kwargs) + + class ImageUrl(_model_base.Model): """An internet location from which the model may retrieve an image. @@ -894,16 +986,16 @@ def __init__( *, url: str, detail: Optional[Union[str, "_models.ImageDetailLevel"]] = None, - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) @@ -915,7 +1007,7 @@ class ModelInfo(_model_base.Model): :vartype model_name: str :ivar model_type: The type of the AI model. A Unique identifier for the profile. Required. Known values are: "embeddings", "image_generation", "text_generation", "image_embeddings", - "audio_generation", and "chat". + "audio_generation", and "chat_completion". :vartype model_type: str or ~azure.ai.inference.models.ModelType :ivar model_provider_name: The model provider name. For example: ``Microsoft Research``. Required. @@ -927,7 +1019,7 @@ class ModelInfo(_model_base.Model): model_type: Union[str, "_models.ModelType"] = rest_field() """The type of the AI model. A Unique identifier for the profile. Required. Known values are: \"embeddings\", \"image_generation\", \"text_generation\", \"image_embeddings\", - \"audio_generation\", and \"chat\".""" + \"audio_generation\", and \"chat_completion\".""" model_provider_name: str = rest_field() """The model provider name. For example: ``Microsoft Research``. Required.""" @@ -938,16 +1030,16 @@ def __init__( model_name: str, model_type: Union[str, "_models.ModelType"], model_provider_name: str, - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) @@ -982,16 +1074,16 @@ def __init__( index: int, finish_reason: Union[str, "_models.CompletionsFinishReason"], delta: "_models.StreamingChatResponseMessageUpdate", - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) @@ -1046,16 +1138,16 @@ def __init__( model: str, usage: "_models.CompletionsUsage", choices: List["_models.StreamingChatChoiceUpdate"], - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) @@ -1090,16 +1182,16 @@ def __init__( role: Optional[Union[str, "_models.ChatRole"]] = None, content: Optional[str] = None, tool_calls: Optional[List["_models.StreamingChatResponseToolCallUpdate"]] = None, - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) @@ -1124,16 +1216,16 @@ def __init__( *, id: str, # pylint: disable=redefined-builtin function: "_models.FunctionCall", - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) @@ -1162,16 +1254,16 @@ def __init__( self, *, content: str, - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, role=ChatRole.SYSTEM, **kwargs) @@ -1198,16 +1290,16 @@ def __init__( self, *, text: str, - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, type="text", **kwargs) @@ -1240,16 +1332,16 @@ def __init__( *, content: str, tool_call_id: str, - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, role=ChatRole.TOOL, **kwargs) @@ -1269,7 +1361,7 @@ class UserMessage(ChatRequestMessage, discriminator="user"): role: Literal[ChatRole.USER] = rest_discriminator(name="role") # type: ignore """The chat role associated with this message, which is always 'user' for user messages. Required. The role that provides input for chat completions.""" - content: Union["str", List["_models.ContentItem"]] = rest_field() + content: Union[str, List["_models.ContentItem"]] = rest_field() """The contents of the user message, with available input types varying by selected model. Required. Is either a str type or a [ContentItem] type.""" @@ -1278,14 +1370,14 @@ def __init__( self, *, content: Union[str, List["_models.ContentItem"]], - ): ... + ) -> None: ... @overload - def __init__(self, mapping: Mapping[str, Any]): + def __init__(self, mapping: Mapping[str, Any]) -> None: """ :param mapping: raw JSON to initialize the model. :type mapping: Mapping[str, Any] """ - def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, role=ChatRole.USER, **kwargs) diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/models/_patch.py b/sdk/ai/azure-ai-inference/azure/ai/inference/models/_patch.py index 61c718eea63f..f7dd32510333 100644 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/models/_patch.py +++ b/sdk/ai/azure-ai-inference/azure/ai/inference/models/_patch.py @@ -6,273 +6,9 @@ Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize """ -import asyncio -import base64 -import json -import logging -import queue -import re -import sys +from typing import List -from typing import Any, List, AsyncIterator, Iterator, Optional, Union -from azure.core.rest import HttpResponse, AsyncHttpResponse -from ._models import ImageUrl as ImageUrlGenerated -from ._models import ChatCompletions as ChatCompletionsGenerated -from ._models import EmbeddingsResult as EmbeddingsResultGenerated -from .. import models as _models - -if sys.version_info >= (3, 11): - from typing import Self -else: - from typing_extensions import Self - -logger = logging.getLogger(__name__) - - -class ChatCompletions(ChatCompletionsGenerated): - """Representation of the response data from a chat completions request. - Completions support a wide variety of tasks and generate text that continues from or - "completes" - provided prompt data. - - - :ivar id: A unique identifier associated with this chat completions response. Required. - :vartype id: str - :ivar created: The first timestamp associated with generation activity for this completions - response, - represented as seconds since the beginning of the Unix epoch of 00:00 on 1 Jan 1970. Required. - :vartype created: ~datetime.datetime - :ivar model: The model used for the chat completion. Required. - :vartype model: str - :ivar usage: Usage information for tokens processed and generated as part of this completions - operation. Required. - :vartype usage: ~azure.ai.inference.models.CompletionsUsage - :ivar choices: The collection of completions choices associated with this completions response. - Generally, ``n`` choices are generated per provided prompt with a default value of 1. - Token limits and other settings may limit the number of choices generated. Required. - :vartype choices: list[~azure.ai.inference.models.ChatChoice] - """ - - def __str__(self) -> str: - # pylint: disable=client-method-name-no-double-underscore - return json.dumps(self.as_dict(), indent=2) - - -class EmbeddingsResult(EmbeddingsResultGenerated): - """Representation of the response data from an embeddings request. - Embeddings measure the relatedness of text strings and are commonly used for search, - clustering, - recommendations, and other similar scenarios. - - - :ivar data: Embedding values for the prompts submitted in the request. Required. - :vartype data: list[~azure.ai.inference.models.EmbeddingItem] - :ivar usage: Usage counts for tokens input using the embeddings API. Required. - :vartype usage: ~azure.ai.inference.models.EmbeddingsUsage - :ivar model: The model ID used to generate this result. Required. - :vartype model: str - """ - - def __str__(self) -> str: - # pylint: disable=client-method-name-no-double-underscore - return json.dumps(self.as_dict(), indent=2) - - -class ImageUrl(ImageUrlGenerated): - - @classmethod - def load( - cls, *, image_file: str, image_format: str, detail: Optional[Union[str, "_models.ImageDetailLevel"]] = None - ) -> Self: - """ - Create an ImageUrl object from a local image file. The method reads the image - file and encodes it as a base64 string, which together with the image format - is then used to format the JSON `url` value passed in the request payload. - - :ivar image_file: The name of the local image file to load. Required. - :vartype image_file: str - :ivar image_format: The MIME type format of the image. For example: "jpeg", "png". Required. - :vartype image_format: str - :ivar detail: The evaluation quality setting to use, which controls relative prioritization of - speed, token consumption, and accuracy. Known values are: "auto", "low", and "high". - :vartype detail: str or ~azure.ai.inference.models.ImageDetailLevel - :return: An ImageUrl object with the image data encoded as a base64 string. - :rtype: ~azure.ai.inference.models.ImageUrl - :raises FileNotFoundError: when the image file could not be opened. - """ - with open(image_file, "rb") as f: - image_data = base64.b64encode(f.read()).decode("utf-8") - url = f"data:image/{image_format};base64,{image_data}" - return cls(url=url, detail=detail) - - -class BaseStreamingChatCompletions: - """A base class for the sync and async streaming chat completions responses, holding any common code - to deserializes the Server Sent Events (SSE) response stream into chat completions updates, each one - represented by a StreamingChatCompletionsUpdate object. - """ - - # Enable detailed logs of SSE parsing. For development only, should be `False` by default. - _ENABLE_CLASS_LOGS = False - - # The prefix of each line in the SSE stream that contains a JSON string - # to deserialize into a StreamingChatCompletionsUpdate object - _SSE_DATA_EVENT_PREFIX = "data: " - - # The line indicating the end of the SSE stream - _SSE_DATA_EVENT_DONE = "data: [DONE]" - - def __init__(self): - self._queue: "queue.Queue[_models.StreamingChatCompletionsUpdate]" = queue.Queue() - self._incomplete_json = "" - self._done = False # Will be set to True when reading 'data: [DONE]' line - - def _deserialize_and_add_to_queue(self, element: bytes) -> bool: - - # Clear the queue of StreamingChatCompletionsUpdate before processing the next block - self._queue.queue.clear() - - # Convert `bytes` to string and split the string by newline, while keeping the new line char. - # the last may be a partial "line" that does not contain a newline char at the end. - line_list: List[str] = re.split(r"(?<=\n)", element.decode("utf-8")) - for index, line in enumerate(line_list): - - if self._ENABLE_CLASS_LOGS: - logger.debug("[Original line] %s", repr(line)) - - if index == 0: - line = self._incomplete_json + line - self._incomplete_json = "" - - if index == len(line_list) - 1 and not line.endswith("\n"): - self._incomplete_json = line - return False - - if self._ENABLE_CLASS_LOGS: - logger.debug("[Modified line] %s", repr(line)) - - if line == "\n": # Empty line, indicating flush output to client - continue - - if not line.startswith(self._SSE_DATA_EVENT_PREFIX): - raise ValueError(f"SSE event not supported (line `{line}`)") - - if line.startswith(self._SSE_DATA_EVENT_DONE): - if self._ENABLE_CLASS_LOGS: - logger.debug("[Done]") - return True - - # If you reached here, the line should contain `data: {...}\n` - # where the curly braces contain a valid JSON object. - # Deserialize it into a StreamingChatCompletionsUpdate object - # and add it to the queue. - # pylint: disable=W0212 # Access to a protected member _deserialize of a client class - update = _models.StreamingChatCompletionsUpdate._deserialize( - json.loads(line[len(self._SSE_DATA_EVENT_PREFIX) : -1]), [] - ) - - # We skip any update that has a None or empty choices list - # (this is what OpenAI Python SDK does) - if update.choices: - - # We update all empty content strings to None - # (this is what OpenAI Python SDK does) - # for choice in update.choices: - # if not choice.delta.content: - # choice.delta.content = None - - self._queue.put(update) - - if self._ENABLE_CLASS_LOGS: - logger.debug("[Added to queue]") - - return False - - -class StreamingChatCompletions(BaseStreamingChatCompletions): - """Represents an interator over StreamingChatCompletionsUpdate objects. It can be used for either synchronous or - asynchronous iterations. The class deserializes the Server Sent Events (SSE) response stream - into chat completions updates, each one represented by a StreamingChatCompletionsUpdate object. - """ - - def __init__(self, response: HttpResponse): - super().__init__() - self._response = response - self._bytes_iterator: Iterator[bytes] = response.iter_bytes() - - def __iter__(self) -> Any: - return self - - def __next__(self) -> "_models.StreamingChatCompletionsUpdate": - while self._queue.empty() and not self._done: - self._done = self._read_next_block() - if self._queue.empty(): - raise StopIteration - return self._queue.get() - - def _read_next_block(self) -> bool: - if self._ENABLE_CLASS_LOGS: - logger.debug("[Reading next block]") - try: - element = self._bytes_iterator.__next__() - except StopIteration: - self.close() - return True - return self._deserialize_and_add_to_queue(element) - - def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None: # type: ignore - self.close() - - def close(self) -> None: - self._response.close() - - -class AsyncStreamingChatCompletions(BaseStreamingChatCompletions): - """Represents an async interator over StreamingChatCompletionsUpdate objects. - It can be used for either synchronous or asynchronous iterations. The class - deserializes the Server Sent Events (SSE) response stream into chat - completions updates, each one represented by a StreamingChatCompletionsUpdate object. - """ - - def __init__(self, response: AsyncHttpResponse): - super().