two version of R2R are hereHEADmaster

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diff --git a/.venv/lib/python3.12/site-packages/azure/ai/inference/_patch.py b/.venv/lib/python3.12/site-packages/azure/ai/inference/_patch.py
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+# pylint: disable=too-many-lines
+# ------------------------------------
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ------------------------------------
+"""Customize generated code here.
+
+Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize
+
+Why do we patch auto-generated code? Below is a summary of the changes made in all _patch files (not just this one):
+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)
+9. Simplify how chat completions "response_format" is set. Define "response_format" as a flat Union of strings and
+   JsonSchemaFormat object, instead of using auto-generated base/derived classes named
+   ChatCompletionsResponseFormatXxxInternal.
+10. Allow UserMessage("my message") in addition to UserMessage(content="my message"). Same applies to 
+AssistantMessage, SystemMessage, DeveloperMessage and ToolMessage.
+
+"""
+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 _get_internal_response_format(
+    response_format: Optional[Union[Literal["text", "json_object"], _models.JsonSchemaFormat]]
+) -> Optional[_models._models.ChatCompletionsResponseFormat]:
+    """
+    Internal helper method to convert between the public response format type that's supported in the `complete` method,
+    and the internal response format type that's used in the generated code.
+
+    :param response_format: Response format. Required.
+    :type response_format: Optional[Union[Literal["text", "json_object"], _models.JsonSchemaFormat]]
+    :return: Internal response format.
+    :rtype: ~azure.ai.inference._models._models.ChatCompletionsResponseFormat
+    """
+    if response_format is not None:
+
+        # To make mypy tool happy, start by declaring the type as the base class
+        internal_response_format: _models._models.ChatCompletionsResponseFormat
+
+        if isinstance(response_format, str) and response_format == "text":
+            internal_response_format = (
+                _models._models.ChatCompletionsResponseFormatText()  # pylint: disable=protected-access
+            )
+        elif isinstance(response_format, str) and response_format == "json_object":
+            internal_response_format = (
+                _models._models.ChatCompletionsResponseFormatJsonObject()  # pylint: disable=protected-access
+            )
+        elif isinstance(response_format, _models.JsonSchemaFormat):
+            internal_response_format = (
+                _models._models.ChatCompletionsResponseFormatJsonSchema(  # pylint: disable=protected-access
+                    json_schema=response_format
+                )
+            )
+        else:
+            raise ValueError(f"Unsupported `response_format` {response_format}")
+
+        return internal_response_format
+
+    return None
+
+
+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.
+    Keyword arguments are passed to the appropriate client's constructor, so if you need to set things like
+    `api_version`, `logging_enable`, `user_agent`, etc., you can do so here.
+    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.
+    Keyword arguments are passed through to the client constructor (you can set keywords such as
+    `api_version`, `user_agent`, `logging_enable` etc. on the client constructor).
+
+    :param endpoint: Service endpoint URL for AI model inference. 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
+    :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.
+        try:
+            model_info = client.get_model_info()  # type: ignore
+        except ResourceNotFoundError as error:
+            error.message = (
+                "`load_client` function does not work on this endpoint (`/info` route not supported). "
+                "Please construct one of the clients (e.g. `ChatCompletionsClient`) directly."
+            )
+            raise error
+
+    _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_completions",
+        "chat",
+        "completion",
+        "chat-completion",
+        "chat-completions",
+        "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",
+        "text_embedding",
+        "text-embeddings",
+        "text embedding",
+        "text embeddings",
+    ):
+        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 in (
+        _models.ModelType.IMAGE_EMBEDDINGS,
+        "image_embedding",
+        "image-embeddings",
+        "image-embedding",
+        "image embedding",
+        "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 endpoint URL for AI model inference. 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 AI model must output. AI chat completions models typically output
+        unformatted text by default. This is equivalent to setting "text" as the response_format.
+        To output JSON format, without adhering to any schema, set to "json_object".
+        To output JSON format adhering to a provided schema, set this to an object of the class
+        ~azure.ai.inference.models.JsonSchemaFormat. Default value is None.
+    :paramtype response_format: Union[Literal['text', 'json_object'], ~azure.ai.inference.models.JsonSchemaFormat]
+    :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[Union[Literal["text", "json_object"], _models.JsonSchemaFormat]] = 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._internal_response_format = _get_internal_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 <key>"
+        # 2. "api-key: <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[Union[Literal["text", "json_object"], _models.JsonSchemaFormat]] = 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[Union[Literal["text", "json_object"], _models.JsonSchemaFormat]] = 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[Union[Literal["text", "json_object"], _models.JsonSchemaFormat]] = 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 AI model must output. AI chat completions models typically output
+         unformatted text by default. This is equivalent to setting "text" as the response_format.
+         To output JSON format, without adhering to any schema, set to "json_object".
+         To output JSON format adhering to a provided schema, set this to an object of the class
+         ~azure.ai.inference.models.JsonSchemaFormat. Default value is None.
+        :paramtype response_format: Union[Literal['text', 'json_object'], ~azure.ai.inference.models.JsonSchemaFormat]
+        :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[Union[Literal["text", "json_object"], _models.JsonSchemaFormat]] = 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 AI model must output. AI chat completions models typically output
+         unformatted text by default. This is equivalent to setting "text" as the response_format.
+         To output JSON format, without adhering to any schema, set to "json_object".
+         To output JSON format adhering to a provided schema, set this to an object of the class
+         ~azure.ai.inference.models.JsonSchemaFormat. Default value is None.
+        :paramtype response_format: Union[Literal['text', 'json_object'], ~azure.ai.inference.models.JsonSchemaFormat]
+        :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))
+
+        internal_response_format = _get_internal_response_format(response_format)
+
+        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": (
+                    internal_response_format if internal_response_format is not None else self._internal_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:
+            try:
+                self._model_info = self._get_model_info(**kwargs)  # pylint: disable=attribute-defined-outside-init
+            except ResourceNotFoundError as error:
+                error.message = "Model information is not available on this endpoint (`/info` route not supported)."
+                raise error
+
+        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 endpoint URL for AI model inference. 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 <key>"
+        # 2. "api-key: <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:
+            try:
+                self._model_info = self._get_model_info(**kwargs)  # pylint: disable=attribute-defined-outside-init
+            except ResourceNotFoundError as error:
+                error.message = "Model information is not available on this endpoint (`/info` route not supported)."
+                raise error
+
+        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 endpoint URL for AI model inference. 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 <key>"
+        # 2. "api-key: <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.ImageEmbeddingInput],
+        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.ImageEmbeddingInput]
+        :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.ImageEmbeddingInput] = _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.ImageEmbeddingInput]
+        :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:
+            try:
+                self._model_info = self._get_model_info(**kwargs)  # pylint: disable=attribute-defined-outside-init
+            except ResourceNotFoundError as error:
+                error.message = "Model information is not available on this endpoint (`/info` route not supported)."
+                raise error
+
+        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
+
+
+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
+    """