__init__() - self._response = response - self._bytes_iterator: AsyncIterator[bytes] = response.iter_bytes() - - def __aiter__(self) -> Any: - return self - - async def __anext__(self) -> "_models.StreamingChatCompletionsUpdate": - while self._queue.empty() and not self._done: - self._done = await self._read_next_block_async() - if self._queue.empty(): - raise StopAsyncIteration - return self._queue.get() - - async def _read_next_block_async(self) -> bool: - if self._ENABLE_CLASS_LOGS: - logger.debug("[Reading next block]") - try: - element = await self._bytes_iterator.__anext__() - except StopAsyncIteration: - await self.aclose() - return True - return self._deserialize_and_add_to_queue(element) - - def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None: # type: ignore - asyncio.run(self.aclose()) - - async def aclose(self) -> None: - await self._response.close() - - -__all__: List[str] = [ - "ImageUrl", - "ChatCompletions", - "EmbeddingsResult", - "StreamingChatCompletions", - "AsyncStreamingChatCompletions", -] # 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-inference/azure/ai/inference/prompts/__init__.py b/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/__init__.py deleted file mode 100644 index 2e11b31cb6a4..000000000000 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -# ------------------------------------ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. -# ------------------------------------ -# pylint: disable=unused-import -from ._patch import patch_sdk as _patch_sdk, PromptTemplate - -_patch_sdk() diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_core.py b/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_core.py deleted file mode 100644 index ec6702995149..000000000000 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_core.py +++ /dev/null @@ -1,312 +0,0 @@ -# ------------------------------------ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. -# ------------------------------------ -# mypy: disable-error-code="assignment,attr-defined,index,arg-type" -# pylint: disable=line-too-long,R,consider-iterating-dictionary,raise-missing-from,dangerous-default-value -from __future__ import annotations -import os -from dataclasses import dataclass, field, asdict -from pathlib import Path -from typing import Any, AsyncIterator, Dict, Iterator, List, Literal, Union -from ._tracer import Tracer, to_dict -from ._utils import load_json - - -@dataclass -class ToolCall: - id: str - name: str - arguments: str - - -@dataclass -class PropertySettings: - """PropertySettings class to define the properties of the model - - Attributes - ---------- - type : str - The type of the property - default : Any - The default value of the property - description : str - The description of the property - """ - - type: Literal["string", "number", "array", "object", "boolean"] - default: Union[str, int, float, List, Dict, bool, None] = field(default=None) - description: str = field(default="") - - -@dataclass -class ModelSettings: - """ModelSettings class to define the model of the prompty - - Attributes - ---------- - api : str - The api of the model - configuration : Dict - The configuration of the model - parameters : Dict - The parameters of the model - response : Dict - The response of the model - """ - - api: str = field(default="") - configuration: Dict = field(default_factory=dict) - parameters: Dict = field(default_factory=dict) - response: Dict = field(default_factory=dict) - - -@dataclass -class TemplateSettings: - """TemplateSettings class to define the template of the prompty - - Attributes - ---------- - type : str - The type of the template - parser : str - The parser of the template - """ - - type: str = field(default="mustache") - parser: str = field(default="") - - -@dataclass -class Prompty: - """Prompty class to define the prompty - - Attributes - ---------- - name : str - The name of the prompty - description : str - The description of the prompty - authors : List[str] - The authors of the prompty - tags : List[str] - The tags of the prompty - version : str - The version of the prompty - base : str - The base of the prompty - basePrompty : Prompty - The base prompty - model : ModelSettings - The model of the prompty - sample : Dict - The sample of the prompty - inputs : Dict[str, PropertySettings] - The inputs of the prompty - outputs : Dict[str, PropertySettings] - The outputs of the prompty - template : TemplateSettings - The template of the prompty - file : FilePath - The file of the prompty - content : Union[str, List[str], Dict] - The content of the prompty - """ - - # metadata - name: str = field(default="") - description: str = field(default="") - authors: List[str] = field(default_factory=list) - tags: List[str] = field(default_factory=list) - version: str = field(default="") - base: str = field(default="") - basePrompty: Union[Prompty, None] = field(default=None) - # model - model: ModelSettings = field(default_factory=ModelSettings) - - # sample - sample: Dict = field(default_factory=dict) - - # input / output - inputs: Dict[str, PropertySettings] = field(default_factory=dict) - outputs: Dict[str, PropertySettings] = field(default_factory=dict) - - # template - template: TemplateSettings = field(default_factory=TemplateSettings) - - file: Union[Path, str] = field(default="") - content: Union[str, List[str], Dict] = field(default="") - - def to_safe_dict(self) -> Dict[str, Any]: - d = {} - if self.model: - d["model"] = asdict(self.model) - _mask_secrets(d, ["model", "configuration"]) - if self.template: - d["template"] = asdict(self.template) - if self.inputs: - d["inputs"] = {k: asdict(v) for k, v in self.inputs.items()} - if self.outputs: - d["outputs"] = {k: asdict(v) for k, v in self.outputs.items()} - if self.file: - d["file"] = str(self.file.as_posix()) if isinstance(self.file, Path) else self.file - return d - - @staticmethod - def hoist_base_prompty(top: Prompty, base: Prompty) -> Prompty: - top.name = base.name if top.name == "" else top.name - top.description = base.description if top.description == "" else top.description - top.authors = list(set(base.authors + top.authors)) - top.tags = list(set(base.tags + top.tags)) - top.version = base.version if top.version == "" else top.version - - top.model.api = base.model.api if top.model.api == "" else top.model.api - top.model.configuration = param_hoisting(top.model.configuration, base.model.configuration) - top.model.parameters = param_hoisting(top.model.parameters, base.model.parameters) - top.model.response = param_hoisting(top.model.response, base.model.response) - - top.sample = param_hoisting(top.sample, base.sample) - - top.basePrompty = base - - return top - - @staticmethod - def _process_file(file: str, parent: Path) -> Any: - file_path = Path(parent / Path(file)).resolve().absolute() - if file_path.exists(): - items = load_json(file_path) - if isinstance(items, list): - return [Prompty.normalize(value, parent) for value in items] - elif isinstance(items, Dict): - return {key: Prompty.normalize(value, parent) for key, value in items.items()} - else: - return items - else: - raise FileNotFoundError(f"File {file} not found") - - @staticmethod - def _process_env(variable: str, env_error=True, default: Union[str, None] = None) -> Any: - if variable in os.environ.keys(): - return os.environ[variable] - else: - if default: - return default - if env_error: - raise ValueError(f"Variable {variable} not found in environment") - - return "" - - @staticmethod - def normalize(attribute: Any, parent: Path, env_error=True) -> Any: - if isinstance(attribute, str): - attribute = attribute.strip() - if attribute.startswith("${") and attribute.endswith("}"): - # check if env or file - variable = attribute[2:-1].split(":") - if variable[0] == "env" and len(variable) > 1: - return Prompty._process_env( - variable[1], - env_error, - variable[2] if len(variable) > 2 else None, - ) - elif variable[0] == "file" and len(variable) > 1: - return Prompty._process_file(variable[1], parent) - else: - raise ValueError(f"Invalid attribute format ({attribute})") - else: - return attribute - elif isinstance(attribute, list): - return [Prompty.normalize(value, parent) for value in attribute] - elif isinstance(attribute, Dict): - return {key: Prompty.normalize(value, parent) for key, value in attribute.items()} - else: - return attribute - - -def param_hoisting(top: Dict[str, Any], bottom: Dict[str, Any], top_key: Union[str, None] = None) -> Dict[str, Any]: - if top_key: - new_dict = {**top[top_key]} if top_key in top else {} - else: - new_dict = {**top} - for key, value in bottom.items(): - if not key in new_dict: - new_dict[key] = value - return new_dict - - -class PromptyStream(Iterator): - """PromptyStream class to iterate over LLM stream. - Necessary for Prompty to handle streaming data when tracing.""" - - def __init__(self, name: str, iterator: Iterator): - self.name = name - self.iterator = iterator - self.items: List[Any] = [] - self.__name__ = "PromptyStream" - - def __iter__(self): - return self - - def __next__(self): - try: - # enumerate but add to list - o = self.iterator.__next__() - self.items.append(o) - return o - - except StopIteration: - # StopIteration is raised - # contents are exhausted - if len(self.items) > 0: - with Tracer.start("PromptyStream") as trace: - trace("signature", f"{self.name}.PromptyStream") - trace("inputs", "None") - trace("result", [to_dict(s) for s in self.items]) - - raise StopIteration - - -class AsyncPromptyStream(AsyncIterator): - """AsyncPromptyStream class to iterate over LLM stream. - Necessary for Prompty to handle streaming data when tracing.""" - - def __init__(self, name: str, iterator: AsyncIterator): - self.name = name - self.iterator = iterator - self.items: List[Any] = [] - self.__name__ = "AsyncPromptyStream" - - def __aiter__(self): - return self - - async def __anext__(self): - try: - # enumerate but add to list - o = await self.iterator.__anext__() - self.items.append(o) - return o - - except StopAsyncIteration: - # StopIteration is raised - # contents are exhausted - if len(self.items) > 0: - with Tracer.start("AsyncPromptyStream") as trace: - trace("signature", f"{self.name}.AsyncPromptyStream") - trace("inputs", "None") - trace("result", [to_dict(s) for s in self.items]) - - raise StopAsyncIteration - - -def _mask_secrets(d: Dict[str, Any], path: list[str], patterns: list[str] = ["key", "secret"]) -> bool: - sub_d = d - for key in path: - if key not in sub_d: - return False - sub_d = sub_d[key] - - for k, v in sub_d.items(): - if any([pattern in k.lower() for pattern in patterns]): - sub_d[k] = "*" * len(v) - return True diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_invoker.py b/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_invoker.py deleted file mode 100644 index d682662e7b01..000000000000 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_invoker.py +++ /dev/null @@ -1,295 +0,0 @@ -# ------------------------------------ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. -# ------------------------------------ -# mypy: disable-error-code="return-value,operator" -# pylint: disable=line-too-long,R,docstring-missing-param,docstring-missing-return,docstring-missing-rtype,unnecessary-pass -import abc -from typing import Any, Callable, Dict, Literal -from ._tracer import trace -from ._core import Prompty - - -class Invoker(abc.ABC): - """Abstract class for Invoker - - Attributes - ---------- - prompty : Prompty - The prompty object - name : str - The name of the invoker - - """ - - def __init__(self, prompty: Prompty) -> None: - self.prompty = prompty - self.name = self.__class__.__name__ - - @abc.abstractmethod - def invoke(self, data: Any) -> Any: - """Abstract method to invoke the invoker - - Parameters - ---------- - data : Any - The data to be invoked - - Returns - ------- - Any - The invoked - """ - pass - - @abc.abstractmethod - async def invoke_async(self, data: Any) -> Any: - """Abstract method to invoke the invoker asynchronously - - Parameters - ---------- - data : Any - The data to be invoked - - Returns - ------- - Any - The invoked - """ - pass - - @trace - def run(self, data: Any) -> Any: - """Method to run the invoker - - Parameters - ---------- - data : Any - The data to be invoked - - Returns - ------- - Any - The invoked - """ - return self.invoke(data) - - @trace - async def run_async(self, data: Any) -> Any: - """Method to run the invoker asynchronously - - Parameters - ---------- - data : Any - The data to be invoked - - Returns - ------- - Any - The invoked - """ - return await self.invoke_async(data) - - -class InvokerFactory: - """Factory class for Invoker""" - - _renderers: Dict[str, Invoker] = {} - _parsers: Dict[str, Invoker] = {} - _executors: Dict[str, Invoker] = {} - _processors: Dict[str, Invoker] = {} - - @classmethod - def add_renderer(cls, name: str, invoker: Invoker) -> None: - cls._renderers[name] = invoker - - @classmethod - def add_parser(cls, name: str, invoker: Invoker) -> None: - cls._parsers[name] = invoker - - @classmethod - def add_executor(cls, name: str, invoker: Invoker) -> None: - cls._executors[name] = invoker - - @classmethod - def add_processor(cls, name: str, invoker: Invoker) -> None: - cls._processors[name] = invoker - - @classmethod - def register_renderer(cls, name: str) -> Callable: - def inner_wrapper(wrapped_class: Invoker) -> Callable: - cls._renderers[name] = wrapped_class - return wrapped_class # type: ignore - - return inner_wrapper - - @classmethod - def register_parser(cls, name: str) -> Callable: - def inner_wrapper(wrapped_class: Invoker) -> Callable: - cls._parsers[name] = wrapped_class - return wrapped_class # type: ignore - - return inner_wrapper - - @classmethod - def register_executor(cls, name: str) -> Callable: - def inner_wrapper(wrapped_class: Invoker) -> Callable: - cls._executors[name] = wrapped_class - return wrapped_class # type: ignore - - return inner_wrapper - - @classmethod - def register_processor(cls, name: str) -> Callable: - def inner_wrapper(wrapped_class: Invoker) -> Callable: - cls._processors[name] = wrapped_class - return wrapped_class # type: ignore - - return inner_wrapper - - @classmethod - def _get_name( - cls, - type: Literal["renderer", "parser", "executor", "processor"], - prompty: Prompty, - ) -> str: - if type == "renderer": - return prompty.template.type - elif type == "parser": - return f"{prompty.template.parser}.{prompty.model.api}" - elif type == "executor": - return prompty.model.configuration["type"] - elif type == "processor": - return prompty.model.configuration["type"] - else: - raise ValueError(f"Type {type} not found") - - @classmethod - def _get_invoker( - cls, - type: Literal["renderer", "parser", "executor", "processor"], - prompty: Prompty, - ) -> Invoker: - if type == "renderer": - name = prompty.template.type - if name not in cls._renderers: - raise ValueError(f"Renderer {name} not found") - - return cls._renderers[name](prompty) # type: ignore - - elif type == "parser": - name = f"{prompty.template.parser}.{prompty.model.api}" - if name not in cls._parsers: - raise ValueError(f"Parser {name} not found") - - return cls._parsers[name](prompty) # type: ignore - - elif type == "executor": - name = prompty.model.configuration["type"] - if name not in cls._executors: - raise ValueError(f"Executor {name} not found") - - return cls._executors[name](prompty) # type: ignore - - elif type == "processor": - name = prompty.model.configuration["type"] - if name not in cls._processors: - raise ValueError(f"Processor {name} not found") - - return cls._processors[name](prompty) # type: ignore - - else: - raise ValueError(f"Type {type} not found") - - @classmethod - def run( - cls, - type: Literal["renderer", "parser", "executor", "processor"], - prompty: Prompty, - data: Any, - default: Any = None, - ): - name = cls._get_name(type, prompty) - if name.startswith("NOOP") and default is not None: - return default - elif name.startswith("NOOP"): - return data - - invoker = cls._get_invoker(type, prompty) - value = invoker.run(data) - return value - - @classmethod - async def run_async( - cls, - type: Literal["renderer", "parser", "executor", "processor"], - prompty: Prompty, - data: Any, - default: Any = None, - ): - name = cls._get_name(type, prompty) - if name.startswith("NOOP") and default is not None: - return default - elif name.startswith("NOOP"): - return data - invoker = cls._get_invoker(type, prompty) - value = await invoker.run_async(data) - return value - - @classmethod - def run_renderer(cls, prompty: Prompty, data: Any, default: Any = None) -> Any: - return cls.run("renderer", prompty, data, default) - - @classmethod - async def run_renderer_async(cls, prompty: Prompty, data: Any, default: Any = None) -> Any: - return await cls.run_async("renderer", prompty, data, default) - - @classmethod - def run_parser(cls, prompty: Prompty, data: Any, default: Any = None) -> Any: - return cls.run("parser", prompty, data, default) - - @classmethod - async def run_parser_async(cls, prompty: Prompty, data: Any, default: Any = None) -> Any: - return await cls.run_async("parser", prompty, data, default) - - @classmethod - def run_executor(cls, prompty: Prompty, data: Any, default: Any = None) -> Any: - return cls.run("executor", prompty, data, default) - - @classmethod - async def run_executor_async(cls, prompty: Prompty, data: Any, default: Any = None) -> Any: - return await cls.run_async("executor", prompty, data, default) - - @classmethod - def run_processor(cls, prompty: Prompty, data: Any, default: Any = None) -> Any: - return cls.run("processor", prompty, data, default) - - @classmethod - async def run_processor_async(cls, prompty: Prompty, data: Any, default: Any = None) -> Any: - return await cls.run_async("processor", prompty, data, default) - - -class InvokerException(Exception): - """Exception class for Invoker""" - - def __init__(self, message: str, type: str) -> None: - super().__init__(message) - self.type = type - - def __str__(self) -> str: - return f"{super().__str__()}. Make sure to pip install any necessary package extras (i.e. could be something like `pip install prompty[{self.type}]`) for {self.type} as well as import the appropriate invokers (i.e. could be something like `import prompty.{self.type}`)." - - -@InvokerFactory.register_renderer("NOOP") -@InvokerFactory.register_parser("NOOP") -@InvokerFactory.register_executor("NOOP") -@InvokerFactory.register_processor("NOOP") -@InvokerFactory.register_parser("prompty.embedding") -@InvokerFactory.register_parser("prompty.image") -@InvokerFactory.register_parser("prompty.completion") -class NoOp(Invoker): - def invoke(self, data: Any) -> Any: - return data - - async def invoke_async(self, data: str) -> Any: - return self.invoke(data) diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_mustache.py b/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_mustache.py deleted file mode 100644 index f7a0c21d8bb8..000000000000 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_mustache.py +++ /dev/null @@ -1,671 +0,0 @@ -# ------------------------------------ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. -# ------------------------------------ -# pylint: disable=line-too-long,R,consider-using-dict-items,docstring-missing-return,docstring-missing-rtype,docstring-missing-param,global-statement,unused-argument,global-variable-not-assigned,protected-access,logging-fstring-interpolation,deprecated-method -from __future__ import annotations -import logging -from collections.abc import Iterator, Sequence -from types import MappingProxyType -from typing import ( - Any, - Dict, - List, - Literal, - Mapping, - Optional, - Union, - cast, -) -from typing_extensions import TypeAlias - -logger = logging.getLogger(__name__) - - -Scopes: TypeAlias = List[Union[Literal[False, 0], Mapping[str, Any]]] - - -# Globals -_CURRENT_LINE = 1 -_LAST_TAG_LINE = None - - -class ChevronError(SyntaxError): - """Custom exception for Chevron errors.""" - - -# -# Helper functions -# - - -def grab_literal(template: str, l_del: str) -> tuple[str, str]: - """Parse a literal from the template. - - Args: - template: The template to parse. - l_del: The left delimiter. - - Returns: - Tuple[str, str]: The literal and the template. - """ - - global _CURRENT_LINE - - try: - # Look for the next tag and move the template to it - literal, template = template.split(l_del, 1) - _CURRENT_LINE += literal.count("\n") - return (literal, template) - - # There are no more tags in the template? - except ValueError: - # Then the rest of the template is a literal - return (template, "") - - -def l_sa_check(template: str, literal: str, is_standalone: bool) -> bool: - """Do a preliminary check to see if a tag could be a standalone. - - Args: - template: The template. (Not used.) - literal: The literal. - is_standalone: Whether the tag is standalone. - - Returns: - bool: Whether the tag could be a standalone. - """ - - # If there is a newline, or the previous tag was a standalone - if literal.find("\n") != -1 or is_standalone: - padding = literal.split("\n")[-1] - - # If all the characters since the last newline are spaces - # Then the next tag could be a standalone - # Otherwise it can't be - return padding.isspace() or padding == "" - else: - return False - - -def r_sa_check(template: str, tag_type: str, is_standalone: bool) -> bool: - """Do a final check to see if a tag could be a standalone. - - Args: - template: The template. - tag_type: The type of the tag. - is_standalone: Whether the tag is standalone. - - Returns: - bool: Whether the tag could be a standalone. - """ - - # Check right side if we might be a standalone - if is_standalone and tag_type not in ["variable", "no escape"]: - on_newline = template.split("\n", 1) - - # If the stuff to the right of us are spaces we're a standalone - return on_newline[0].isspace() or not on_newline[0] - - # If we're a tag can't be a standalone - else: - return False - - -def parse_tag(template: str, l_del: str, r_del: str) -> tuple[tuple[str, str], str]: - """Parse a tag from a template. - - Args: - template: The template. - l_del: The left delimiter. - r_del: The right delimiter. - - Returns: - Tuple[Tuple[str, str], str]: The tag and the template. - - Raises: - ChevronError: If the tag is unclosed. - ChevronError: If the set delimiter tag is unclosed. - """ - global _CURRENT_LINE - global _LAST_TAG_LINE - - tag_types = { - "!": "comment", - "#": "section", - "^": "inverted section", - "/": "end", - ">": "partial", - "=": "set delimiter?", - "{": "no escape?", - "&": "no escape", - } - - # Get the tag - try: - tag, template = template.split(r_del, 1) - except ValueError as e: - msg = "unclosed tag " f"at line {_CURRENT_LINE}" - raise ChevronError(msg) from e - - # Find the type meaning of the first character - tag_type = tag_types.get(tag[0], "variable") - - # If the type is not a variable - if tag_type != "variable": - # Then that first character is not needed - tag = tag[1:] - - # If we might be a set delimiter tag - if tag_type == "set delimiter?": - # Double check to make sure we are - if tag.endswith("="): - tag_type = "set delimiter" - # Remove the equal sign - tag = tag[:-1] - - # Otherwise we should complain - else: - msg = "unclosed set delimiter tag\n" f"at line {_CURRENT_LINE}" - raise ChevronError(msg) - - elif ( - # If we might be a no html escape tag - tag_type == "no escape?" - # And we have a third curly brace - # (And are using curly braces as delimiters) - and l_del == "{{" - and r_del == "}}" - and template.startswith("}") - ): - # Then we are a no html escape tag - template = template[1:] - tag_type = "no escape" - - # Strip the whitespace off the key and return - return ((tag_type, tag.strip()), template) - - -# -# The main tokenizing function -# - - -def tokenize(template: str, def_ldel: str = "{{", def_rdel: str = "}}") -> Iterator[tuple[str, str]]: - """Tokenize a mustache template. - - Tokenizes a mustache template in a generator fashion, - using file-like objects. It also accepts a string containing - the template. - - - Arguments: - - template -- a file-like object, or a string of a mustache template - - def_ldel -- The default left delimiter - ("{{" by default, as in spec compliant mustache) - - def_rdel -- The default right delimiter - ("}}" by default, as in spec compliant mustache) - - - Returns: - - A generator of mustache tags in the form of a tuple - - -- (tag_type, tag_key) - - Where tag_type is one of: - * literal - * section - * inverted section - * end - * partial - * no escape - - And tag_key is either the key or in the case of a literal tag, - the literal itself. - """ - - global _CURRENT_LINE, _LAST_TAG_LINE - _CURRENT_LINE = 1 - _LAST_TAG_LINE = None - - is_standalone = True - open_sections = [] - l_del = def_ldel - r_del = def_rdel - - while template: - literal, template = grab_literal(template, l_del) - - # If the template is completed - if not template: - # Then yield the literal and leave - yield ("literal", literal) - break - - # Do the first check to see if we could be a standalone - is_standalone = l_sa_check(template, literal, is_standalone) - - # Parse the tag - tag, template = parse_tag(template, l_del, r_del) - tag_type, tag_key = tag - - # Special tag logic - - # If we are a set delimiter tag - if tag_type == "set delimiter": - # Then get and set the delimiters - dels = tag_key.strip().split(" ") - l_del, r_del = dels[0], dels[-1] - - # If we are a section tag - elif tag_type in ["section", "inverted section"]: - # Then open a new section - open_sections.append(tag_key) - _LAST_TAG_LINE = _CURRENT_LINE - - # If we are an end tag - elif tag_type == "end": - # Then check to see if the last opened section - # is the same as us - try: - last_section = open_sections.pop() - except IndexError as e: - msg = f'Trying to close tag "{tag_key}"\n' "Looks like it was not opened.\n" f"line {_CURRENT_LINE + 1}" - raise ChevronError(msg) from e - if tag_key != last_section: - # Otherwise we need to complain - msg = ( - f'Trying to close tag "{tag_key}"\n' - f'last open tag is "{last_section}"\n' - f"line {_CURRENT_LINE + 1}" - ) - raise ChevronError(msg) - - # Do the second check to see if we're a standalone - is_standalone = r_sa_check(template, tag_type, is_standalone) - - # Which if we are - if is_standalone: - # Remove the stuff before the newline - template = template.split("\n", 1)[-1] - - # Partials need to keep the spaces on their left - if tag_type != "partial": - # But other tags don't - literal = literal.rstrip(" ") - - # Start yielding - # Ignore literals that are empty - if literal != "": - yield ("literal", literal) - - # Ignore comments and set delimiters - if tag_type not in ["comment", "set delimiter?"]: - yield (tag_type, tag_key) - - # If there are any open sections when we're done - if open_sections: - # Then we need to complain - msg = ( - "Unexpected EOF\n" - f'the tag "{open_sections[-1]}" was never closed\n' - f"was opened at line {_LAST_TAG_LINE}" - ) - raise ChevronError(msg) - - -# -# Helper functions -# - - -def _html_escape(string: str) -> str: - """HTML escape all of these " & < >""" - - html_codes = { - '"': """, - "<": "<", - ">": ">", - } - - # & must be handled first - string = string.replace("&", "&") - for char in html_codes: - string = string.replace(char, html_codes[char]) - return string - - -def _get_key( - key: str, - scopes: Scopes, - warn: bool, - keep: bool, - def_ldel: str, - def_rdel: str, -) -> Any: - """Get a key from the current scope""" - - # If the key is a dot - if key == ".": - # Then just return the current scope - return scopes[0] - - # Loop through the scopes - for scope in scopes: - try: - # Return an empty string if falsy, with two exceptions - # 0 should return 0, and False should return False - if scope in (0, False): - return scope - - # For every dot separated key - for child in key.split("."): - # Return an empty string if falsy, with two exceptions - # 0 should return 0, and False should return False - if scope in (0, False): - return scope - # Move into the scope - try: - # Try subscripting (Normal dictionaries) - scope = cast(Dict[str, Any], scope)[child] - except (TypeError, AttributeError): - try: - scope = getattr(scope, child) - except (TypeError, AttributeError): - # Try as a list - scope = scope[int(child)] # type: ignore - - try: - # This allows for custom falsy data types - # https://github.com/noahmorrison/chevron/issues/35 - if scope._CHEVRON_return_scope_when_falsy: # type: ignore - return scope - except AttributeError: - if scope in (0, False): - return scope - return scope or "" - except (AttributeError, KeyError, IndexError, ValueError): - # We couldn't find the key in the current scope - # We'll try again on the next pass - pass - - # We couldn't find the key in any of the scopes - - if warn: - logger.warn(f"Could not find key '{key}'") - - if keep: - return f"{def_ldel} {key} {def_rdel}" - - return "" - - -def _get_partial(name: str, partials_dict: Mapping[str, str]) -> str: - """Load a partial""" - try: - # Maybe the partial is in the dictionary - return partials_dict[name] - except KeyError: - return "" - - -# -# The main rendering function -# -g_token_cache: Dict[str, List[tuple[str, str]]] = {} - -EMPTY_DICT: MappingProxyType[str, str] = MappingProxyType({}) - - -def render( - template: Union[str, List[tuple[str, str]]] = "", - data: Mapping[str, Any] = EMPTY_DICT, - partials_dict: Mapping[str, str] = EMPTY_DICT, - padding: str = "", - def_ldel: str = "{{", - def_rdel: str = "}}", - scopes: Optional[Scopes] = None, - warn: bool = False, - keep: bool = False, -) -> str: - """Render a mustache template. - - Renders a mustache template with a data scope and inline partial capability. - - Arguments: - - template -- A file-like object or a string containing the template. - - data -- A python dictionary with your data scope. - - partials_path -- The path to where your partials are stored. - If set to None, then partials won't be loaded from the file system - (defaults to '.'). - - partials_ext -- The extension that you want the parser to look for - (defaults to 'mustache'). - - partials_dict -- A python dictionary which will be search for partials - before the filesystem is. {'include': 'foo'} is the same - as a file called include.mustache - (defaults to {}). - - padding -- This is for padding partials, and shouldn't be used - (but can be if you really want to). - - def_ldel -- The default left delimiter - ("{{" by default, as in spec compliant mustache). - - def_rdel -- The default right delimiter - ("}}" by default, as in spec compliant mustache). - - scopes -- The list of scopes that get_key will look through. - - warn -- Log a warning when a template substitution isn't found in the data - - keep -- Keep unreplaced tags when a substitution isn't found in the data. - - - Returns: - - A string containing the rendered template. - """ - - # If the template is a sequence but not derived from a string - if isinstance(template, Sequence) and not isinstance(template, str): - # Then we don't need to tokenize it - # But it does need to be a generator - tokens: Iterator[tuple[str, str]] = (token for token in template) - else: - if template in g_token_cache: - tokens = (token for token in g_token_cache[template]) - else: - # Otherwise make a generator - tokens = tokenize(template, def_ldel, def_rdel) - - output = "" - - if scopes is None: - scopes = [data] - - # Run through the tokens - for tag, key in tokens: - # Set the current scope - current_scope = scopes[0] - - # If we're an end tag - if tag == "end": - # Pop out of the latest scope - del scopes[0] - - # If the current scope is falsy and not the only scope - elif not current_scope and len(scopes) != 1: - if tag in ["section", "inverted section"]: - # Set the most recent scope to a falsy value - scopes.insert(0, False) - - # If we're a literal tag - elif tag == "literal": - # Add padding to the key and add it to the output - output += key.replace("\n", "\n" + padding) - - # If we're a variable tag - elif tag == "variable": - # Add the html escaped key to the output - thing = _get_key(key, scopes, warn=warn, keep=keep, def_ldel=def_ldel, def_rdel=def_rdel) - if thing is True and key == ".": - # if we've coerced into a boolean by accident - # (inverted tags do this) - # then get the un-coerced object (next in the stack) - thing = scopes[1] - if not isinstance(thing, str): - thing = str(thing) - output += _html_escape(thing) - - # If we're a no html escape tag - elif tag == "no escape": - # Just lookup the key and add it - thing = _get_key(key, scopes, warn=warn, keep=keep, def_ldel=def_ldel, def_rdel=def_rdel) - if not isinstance(thing, str): - thing = str(thing) - output += thing - - # If we're a section tag - elif tag == "section": - # Get the sections scope - scope = _get_key(key, scopes, warn=warn, keep=keep, def_ldel=def_ldel, def_rdel=def_rdel) - - # If the scope is a callable (as described in - # https://mustache.github.io/mustache.5.html) - if callable(scope): - # Generate template text from tags - text = "" - tags: List[tuple[str, str]] = [] - for token in tokens: - if token == ("end", key): - break - - tags.append(token) - tag_type, tag_key = token - if tag_type == "literal": - text += tag_key - elif tag_type == "no escape": - text += f"{def_ldel}& {tag_key} {def_rdel}" - else: - text += "{}{} {}{}".format( - def_ldel, - { - "comment": "!", - "section": "#", - "inverted section": "^", - "end": "/", - "partial": ">", - "set delimiter": "=", - "no escape": "&", - "variable": "", - }[tag_type], - tag_key, - def_rdel, - ) - - g_token_cache[text] = tags - - rend = scope( - text, - lambda template, data=None: render( - template, - data={}, - partials_dict=partials_dict, - padding=padding, - def_ldel=def_ldel, - def_rdel=def_rdel, - scopes=data and [data] + scopes or scopes, - warn=warn, - keep=keep, - ), - ) - - output += rend # type: ignore[reportOperatorIssue] - - # If the scope is a sequence, an iterator or generator but not - # derived from a string - elif isinstance(scope, (Sequence, Iterator)) and not isinstance(scope, str): - # Then we need to do some looping - - # Gather up all the tags inside the section - # (And don't be tricked by nested end tags with the same key) - # TODO: This feels like it still has edge cases, no? - tags = [] - tags_with_same_key = 0 - for token in tokens: - if token == ("section", key): - tags_with_same_key += 1 - if token == ("end", key): - tags_with_same_key -= 1 - if tags_with_same_key < 0: - break - tags.append(token) - - # For every item in the scope - for thing in scope: - # Append it as the most recent scope and render - new_scope = [thing] + scopes - rend = render( - template=tags, - scopes=new_scope, - padding=padding, - partials_dict=partials_dict, - def_ldel=def_ldel, - def_rdel=def_rdel, - warn=warn, - keep=keep, - ) - - output += rend - - else: - # Otherwise we're just a scope section - scopes.insert(0, scope) # type: ignore[reportArgumentType] - - # If we're an inverted section - elif tag == "inverted section": - # Add the flipped scope to the scopes - scope = _get_key(key, scopes, warn=warn, keep=keep, def_ldel=def_ldel, def_rdel=def_rdel) - scopes.insert(0, cast(Literal[False], not scope)) - - # If we're a partial - elif tag == "partial": - # Load the partial - partial = _get_partial(key, partials_dict) - - # Find what to pad the partial with - left = output.rpartition("\n")[2] - part_padding = padding - if left.isspace(): - part_padding += left - - # Render the partial - part_out = render( - template=partial, - partials_dict=partials_dict, - def_ldel=def_ldel, - def_rdel=def_rdel, - padding=part_padding, - scopes=scopes, - warn=warn, - keep=keep, - ) - - # If the partial was indented - if left.isspace(): - # then remove the spaces from the end - part_out = part_out.rstrip(" \t") - - # Add the partials output to the output - output += part_out - - return output diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_parsers.py b/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_parsers.py deleted file mode 100644 index de3c570e5c89..000000000000 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_parsers.py +++ /dev/null @@ -1,156 +0,0 @@ -# ------------------------------------ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. -# ------------------------------------ -# mypy: disable-error-code="union-attr,return-value" -# pylint: disable=line-too-long,R,consider-using-enumerate,docstring-missing-param,docstring-missing-return,docstring-missing-rtype -import re -import base64 -from pathlib import Path -from typing import Any, Union -from ._core import Prompty -from ._invoker import Invoker, InvokerFactory - - -ROLES = ["assistant", "function", "system", "user"] - - -@InvokerFactory.register_parser("prompty.chat") -class PromptyChatParser(Invoker): - """Prompty Chat Parser""" - - def __init__(self, prompty: Prompty) -> None: - super().__init__(prompty) - self.path = Path(self.prompty.file).parent - - def invoke(self, data: str) -> Any: - return invoke_parser(self.path, data) - - async def invoke_async(self, data: str) -> Any: - """Invoke the Prompty Chat Parser (Async) - - Parameters - ---------- - data : str - The data to parse - - Returns - ------- - str - The parsed data - """ - return self.invoke(data) - - -def _inline_image(path: Union[Path, None], image_item: str) -> str: - """Inline Image - - Parameters - ---------- - image_item : str - The image item to inline - - Returns - ------- - str - The inlined image - """ - # pass through if it's a url or base64 encoded or the path is None - if image_item.startswith("http") or image_item.startswith("data") or path is None: - return image_item - # otherwise, it's a local file - need to base64 encode it - else: - image_path = (path if path is not None else Path(".")) / image_item - with open(image_path, "rb") as f: - base64_image = base64.b64encode(f.read()).decode("utf-8") - - if image_path.suffix == ".png": - return f"data:image/png;base64,{base64_image}" - elif image_path.suffix == ".jpg": - return f"data:image/jpeg;base64,{base64_image}" - elif image_path.suffix == ".jpeg": - return f"data:image/jpeg;base64,{base64_image}" - else: - raise ValueError( - f"Invalid image format {image_path.suffix} - currently only .png and .jpg / .jpeg are supported." - ) - - -def _parse_content(path: Union[Path, None], content: str): - """for parsing inline images - - Parameters - ---------- - content : str - The content to parse - - Returns - ------- - any - The parsed content - """ - # regular expression to parse markdown images - image = r"(?P!\[[^\]]*\])\((?P.*?)(?=\"|\))\)" - matches = re.findall(image, content, flags=re.MULTILINE) - if len(matches) > 0: - content_items = [] - content_chunks = re.split(image, content, flags=re.MULTILINE) - current_chunk = 0 - for i in range(len(content_chunks)): - # image entry - if current_chunk < len(matches) and content_chunks[i] == matches[current_chunk][0]: - content_items.append( - { - "type": "image_url", - "image_url": {"url": _inline_image(path, matches[current_chunk][1].split(" ")[0].strip())}, - } - ) - # second part of image entry - elif current_chunk < len(matches) and content_chunks[i] == matches[current_chunk][1]: - current_chunk += 1 - # text entry - else: - if len(content_chunks[i].strip()) > 0: - content_items.append({"type": "text", "text": content_chunks[i].strip()}) - return content_items - else: - return content - - -def invoke_parser(path: Union[Path, None], data: str) -> Any: - """Invoke the Prompty Chat Parser - - Parameters - ---------- - data : str - The data to parse - - Returns - ------- - str - The parsed data - """ - messages = [] - separator = r"(?i)^\s*#?\s*(" + "|".join(ROLES) + r")\s*:\s*\n" - - # get valid chunks - remove empty items - chunks = [item for item in re.split(separator, data, flags=re.MULTILINE) if len(item.strip()) > 0] - - # if no starter role, then inject system role - if not chunks[0].strip().lower() in ROLES: - chunks.insert(0, "system") - - # if last chunk is role entry, then remove (no content?) - if chunks[-1].strip().lower() in ROLES: - chunks.pop() - - if len(chunks) % 2 != 0: - raise ValueError("Invalid prompt format") - - # create messages - for i in range(0, len(chunks), 2): - role = chunks[i].strip().lower() - content = chunks[i + 1].strip() - messages.append({"role": role, "content": _parse_content(path, content)}) - - return messages diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_patch.py b/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_patch.py deleted file mode 100644 index 14ad4f62b4c1..000000000000 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_patch.py +++ /dev/null @@ -1,124 +0,0 @@ -# ------------------------------------ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. -# ------------------------------------ -# pylint: disable=line-too-long,R -"""Customize generated code here. - -Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize -""" - -import traceback -from pathlib import Path -from typing import Any, Dict, List, Optional -from typing_extensions import Self -from ._core import Prompty -from ._mustache import render -from ._parsers import invoke_parser -from ._prompty_utils import load, prepare -from ._utils import remove_leading_empty_space - - -class PromptTemplate: - """The helper class which takes variant of inputs, e.g. Prompty format or string, and returns the parsed prompt in an array.""" - - @classmethod - def from_prompty(cls, file_path: str) -> Self: - """Initialize a PromptTemplate object from a prompty file. - - :param file_path: The path to the prompty file. - :type file_path: str - :return: The PromptTemplate object. - :rtype: PromptTemplate - """ - if not file_path: - raise ValueError("Please provide file_path") - - # Get the absolute path of the file by `traceback.extract_stack()`, it's "-2" because: - # In the stack, the last function is the current function. - # The second last function is the caller function, which is the root of the file_path. - stack = traceback.extract_stack() - caller = Path(stack[-2].filename) - abs_file_path = Path(caller.parent / Path(file_path)).resolve().absolute() - - prompty = load(str(abs_file_path)) - return cls(prompty=prompty) - - @classmethod - def from_string(cls, prompt_template: str, api: str = "chat", model_name: Optional[str] = None) -> Self: - """Initialize a PromptTemplate object from a message template. - - :param prompt_template: The prompt template string. - :type prompt_template: str - :param api: The API type, e.g. "chat" or "completion". - :type api: str - :param model_name: The model name, e.g. "gpt-4o-mini". - :type model_name: str - :return: The PromptTemplate object. - :rtype: PromptTemplate - """ - return cls( - api=api, - prompt_template=prompt_template, - model_name=model_name, - prompty=None, - ) - - def __init__( - self, - *, - api: str = "chat", - prompty: Optional[Prompty] = None, - prompt_template: Optional[str] = None, - model_name: Optional[str] = None, - ) -> None: - self.prompty = prompty - if self.prompty is not None: - self.model_name = ( - self.prompty.model.configuration["azure_deployment"] - if "azure_deployment" in self.prompty.model.configuration - else None - ) - self.parameters = self.prompty.model.parameters - self._config = {} - elif prompt_template is not None: - self.model_name = model_name - self.parameters = {} - # _config is a dict to hold the internal configuration - self._config = { - "api": api if api is not None else "chat", - "prompt_template": prompt_template, - } - else: - raise ValueError("Please pass valid arguments for PromptTemplate") - - def create_messages(self, data: Optional[Dict[str, Any]] = None, **kwargs) -> List[Dict[str, Any]]: - """Render the prompt template with the given data. - - :param data: The data to render the prompt template with. - :type data: Optional[Dict[str, Any]] - :return: The rendered prompt template. - :rtype: List[Dict[str, Any]] - """ - if data is None: - data = kwargs - - if self.prompty is not None: - parsed = prepare(self.prompty, data) - return parsed - elif "prompt_template" in self._config: - prompt_template = remove_leading_empty_space(self._config["prompt_template"]) - system_prompt_str = render(prompt_template, data) - parsed = invoke_parser(None, system_prompt_str) - return parsed - else: - raise ValueError("Please provide valid prompt template") - - -def patch_sdk(): - """Do not remove from this file. - - `patch_sdk` is a last resort escape hatch that allows you to do customizations - you can't accomplish using the techniques described in - https://aka.ms/azsdk/python/dpcodegen/python/customize - """ diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_prompty_utils.py b/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_prompty_utils.py deleted file mode 100644 index 5ea38bda6229..000000000000 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_prompty_utils.py +++ /dev/null @@ -1,415 +0,0 @@ -# ------------------------------------ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. -# ------------------------------------ -# mypy: disable-error-code="assignment" -# pylint: disable=R,docstring-missing-param,docstring-missing-return,docstring-missing-rtype,dangerous-default-value,redefined-outer-name,unused-wildcard-import,wildcard-import,raise-missing-from -import traceback -from pathlib import Path -from typing import Any, Dict, List, Union -from ._tracer import trace -from ._invoker import InvokerFactory -from ._core import ( - ModelSettings, - Prompty, - PropertySettings, - TemplateSettings, - param_hoisting, -) -from ._utils import ( - load_global_config, - load_prompty, -) - -from ._renderers import * -from ._parsers import * - - -@trace(description="Create a headless prompty object for programmatic use.") -def headless( - api: str, - content: Union[str, List[str], dict], - configuration: Dict[str, Any] = {}, - parameters: Dict[str, Any] = {}, - connection: str = "default", -) -> Prompty: - """Create a headless prompty object for programmatic use. - - Parameters - ---------- - api : str - The API to use for the model - content : Union[str, List[str], dict] - The content to process - configuration : Dict[str, Any], optional - The configuration to use, by default {} - parameters : Dict[str, Any], optional - The parameters to use, by default {} - connection : str, optional - The connection to use, by default "default" - - Returns - ------- - Prompty - The headless prompty object - - Example - ------- - >>> import prompty - >>> p = prompty.headless( - api="embedding", - configuration={"type": "azure", "azure_deployment": "text-embedding-ada-002"}, - content="hello world", - ) - >>> emb = prompty.execute(p) - - """ - - # get caller's path (to get relative path for prompty.json) - caller = Path(traceback.extract_stack()[-2].filename) - templateSettings = TemplateSettings(type="NOOP", parser="NOOP") - modelSettings = ModelSettings( - api=api, - configuration=Prompty.normalize( - param_hoisting(configuration, load_global_config(caller.parent, connection)), - caller.parent, - ), - parameters=parameters, - ) - - return Prompty(model=modelSettings, template=templateSettings, content=content) - - -def _load_raw_prompty(attributes: dict, content: str, p: Path, global_config: dict): - if "model" not in attributes: - attributes["model"] = {} - - if "configuration" not in attributes["model"]: - attributes["model"]["configuration"] = global_config - else: - attributes["model"]["configuration"] = param_hoisting( - attributes["model"]["configuration"], - global_config, - ) - - # pull model settings out of attributes - try: - model = ModelSettings(**attributes.pop("model")) - except Exception as e: - raise ValueError(f"Error in model settings: {e}") - - # pull template settings - try: - if "template" in attributes: - t = attributes.pop("template") - if isinstance(t, dict): - template = TemplateSettings(**t) - # has to be a string denoting the type - else: - template = TemplateSettings(type=t, parser="prompty") - else: - template = TemplateSettings(type="mustache", parser="prompty") - except Exception as e: - raise ValueError(f"Error in template loader: {e}") - - # formalize inputs and outputs - if "inputs" in attributes: - try: - inputs = {k: PropertySettings(**v) for (k, v) in attributes.pop("inputs").items()} - except Exception as e: - raise ValueError(f"Error in inputs: {e}") - else: - inputs = {} - if "outputs" in attributes: - try: - outputs = {k: PropertySettings(**v) for (k, v) in attributes.pop("outputs").items()} - except Exception as e: - raise ValueError(f"Error in outputs: {e}") - else: - outputs = {} - - prompty = Prompty( - **attributes, - model=model, - inputs=inputs, - outputs=outputs, - template=template, - content=content, - file=p, - ) - - return prompty - - -@trace(description="Load a prompty file.") -def load(prompty_file: Union[str, Path], configuration: str = "default") -> Prompty: - """Load a prompty file. - - Parameters - ---------- - prompty_file : Union[str, Path] - The path to the prompty file - configuration : str, optional - The configuration to use, by default "default" - - Returns - ------- - Prompty - The loaded prompty object - - Example - ------- - >>> import prompty - >>> p = prompty.load("prompts/basic.prompty") - >>> print(p) - """ - - p = Path(prompty_file) - if not p.is_absolute(): - # get caller's path (take into account trace frame) - caller = Path(traceback.extract_stack()[-3].filename) - p = Path(caller.parent / p).resolve().absolute() - - # load dictionary from prompty file - matter = load_prompty(p) - - attributes = matter["attributes"] - content = matter["body"] - - # normalize attribute dictionary resolve keys and files - attributes = Prompty.normalize(attributes, p.parent) - - # load global configuration - global_config = Prompty.normalize(load_global_config(p.parent, configuration), p.parent) - - prompty = _load_raw_prompty(attributes, content, p, global_config) - - # recursive loading of base prompty - if "base" in attributes: - # load the base prompty from the same directory as the current prompty - base = load(p.parent / attributes["base"]) - prompty = Prompty.hoist_base_prompty(prompty, base) - - return prompty - - -@trace(description="Prepare the inputs for the prompt.") -def prepare( - prompt: Prompty, - inputs: Dict[str, Any] = {}, -): - """Prepare the inputs for the prompt. - - Parameters - ---------- - prompt : Prompty - The prompty object - inputs : Dict[str, Any], optional - The inputs to the prompt, by default {} - - Returns - ------- - dict - The prepared and hidrated template shaped to the LLM model - - Example - ------- - >>> import prompty - >>> p = prompty.load("prompts/basic.prompty") - >>> inputs = {"name": "John Doe"} - >>> content = prompty.prepare(p, inputs) - """ - inputs = param_hoisting(inputs, prompt.sample) - - render = InvokerFactory.run_renderer(prompt, inputs, prompt.content) - result = InvokerFactory.run_parser(prompt, render) - - return result - - -@trace(description="Prepare the inputs for the prompt.") -async def prepare_async( - prompt: Prompty, - inputs: Dict[str, Any] = {}, -): - """Prepare the inputs for the prompt. - - Parameters - ---------- - prompt : Prompty - The prompty object - inputs : Dict[str, Any], optional - The inputs to the prompt, by default {} - - Returns - ------- - dict - The prepared and hidrated template shaped to the LLM model - - Example - ------- - >>> import prompty - >>> p = prompty.load("prompts/basic.prompty") - >>> inputs = {"name": "John Doe"} - >>> content = await prompty.prepare_async(p, inputs) - """ - inputs = param_hoisting(inputs, prompt.sample) - - render = await InvokerFactory.run_renderer_async(prompt, inputs, prompt.content) - result = await InvokerFactory.run_parser_async(prompt, render) - - return result - - -@trace(description="Run the prepared Prompty content against the model.") -def run( - prompt: Prompty, - content: Union[dict, list, str], - configuration: Dict[str, Any] = {}, - parameters: Dict[str, Any] = {}, - raw: bool = False, -): - """Run the prepared Prompty content. - - Parameters - ---------- - prompt : Prompty - The prompty object - content : Union[dict, list, str] - The content to process - configuration : Dict[str, Any], optional - The configuration to use, by default {} - parameters : Dict[str, Any], optional - The parameters to use, by default {} - raw : bool, optional - Whether to skip processing, by default False - - Returns - ------- - Any - The result of the prompt - - Example - ------- - >>> import prompty - >>> p = prompty.load("prompts/basic.prompty") - >>> inputs = {"name": "John Doe"} - >>> content = prompty.prepare(p, inputs) - >>> result = prompty.run(p, content) - """ - - if configuration != {}: - prompt.model.configuration = param_hoisting(configuration, prompt.model.configuration) - - if parameters != {}: - prompt.model.parameters = param_hoisting(parameters, prompt.model.parameters) - - result = InvokerFactory.run_executor(prompt, content) - if not raw: - result = InvokerFactory.run_processor(prompt, result) - - return result - - -@trace(description="Run the prepared Prompty content against the model.") -async def run_async( - prompt: Prompty, - content: Union[dict, list, str], - configuration: Dict[str, Any] = {}, - parameters: Dict[str, Any] = {}, - raw: bool = False, -): - """Run the prepared Prompty content. - - Parameters - ---------- - prompt : Prompty - The prompty object - content : Union[dict, list, str] - The content to process - configuration : Dict[str, Any], optional - The configuration to use, by default {} - parameters : Dict[str, Any], optional - The parameters to use, by default {} - raw : bool, optional - Whether to skip processing, by default False - - Returns - ------- - Any - The result of the prompt - - Example - ------- - >>> import prompty - >>> p = prompty.load("prompts/basic.prompty") - >>> inputs = {"name": "John Doe"} - >>> content = await prompty.prepare_async(p, inputs) - >>> result = await prompty.run_async(p, content) - """ - - if configuration != {}: - prompt.model.configuration = param_hoisting(configuration, prompt.model.configuration) - - if parameters != {}: - prompt.model.parameters = param_hoisting(parameters, prompt.model.parameters) - - result = await InvokerFactory.run_executor_async(prompt, content) - if not raw: - result = await InvokerFactory.run_processor_async(prompt, result) - - return result - - -@trace(description="Execute a prompty") -def execute( - prompt: Union[str, Prompty], - configuration: Dict[str, Any] = {}, - parameters: Dict[str, Any] = {}, - inputs: Dict[str, Any] = {}, - raw: bool = False, - config_name: str = "default", -): - """Execute a prompty. - - Parameters - ---------- - prompt : Union[str, Prompty] - The prompty object or path to the prompty file - configuration : Dict[str, Any], optional - The configuration to use, by default {} - parameters : Dict[str, Any], optional - The parameters to use, by default {} - inputs : Dict[str, Any], optional - The inputs to the prompt, by default {} - raw : bool, optional - Whether to skip processing, by default False - connection : str, optional - The connection to use, by default "default" - - Returns - ------- - Any - The result of the prompt - - Example - ------- - >>> import prompty - >>> inputs = {"name": "John Doe"} - >>> result = prompty.execute("prompts/basic.prompty", inputs=inputs) - """ - if isinstance(prompt, str): - path = Path(prompt) - if not path.is_absolute(): - # get caller's path (take into account trace frame) - caller = Path(traceback.extract_stack()[-3].filename) - path = Path(caller.parent / path).resolve().absolute() - prompt = load(path, config_name) - - # prepare content - content = prepare(prompt, inputs) - - # run LLM model - result = run(prompt, content, configuration, parameters, raw) - - return result diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_renderers.py b/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_renderers.py deleted file mode 100644 index 0d682a7fe151..000000000000 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_renderers.py +++ /dev/null @@ -1,30 +0,0 @@ -# ------------------------------------ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. -# ------------------------------------ -# mypy: disable-error-code="union-attr,assignment,arg-type" -from pathlib import Path -from ._core import Prompty -from ._invoker import Invoker, InvokerFactory -from ._mustache import render - - -@InvokerFactory.register_renderer("mustache") -class MustacheRenderer(Invoker): - """Render a mustache template.""" - - def __init__(self, prompty: Prompty) -> None: - super().__init__(prompty) - self.templates = {} - cur_prompt = self.prompty - while cur_prompt: - self.templates[Path(cur_prompt.file).name] = cur_prompt.content - cur_prompt = cur_prompt.basePrompty - self.name = Path(self.prompty.file).name - - def invoke(self, data: str) -> str: - generated = render(self.prompty.content, data) # type: ignore - return generated - - async def invoke_async(self, data: str) -> str: - return self.invoke(data) diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_tracer.py b/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_tracer.py deleted file mode 100644 index 24f800b465f4..000000000000 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_tracer.py +++ /dev/null @@ -1,316 +0,0 @@ -# ------------------------------------ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. -# ------------------------------------ -# mypy: disable-error-code="union-attr,arg-type,misc,return-value,assignment,func-returns-value" -# pylint: disable=R,redefined-outer-name,bare-except,unspecified-encoding -import os -import json -import inspect -import traceback -import importlib -import contextlib -from pathlib import Path -from numbers import Number -from datetime import datetime -from functools import wraps, partial -from typing import Any, Callable, Dict, Iterator, List, Union - - -# clean up key value pairs for sensitive values -def sanitize(key: str, value: Any) -> Any: - if isinstance(value, str) and any([s in key.lower() for s in ["key", "token", "secret", "password", "credential"]]): - return len(str(value)) * "*" - - if isinstance(value, dict): - return {k: sanitize(k, v) for k, v in value.items()} - - return value - - -class Tracer: - _tracers: Dict[str, Callable[[str], Iterator[Callable[[str, Any], None]]]] = {} - - @classmethod - def add(cls, name: str, tracer: Callable[[str], Iterator[Callable[[str, Any], None]]]) -> None: - cls._tracers[name] = tracer - - @classmethod - def clear(cls) -> None: - cls._tracers = {} - - @classmethod - @contextlib.contextmanager - def start(cls, name: str) -> Iterator[Callable[[str, Any], None]]: - with contextlib.ExitStack() as stack: - traces: List[Any] = [stack.enter_context(tracer(name)) for tracer in cls._tracers.values()] # type: ignore - yield lambda key, value: [ # type: ignore - # normalize and sanitize any trace values - trace(key, sanitize(key, to_dict(value))) - for trace in traces - ] - - -def to_dict(obj: Any) -> Union[Dict[str, Any], List[Dict[str, Any]], str, Number, bool]: - # simple json types - if isinstance(obj, str) or isinstance(obj, Number) or isinstance(obj, bool): - return obj - - # datetime - if isinstance(obj, datetime): - return obj.isoformat() - - # safe Prompty obj serialization - if type(obj).__name__ == "Prompty": - return obj.to_safe_dict() - - # safe PromptyStream obj serialization - if type(obj).__name__ == "PromptyStream": - return "PromptyStream" - - if type(obj).__name__ == "AsyncPromptyStream": - return "AsyncPromptyStream" - - # recursive list and dict - if isinstance(obj, List): - return [to_dict(item) for item in obj] # type: ignore - - if isinstance(obj, Dict): - return {k: v if isinstance(v, str) else to_dict(v) for k, v in obj.items()} - - if isinstance(obj, Path): - return str(obj) - - # cast to string otherwise... - return str(obj) - - -def _name(func: Callable, args): - if hasattr(func, "__qualname__"): - signature = f"{func.__module__}.{func.__qualname__}" - else: - signature = f"{func.__module__}.{func.__name__}" - - # core invoker gets special treatment prompty.invoker.Invoker - core_invoker = signature.startswith("prompty.invoker.Invoker.run") - if core_invoker: - name = type(args[0]).__name__ - if signature.endswith("async"): - signature = f"{args[0].__module__}.{args[0].__class__.__name__}.invoke_async" - else: - signature = f"{args[0].__module__}.{args[0].__class__.__name__}.invoke" - else: - name = func.__name__ - - return name, signature - - -def _inputs(func: Callable, args, kwargs) -> dict: - ba = inspect.signature(func).bind(*args, **kwargs) - ba.apply_defaults() - - inputs = {k: to_dict(v) for k, v in ba.arguments.items() if k != "self"} - - return inputs - - -def _results(result: Any) -> Union[Dict, List[Dict], str, Number, bool]: - return to_dict(result) if result is not None else "None" - - -def _trace_sync(func: Union[Callable, None] = None, **okwargs: Any) -> Callable: - - @wraps(func) # type: ignore - def wrapper(*args, **kwargs): - name, signature = _name(func, args) # type: ignore - with Tracer.start(name) as trace: - trace("signature", signature) - - # support arbitrary keyword - # arguments for trace decorator - for k, v in okwargs.items(): - trace(k, to_dict(v)) - - inputs = _inputs(func, args, kwargs) # type: ignore - trace("inputs", inputs) - - try: - result = func(*args, **kwargs) # type: ignore - trace("result", _results(result)) - except Exception as e: - trace( - "result", - { - "exception": { - "type": type(e), - "traceback": (traceback.format_tb(tb=e.__traceback__) if e.__traceback__ else None), - "message": str(e), - "args": to_dict(e.args), - } - }, - ) - raise e - - return result - - return wrapper - - -def _trace_async(func: Union[Callable, None] = None, **okwargs: Any) -> Callable: - - @wraps(func) # type: ignore - async def wrapper(*args, **kwargs): - name, signature = _name(func, args) # type: ignore - with Tracer.start(name) as trace: - trace("signature", signature) - - # support arbitrary keyword - # arguments for trace decorator - for k, v in okwargs.items(): - trace(k, to_dict(v)) - - inputs = _inputs(func, args, kwargs) # type: ignore - trace("inputs", inputs) - try: - result = await func(*args, **kwargs) # type: ignore - trace("result", _results(result)) - except Exception as e: - trace( - "result", - { - "exception": { - "type": type(e), - "traceback": (traceback.format_tb(tb=e.__traceback__) if e.__traceback__ else None), - "message": str(e), - "args": to_dict(e.args), - } - }, - ) - raise e - - return result - - return wrapper - - -def trace(func: Union[Callable, None] = None, **kwargs: Any) -> Callable: - if func is None: - return partial(trace, **kwargs) - wrapped_method = _trace_async if inspect.iscoroutinefunction(func) else _trace_sync - return wrapped_method(func, **kwargs) - - -class PromptyTracer: - def __init__(self, output_dir: Union[str, None] = None) -> None: - if output_dir: - self.output = Path(output_dir).resolve().absolute() - else: - self.output = Path(Path(os.getcwd()) / ".runs").resolve().absolute() - - if not self.output.exists(): - self.output.mkdir(parents=True, exist_ok=True) - - self.stack: List[Dict[str, Any]] = [] - - @contextlib.contextmanager - def tracer(self, name: str) -> Iterator[Callable[[str, Any], None]]: - try: - self.stack.append({"name": name}) - frame = self.stack[-1] - frame["__time"] = { - "start": datetime.now(), - } - - def add(key: str, value: Any) -> None: - if key not in frame: - frame[key] = value - # multiple values creates list - else: - if isinstance(frame[key], list): - frame[key].append(value) - else: - frame[key] = [frame[key], value] - - yield add - finally: - frame = self.stack.pop() - start: datetime = frame["__time"]["start"] - end: datetime = datetime.now() - - # add duration to frame - frame["__time"] = { - "start": start.strftime("%Y-%m-%dT%H:%M:%S.%f"), - "end": end.strftime("%Y-%m-%dT%H:%M:%S.%f"), - "duration": int((end - start).total_seconds() * 1000), - } - - # hoist usage to parent frame - if "result" in frame and isinstance(frame["result"], dict): - if "usage" in frame["result"]: - frame["__usage"] = self.hoist_item( - frame["result"]["usage"], - frame["__usage"] if "__usage" in frame else {}, - ) - - # streamed results may have usage as well - if "result" in frame and isinstance(frame["result"], list): - for result in frame["result"]: - if isinstance(result, dict) and "usage" in result and isinstance(result["usage"], dict): - frame["__usage"] = self.hoist_item( - result["usage"], - frame["__usage"] if "__usage" in frame else {}, - ) - - # add any usage frames from below - if "__frames" in frame: - for child in frame["__frames"]: - if "__usage" in child: - frame["__usage"] = self.hoist_item( - child["__usage"], - frame["__usage"] if "__usage" in frame else {}, - ) - - # if stack is empty, dump the frame - if len(self.stack) == 0: - self.write_trace(frame) - # otherwise, append the frame to the parent - else: - if "__frames" not in self.stack[-1]: - self.stack[-1]["__frames"] = [] - self.stack[-1]["__frames"].append(frame) - - def hoist_item(self, src: Dict[str, Any], cur: Dict[str, Any]) -> Dict[str, Any]: - for key, value in src.items(): - if value is None or isinstance(value, list) or isinstance(value, dict): - continue - try: - if key not in cur: - cur[key] = value - else: - cur[key] += value - except: - continue - - return cur - - def write_trace(self, frame: Dict[str, Any]) -> None: - trace_file = self.output / f"{frame['name']}.{datetime.now().strftime('%Y%m%d.%H%M%S')}.tracy" - - v = importlib.metadata.version("prompty") # type: ignore - enriched_frame = { - "runtime": "python", - "version": v, - "trace": frame, - } - - with open(trace_file, "w") as f: - json.dump(enriched_frame, f, indent=4) - - -@contextlib.contextmanager -def console_tracer(name: str) -> Iterator[Callable[[str, Any], None]]: - try: - print(f"Starting {name}") - yield lambda key, value: print(f"{key}:\n{json.dumps(to_dict(value), indent=4)}") - finally: - print(f"Ending {name}") diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_utils.py b/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_utils.py deleted file mode 100644 index 22f284180ee1..000000000000 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/prompts/_utils.py +++ /dev/null @@ -1,100 +0,0 @@ -# ------------------------------------ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. -# ------------------------------------ -# mypy: disable-error-code="import-untyped,return-value" -# pylint: disable=line-too-long,R,wrong-import-order,global-variable-not-assigned) -import json -import os -import re -import sys -from typing import Any, Dict -from pathlib import Path - - -_yaml_regex = re.compile( - r"^\s*" + r"(?:---|\+\+\+)" + r"(.*?)" + r"(?:---|\+\+\+)" + r"\s*(.+)$", - re.S | re.M, -) - - -def load_text(file_path, encoding="utf-8"): - with open(file_path, "r", encoding=encoding) as file: - return file.read() - - -def load_json(file_path, encoding="utf-8"): - return json.loads(load_text(file_path, encoding=encoding)) - - -def load_global_config(prompty_path: Path = Path.cwd(), configuration: str = "default") -> Dict[str, Any]: - prompty_config_path = prompty_path.joinpath("prompty.json") - if os.path.exists(prompty_config_path): - c = load_json(prompty_config_path) - if configuration in c: - return c[configuration] - else: - raise ValueError(f'Item "{configuration}" not found in "{prompty_config_path}"') - else: - return {} - - -def load_prompty(file_path, encoding="utf-8") -> Dict[str, Any]: - contents = load_text(file_path, encoding=encoding) - return parse(contents) - - -def parse(contents): - try: - import yaml # type: ignore - except ImportError as exc: - raise ImportError("Please install pyyaml to use this function. Run `pip install pyyaml`.") from exc - - global _yaml_regex - - fmatter = "" - body = "" - result = _yaml_regex.search(contents) - - if result: - fmatter = result.group(1) - body = result.group(2) - return { - "attributes": yaml.load(fmatter, Loader=yaml.SafeLoader), - "body": body, - "frontmatter": fmatter, - } - - -def remove_leading_empty_space(multiline_str: str) -> str: - """ - Processes a multiline string by: - 1. Removing empty lines - 2. Finding the minimum leading spaces - 3. Indenting all lines to the minimum level - - :param multiline_str: The input multiline string. - :type multiline_str: str - :return: The processed multiline string. - :rtype: str - """ - lines = multiline_str.splitlines() - start_index = 0 - while start_index < len(lines) and lines[start_index].strip() == "": - start_index += 1 - - # Find the minimum number of leading spaces - min_spaces = sys.maxsize - for line in lines[start_index:]: - if len(line.strip()) == 0: - continue - spaces = len(line) - len(line.lstrip()) - spaces += line.lstrip().count("\t") * 2 # Count tabs as 2 spaces - min_spaces = min(min_spaces, spaces) - - # Remove leading spaces and indent to the minimum level - processed_lines = [] - for line in lines[start_index:]: - processed_lines.append(line[min_spaces:]) - - return "\n".join(processed_lines) diff --git a/sdk/ai/azure-ai-inference/azure/ai/inference/tracing.py b/sdk/ai/azure-ai-inference/azure/ai/inference/tracing.py deleted file mode 100644 index dc3f0ed982e4..000000000000 --- a/sdk/ai/azure-ai-inference/azure/ai/inference/tracing.py +++ /dev/null @@ -1,823 +0,0 @@ -# ------------------------------------ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. -# ------------------------------------ -import copy -from enum import Enum -import functools -import json -import importlib -import logging -import os -from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union -from urllib.parse import urlparse - -# pylint: disable = no-name-in-module -from azure.core import CaseInsensitiveEnumMeta # type: ignore -from azure.core.settings import settings -from . import models as _models - -try: - # pylint: disable = no-name-in-module - from azure.core.tracing import AbstractSpan, SpanKind # type: ignore - from opentelemetry.trace import StatusCode, Span - - _tracing_library_available = True -except ModuleNotFoundError: - - _tracing_library_available = False - - -__all__ = [ - "AIInferenceInstrumentor", -] - - -_inference_traces_enabled: bool = False -_trace_inference_content: bool = False -_INFERENCE_GEN_AI_SYSTEM_NAME = "az.ai.inference" - - -class TraceType(str, Enum, metaclass=CaseInsensitiveEnumMeta): # pylint: disable=C4747 - """An enumeration class to represent different types of traces.""" - - INFERENCE = "Inference" - - -class AIInferenceInstrumentor: - """ - A class for managing the trace instrumentation of AI Inference. - - This class allows enabling or disabling tracing for AI Inference. - 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 = _AIInferenceInstrumentorPreview() - - def instrument(self, enable_content_recording: Optional[bool] = None) -> None: - """ - Enable trace instrumentation for AI Inference. - - :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 - s 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=enable_content_recording) - - def uninstrument(self) -> None: - """ - Disable trace instrumentation for AI Inference. - - Raises: - RuntimeError: If instrumentation is not currently enabled. - - This method removes any active instrumentation, stopping the tracing - of AI Inference. - """ - self._impl.uninstrument() - - def is_instrumented(self) -> bool: - """ - Check if trace instrumentation for AI Inference 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 _AIInferenceInstrumentorPreview: - """ - A class for managing the trace instrumentation of AI Inference. - - This class allows enabling or disabling tracing for AI Inference. - 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 Inference. - - :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_inference(enable_content_recording) - else: - self._set_content_recording_enabled(enable_content_recording=enable_content_recording) - - def uninstrument(self): - """ - Disable trace instrumentation for AI Inference. - - This method removes any active instrumentation, stopping the tracing - of AI Inference. - """ - if self.is_instrumented(): - self._uninstrument_inference() - - def is_instrumented(self): - """ - Check if trace instrumentation for AI Inference is currently enabled. - - :return: True if instrumentation is active, False otherwise. - :rtype: bool - """ - return self._is_instrumented() - - def set_content_recording_enabled(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_content_recording_enabled(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 _add_request_chat_message_event(self, span: "AbstractSpan", **kwargs: Any) -> None: - for message in kwargs.get("messages", []): - try: - message = message.as_dict() - except AttributeError: - pass - - if message.get("role"): - name = f"gen_ai.{message.get('role')}.message" - span.span_instance.add_event( - name=name, - attributes={ - "gen_ai.system": _INFERENCE_GEN_AI_SYSTEM_NAME, - "gen_ai.event.content": json.dumps(message), - }, - ) - - def _parse_url(self, url): - parsed = urlparse(url) - server_address = parsed.hostname - port = parsed.port - return server_address, port - - def _add_request_chat_attributes(self, span: "AbstractSpan", *args: Any, **kwargs: Any) -> None: - client = args[0] - endpoint = client._config.endpoint # pylint: disable=protected-access - server_address, port = self._parse_url(endpoint) - model = "chat" - if kwargs.get("model") is not None: - model_value = kwargs.get("model") - if model_value is not None: - model = model_value - - self._set_attributes( - span, - ("gen_ai.operation.name", "chat"), - ("gen_ai.system", _INFERENCE_GEN_AI_SYSTEM_NAME), - ("gen_ai.request.model", model), - ("gen_ai.request.max_tokens", kwargs.get("max_tokens")), - ("gen_ai.request.temperature", kwargs.get("temperature")), - ("gen_ai.request.top_p", kwargs.get("top_p")), - ("server.address", server_address), - ) - if port is not None and port != 443: - span.add_attribute("server.port", 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 _get_finish_reasons(self, result) -> Optional[List[str]]: - if hasattr(result, "choices") and result.choices: - finish_reasons: List[str] = [] - for choice in result.choices: - finish_reason = getattr(choice, "finish_reason", None) - - if finish_reason is None: - # If finish_reason is None, default to "none" - finish_reasons.append("none") - elif hasattr(finish_reason, "value"): - # If finish_reason has a 'value' attribute (i.e., it's an enum), use it - finish_reasons.append(finish_reason.value) - elif isinstance(finish_reason, str): - # If finish_reason is a string, use it directly - finish_reasons.append(finish_reason) - else: - # Default to "none" - finish_reasons.append("none") - - return finish_reasons - return None - - def _get_finish_reason_for_choice(self, choice): - finish_reason = getattr(choice, "finish_reason", None) - if finish_reason is not None: - return finish_reason.value - - return "none" - - def _add_response_chat_message_event(self, span: "AbstractSpan", result: _models.ChatCompletions) -> None: - for choice in result.choices: - if _trace_inference_content: - full_response: Dict[str, Any] = { - "message": {"content": choice.message.content}, - "finish_reason": self._get_finish_reason_for_choice(choice), - "index": choice.index, - } - if choice.message.tool_calls: - full_response["message"]["tool_calls"] = [tool.as_dict() for tool in choice.message.tool_calls] - attributes = { - "gen_ai.system": _INFERENCE_GEN_AI_SYSTEM_NAME, - "gen_ai.event.content": json.dumps(full_response), - } - else: - response: Dict[str, Any] = { - "finish_reason": self._get_finish_reason_for_choice(choice), - "index": choice.index, - } - if choice.message.tool_calls: - response["message"] = {} - tool_calls_function_names_and_arguments_removed = self._remove_function_call_names_and_arguments( - choice.message.tool_calls - ) - response["message"]["tool_calls"] = [ - tool.as_dict() for tool in tool_calls_function_names_and_arguments_removed - ] - - attributes = { - "gen_ai.system": _INFERENCE_GEN_AI_SYSTEM_NAME, - "gen_ai.event.content": json.dumps(response), - } - span.span_instance.add_event(name="gen_ai.choice", attributes=attributes) - - def _add_response_chat_attributes( - self, - span: "AbstractSpan", - result: Union[_models.ChatCompletions, _models.StreamingChatCompletionsUpdate], - ) -> None: - self._set_attributes( - span, - ("gen_ai.response.id", result.id), - ("gen_ai.response.model", result.model), - ( - "gen_ai.usage.input_tokens", - (result.usage.prompt_tokens if hasattr(result, "usage") and result.usage else None), - ), - ( - "gen_ai.usage.output_tokens", - (result.usage.completion_tokens if hasattr(result, "usage") and result.usage else None), - ), - ) - finish_reasons = self._get_finish_reasons(result) - if not finish_reasons is None: - span.add_attribute("gen_ai.response.finish_reasons", finish_reasons) # type: ignore - - def _add_request_span_attributes(self, span: "AbstractSpan", _span_name: str, args: Any, kwargs: Any) -> None: - self._add_request_chat_attributes(span, *args, **kwargs) - if _trace_inference_content: - self._add_request_chat_message_event(span, **kwargs) - - def _add_response_span_attributes(self, span: "AbstractSpan", result: object) -> None: - if isinstance(result, _models.ChatCompletions): - self._add_response_chat_attributes(span, result) - self._add_response_chat_message_event(span, result) - # TODO add more models here - - def _accumulate_response(self, item, accumulate: Dict[str, Any]) -> None: - if item.finish_reason: - accumulate["finish_reason"] = item.finish_reason - if item.index: - accumulate["index"] = item.index - if item.delta.content: - accumulate.setdefault("message", {}) - accumulate["message"].setdefault("content", "") - accumulate["message"]["content"] += item.delta.content - if item.delta.tool_calls: - accumulate.setdefault("message", {}) - accumulate["message"].setdefault("tool_calls", []) - if item.delta.tool_calls is not None: - for tool_call in item.delta.tool_calls: - if tool_call.id: - accumulate["message"]["tool_calls"].append( - { - "id": tool_call.id, - "type": "", - "function": {"name": "", "arguments": ""}, - } - ) - if tool_call.function: - accumulate["message"]["tool_calls"][-1]["type"] = "function" - if tool_call.function and tool_call.function.name: - accumulate["message"]["tool_calls"][-1]["function"]["name"] = tool_call.function.name - if tool_call.function and tool_call.function.arguments: - accumulate["message"]["tool_calls"][-1]["function"]["arguments"] += tool_call.function.arguments - - def _accumulate_async_streaming_response(self, item, accumulate: Dict[str, Any]) -> None: - if not "choices" in item: - return - if "finish_reason" in item["choices"][0] and item["choices"][0]["finish_reason"]: - accumulate["finish_reason"] = item["choices"][0]["finish_reason"] - if "index" in item["choices"][0] and item["choices"][0]["index"]: - accumulate["index"] = item["choices"][0]["index"] - if not "delta" in item["choices"][0]: - return - if "content" in item["choices"][0]["delta"] and item["choices"][0]["delta"]["content"]: - accumulate.setdefault("message", {}) - accumulate["message"].setdefault("content", "") - accumulate["message"]["content"] += item["choices"][0]["delta"]["content"] - if "tool_calls" in item["choices"][0]["delta"] and item["choices"][0]["delta"]["tool_calls"]: - accumulate.setdefault("message", {}) - accumulate["message"].setdefault("tool_calls", []) - if item["choices"][0]["delta"]["tool_calls"] is not None: - for tool_call in item["choices"][0]["delta"]["tool_calls"]: - if tool_call.id: - accumulate["message"]["tool_calls"].append( - { - "id": tool_call.id, - "type": "", - "function": {"name": "", "arguments": ""}, - } - ) - if tool_call.function: - accumulate["message"]["tool_calls"][-1]["type"] = "function" - if tool_call.function and tool_call.function.name: - accumulate["message"]["tool_calls"][-1]["function"]["name"] = tool_call.function.name - if tool_call.function and tool_call.function.arguments: - accumulate["message"]["tool_calls"][-1]["function"]["arguments"] += tool_call.function.arguments - - def _wrapped_stream( - self, stream_obj: _models.StreamingChatCompletions, span: "AbstractSpan" - ) -> _models.StreamingChatCompletions: - class StreamWrapper(_models.StreamingChatCompletions): - def __init__(self, stream_obj, instrumentor): - super().__init__(stream_obj._response) - self._instrumentor = instrumentor - - def __iter__( # pyright: ignore [reportIncompatibleMethodOverride] - self, - ) -> Iterator[_models.StreamingChatCompletionsUpdate]: - accumulate: Dict[str, Any] = {} - try: - chunk = None - for chunk in stream_obj: - for item in chunk.choices: - self._instrumentor._accumulate_response(item, accumulate) - yield chunk - - if chunk is not None: - self._instrumentor._add_response_chat_attributes(span, chunk) - - 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 = exc.__module__ if hasattr(exc, "__module__") and exc.__module__ != "builtins" else "" - error_type = f"{module}.{type(exc).__name__}" if module else type(exc).__name__ - self._instrumentor._set_attributes(span, ("error.type", error_type)) - raise - - finally: - if stream_obj._done is False: - if accumulate.get("finish_reason") is None: - accumulate["finish_reason"] = "error" - else: - # Only one choice expected with streaming - accumulate["index"] = 0 - # Delete message if content tracing is not enabled - if not _trace_inference_content: - if "message" in accumulate: - if "content" in accumulate["message"]: - del accumulate["message"]["content"] - if not accumulate["message"]: - del accumulate["message"] - if "message" in accumulate: - if "tool_calls" in accumulate["message"]: - tool_calls_function_names_and_arguments_removed = ( - self._instrumentor._remove_function_call_names_and_arguments( - accumulate["message"]["tool_calls"] - ) - ) - accumulate["message"]["tool_calls"] = list( - tool_calls_function_names_and_arguments_removed - ) - - span.span_instance.add_event( - name="gen_ai.choice", - attributes={ - "gen_ai.system": _INFERENCE_GEN_AI_SYSTEM_NAME, - "gen_ai.event.content": json.dumps(accumulate), - }, - ) - span.finish() - - return StreamWrapper(stream_obj, self) - - def _async_wrapped_stream( - self, stream_obj: _models.AsyncStreamingChatCompletions, span: "AbstractSpan" - ) -> _models.AsyncStreamingChatCompletions: - class AsyncStreamWrapper(_models.AsyncStreamingChatCompletions): - def __init__(self, stream_obj, instrumentor, span): - super().__init__(stream_obj._response) - self._instrumentor = instrumentor - self._accumulate: Dict[str, Any] = {} - self._stream_obj = stream_obj - self.span = span - self._last_result = None - - async def __anext__(self) -> "_models.StreamingChatCompletionsUpdate": - try: - result = await super().__anext__() - self._instrumentor._accumulate_async_streaming_response( # pylint: disable=protected-access, line-too-long # pyright: ignore [reportFunctionMemberAccess] - result, self._accumulate - ) - self._last_result = result - except StopAsyncIteration as exc: - self._trace_stream_content() - raise exc - return result - - def _trace_stream_content(self) -> None: - if self._last_result: - self._instrumentor._add_response_chat_attributes( # pylint: disable=protected-access, line-too-long # pyright: ignore [reportFunctionMemberAccess] - span, self._last_result - ) - # Only one choice expected with streaming - self._accumulate["index"] = 0 - # Delete message if content tracing is not enabled - if not _trace_inference_content: - if "message" in self._accumulate: - if "content" in self._accumulate["message"]: - del self._accumulate["message"]["content"] - if not self._accumulate["message"]: - del self._accumulate["message"] - if "message" in self._accumulate: - if "tool_calls" in self._accumulate["message"]: - tools_no_recording = self._instrumentor._remove_function_call_names_and_arguments( # pylint: disable=protected-access, line-too-long # pyright: ignore [reportFunctionMemberAccess] - self._accumulate["message"]["tool_calls"] - ) - self._accumulate["message"]["tool_calls"] = list(tools_no_recording) - - self.span.span_instance.add_event( - name="gen_ai.choice", - attributes={ - "gen_ai.system": _INFERENCE_GEN_AI_SYSTEM_NAME, - "gen_ai.event.content": json.dumps(self._accumulate), - }, - ) - span.finish() - - async_stream_wrapper = AsyncStreamWrapper(stream_obj, self, span) - return async_stream_wrapper - - def _trace_sync_function( - self, - function: Callable, - *, - _args_to_ignore: Optional[List[str]] = None, - _trace_type=TraceType.INFERENCE, - _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.INFERENCE. - :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): - - span_impl_type = settings.tracing_implementation() - if span_impl_type is None: - return function(*args, **kwargs) - - class_function_name = function.__qualname__ - - if class_function_name.startswith("ChatCompletionsClient.complete"): - if kwargs.get("model") is None: - span_name = "chat" - else: - model = kwargs.get("model") - span_name = f"chat {model}" - - span = span_impl_type( - name=span_name, - kind=SpanKind.CLIENT, # pyright: ignore [reportPossiblyUnboundVariable] - ) - try: - # tracing events not supported in azure-core-tracing-opentelemetry - # so need to access the span instance directly - with span_impl_type.change_context(span.span_instance): - self._add_request_span_attributes(span, span_name, args, kwargs) - result = function(*args, **kwargs) - if kwargs.get("stream") is True: - return self._wrapped_stream(result, span) - self._add_response_span_attributes(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)) - span.finish() - raise - - span.finish() - return result - - # 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.INFERENCE, - _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.INFERENCE. - :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): - span_impl_type = settings.tracing_implementation() - if span_impl_type is None: - return await function(*args, **kwargs) - - class_function_name = function.__qualname__ - - if class_function_name.startswith("ChatCompletionsClient.complete"): - if kwargs.get("model") is None: - span_name = "chat" - else: - model = kwargs.get("model") - span_name = f"chat {model}" - - span = span_impl_type( - name=span_name, - kind=SpanKind.CLIENT, # pyright: ignore [reportPossiblyUnboundVariable] - ) - try: - # tracing events not supported in azure-core-tracing-opentelemetry - # so need to access the span instance directly - with span_impl_type.change_context(span.span_instance): - self._add_request_span_attributes(span, span_name, args, kwargs) - result = await function(*args, **kwargs) - if kwargs.get("stream") is True: - return self._async_wrapped_stream(result, span) - self._add_response_span_attributes(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)) - span.finish() - raise - - span.finish() - return result - - # 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 _inference_apis(self): - sync_apis = ( - ( - "azure.ai.inference", - "ChatCompletionsClient", - "complete", - TraceType.INFERENCE, - "inference_chat_completions_complete", - ), - ) - async_apis = ( - ( - "azure.ai.inference.aio", - "ChatCompletionsClient", - "complete", - TraceType.INFERENCE, - "inference_chat_completions_complete", - ), - ) - return sync_apis, async_apis - - def _inference_api_list(self): - sync_apis, async_apis = self._inference_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_inference_apis_and_injectors(self): - """ - Generates a sequence of tuples containing Inference API classes, method names, and - corresponding injector functions. - - :return: A generator yielding tuples. - :rtype: tuple - """ - yield from self._generate_api_and_injector(self._inference_api_list()) - - def _instrument_inference(self, enable_content_tracing: bool = False): - """This function modifies the methods of the Inference 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 _inference_traces_enabled - global _trace_inference_content - if _inference_traces_enabled: - raise RuntimeError("Traces already started for azure.ai.inference") - _inference_traces_enabled = True - _trace_inference_content = enable_content_tracing - for ( - api, - method, - trace_type, - injector, - name, - ) in self._available_inference_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_inference(self): - """This function restores the original methods of the Inference API classes - by assigning them back from the _original attributes of the modified methods. - """ - # pylint: disable=W0603 - global _inference_traces_enabled - global _trace_inference_content - _trace_inference_content = False - for api, method, _, _, _ in self._available_inference_apis_and_injectors(): - if hasattr(getattr(api, method), "_original"): - setattr(api, method, getattr(getattr(api, method), "_original")) - _inference_traces_enabled = False - - def _is_instrumented(self): - """This function returns True if Inference libary has already been instrumented - for tracing and False if it has not been instrumented. - - :return: A value indicating whether the Inference library is currently instrumented or not. - :rtype: bool - """ - return _inference_traces_enabled - - def _set_content_recording_enabled(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_inference_content # pylint: disable=W0603 - _trace_inference_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_inference_content diff --git a/sdk/ai/azure-ai-inference/sdk_packaging.toml b/sdk/ai/azure-ai-inference/sdk_packaging.toml new file mode 100644 index 000000000000..e7687fdae93b --- /dev/null +++ b/sdk/ai/azure-ai-inference/sdk_packaging.toml @@ -0,0 +1,2 @@ +[packaging] +auto_update = false \ No newline at end of file diff --git a/sdk/ai/azure-ai-inference/setup.py b/sdk/ai/azure-ai-inference/setup.py index 999dd87812fa..c7b5395a3f9f 100644 --- a/sdk/ai/azure-ai-inference/setup.py +++ b/sdk/ai/azure-ai-inference/setup.py @@ -13,7 +13,7 @@ PACKAGE_NAME = "azure-ai-inference" -PACKAGE_PPRINT_NAME = "Azure AI Inference" +PACKAGE_PPRINT_NAME = "Azure Ai Inference" # a-b-c => a/b/c package_folder_path = PACKAGE_NAME.replace("-", "/") @@ -35,7 +35,7 @@ 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-inference", + url="https://github.com/Azure/azure-sdk-for-python/tree/main/sdk", keywords="azure, azure sdk", classifiers=[ "Development Status :: 4 - Beta", @@ -62,10 +62,10 @@ package_data={ "azure.ai.inference": ["py.typed"], }, - install_requires=["isodate>=0.6.1", "azure-core>=1.30.0", "typing-extensions>=4.6.0"], + install_requires=[ + "isodate>=0.6.1", + "azure-core>=1.30.0", + "typing-extensions>=4.6.0", + ], python_requires=">=3.8", - extras_require={ - "opentelemetry": ["azure-core-tracing-opentelemetry"], - "prompts": ["pyyaml"], - }, ) diff --git a/sdk/ai/azure-ai-inference/tests/test_model_inference_client.py b/sdk/ai/azure-ai-inference/tests/test_model_inference_client.py index 5ea57b1e2935..036e1052b59d 100644 --- a/sdk/ai/azure-ai-inference/tests/test_model_inference_client.py +++ b/sdk/ai/azure-ai-inference/tests/test_model_inference_client.py @@ -1,3 +1,4 @@ +# pylint: disable=too-many-lines # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. diff --git a/sdk/ai/azure-ai-inference/tsp-location.yaml b/sdk/ai/azure-ai-inference/tsp-location.yaml index df185250688b..b22815804d6d 100644 --- a/sdk/ai/azure-ai-inference/tsp-location.yaml +++ b/sdk/ai/azure-ai-inference/tsp-location.yaml @@ -1,4 +1,4 @@ directory: specification/ai/ModelClient -commit: 3e95e575e537024a02470cf59c7a78078dc10cd1 +commit: c45b367efe1b10e82c6ac75fed19e0a91f9e3a8a repo: Azure/azure-rest-api-specs -additionalDirectories: +additionalDirectories: