two version of R2R are hereHEADmaster

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diff --git a/.venv/lib/python3.12/site-packages/azure/ai/inference/models/_models.py b/.venv/lib/python3.12/site-packages/azure/ai/inference/models/_models.py
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+# pylint: disable=too-many-lines
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for license information.
+# Code generated by Microsoft (R) Python Code Generator.
+# Changes may cause incorrect behavior and will be lost if the code is regenerated.
+# --------------------------------------------------------------------------
+# pylint: disable=useless-super-delegation
+
+import datetime
+from typing import Any, Dict, List, Literal, Mapping, Optional, TYPE_CHECKING, Union, overload
+
+from .. import _model_base
+from .._model_base import rest_discriminator, rest_field
+from ._enums import ChatRole
+
+if TYPE_CHECKING:
+    from .. import models as _models
+
+
+class ContentItem(_model_base.Model):
+    """An abstract representation of a structured content item within a chat message.
+
+    You probably want to use the sub-classes and not this class directly. Known sub-classes are:
+    ImageContentItem, AudioContentItem, TextContentItem
+
+    :ivar type: The discriminated object type. Required. Default value is None.
+    :vartype type: str
+    """
+
+    __mapping__: Dict[str, _model_base.Model] = {}
+    type: str = rest_discriminator(name="type")
+    """The discriminated object type. Required. Default value is None."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        type: str,
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :param mapping: raw JSON to initialize the model.
+        :type mapping: Mapping[str, Any]
+        """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, **kwargs)
+
+
+class AudioContentItem(ContentItem, discriminator="input_audio"):
+    """A structured chat content item containing an audio content.
+
+    :ivar type: The discriminated object type: always 'input_audio' for this type. Required.
+     Default value is "input_audio".
+    :vartype type: str
+    :ivar input_audio: The details of the input audio. Required.
+    :vartype input_audio: ~azure.ai.inference.models.InputAudio
+    """
+
+    type: Literal["input_audio"] = rest_discriminator(name="type")  # type: ignore
+    """The discriminated object type: always 'input_audio' for this type. Required. Default value is
+     \"input_audio\"."""
+    input_audio: "_models.InputAudio" = rest_field()
+    """The details of the input audio. Required."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        input_audio: "_models.InputAudio",
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :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="input_audio", **kwargs)
+
+
+class ChatChoice(_model_base.Model):
+    """The representation of a single prompt completion as part of an overall chat completions
+    request.
+    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.
+
+
+    :ivar index: The ordered index associated with this chat completions choice. Required.
+    :vartype index: int
+    :ivar finish_reason: The reason that this chat completions choice completed its generated.
+     Required. Known values are: "stop", "length", "content_filter", and "tool_calls".
+    :vartype finish_reason: str or ~azure.ai.inference.models.CompletionsFinishReason
+    :ivar message: The chat message for a given chat completions prompt. Required.
+    :vartype message: ~azure.ai.inference.models.ChatResponseMessage
+    """
+
+    index: int = rest_field()
+    """The ordered index associated with this chat completions choice. Required."""
+    finish_reason: Union[str, "_models.CompletionsFinishReason"] = rest_field()
+    """The reason that this chat completions choice completed its generated. Required. Known values
+     are: \"stop\", \"length\", \"content_filter\", and \"tool_calls\"."""
+    message: "_models.ChatResponseMessage" = rest_field()
+    """The chat message for a given chat completions prompt. Required."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        index: int,
+        finish_reason: Union[str, "_models.CompletionsFinishReason"],
+        message: "_models.ChatResponseMessage",
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :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 ChatCompletions(_model_base.Model):
+    """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 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]
+    :ivar usage: Usage information for tokens processed and generated as part of this completions
+     operation. Required.
+    :vartype usage: ~azure.ai.inference.models.CompletionsUsage
+    """
+
+    id: str = rest_field()
+    """A unique identifier associated with this chat completions response. Required."""
+    created: datetime.datetime = rest_field(format="unix-timestamp")
+    """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."""
+    model: str = rest_field()
+    """The model used for the chat completion. Required."""
+    choices: List["_models.ChatChoice"] = rest_field()
+    """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."""
+    usage: "_models.CompletionsUsage" = rest_field()
+    """Usage information for tokens processed and generated as part of this completions operation.
+     Required."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        id: str,  # pylint: disable=redefined-builtin
+        created: datetime.datetime,
+        model: str,
+        choices: List["_models.ChatChoice"],
+        usage: "_models.CompletionsUsage",
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :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 ChatCompletionsNamedToolChoice(_model_base.Model):
+    """A tool selection of a specific, named function tool that will limit chat completions to using
+    the named function.
+
+    :ivar type: The type of the tool. Currently, only ``function`` is supported. Required. Default
+     value is "function".
+    :vartype type: str
+    :ivar function: The function that should be called. Required.
+    :vartype function: ~azure.ai.inference.models.ChatCompletionsNamedToolChoiceFunction
+    """
+
+    type: Literal["function"] = rest_field()
+    """The type of the tool. Currently, only ``function`` is supported. Required. Default value is
+     \"function\"."""
+    function: "_models.ChatCompletionsNamedToolChoiceFunction" = rest_field()
+    """The function that should be called. Required."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        function: "_models.ChatCompletionsNamedToolChoiceFunction",
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :param mapping: raw JSON to initialize the model.
+        :type mapping: Mapping[str, Any]
+        """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, **kwargs)
+        self.type: Literal["function"] = "function"
+
+
+class ChatCompletionsNamedToolChoiceFunction(_model_base.Model):
+    """A tool selection of a specific, named function tool that will limit chat completions to using
+    the named function.
+
+    :ivar name: The name of the function that should be called. Required.
+    :vartype name: str
+    """
+
+    name: str = rest_field()
+    """The name of the function that should be called. Required."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        name: str,
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :param mapping: raw JSON to initialize the model.
+        :type mapping: Mapping[str, Any]
+        """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, **kwargs)
+
+
+class ChatCompletionsResponseFormat(_model_base.Model):
+    """Represents 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.
+
+    You probably want to use the sub-classes and not this class directly. Known sub-classes are:
+    ChatCompletionsResponseFormatJsonObject, ChatCompletionsResponseFormatJsonSchema,
+    ChatCompletionsResponseFormatText
+
+    :ivar type: The response format type to use for chat completions. Required. Default value is
+     None.
+    :vartype type: str
+    """
+
+    __mapping__: Dict[str, _model_base.Model] = {}
+    type: str = rest_discriminator(name="type")
+    """The response format type to use for chat completions. Required. Default value is None."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        type: str,
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :param mapping: raw JSON to initialize the model.
+        :type mapping: Mapping[str, Any]
+        """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, **kwargs)
+
+
+class 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
+    via a system or user message.
+
+    :ivar type: Response format type: always 'json_object' for this object. Required. Default value
+     is "json_object".
+    :vartype type: str
+    """
+
+    type: Literal["json_object"] = rest_discriminator(name="type")  # type: ignore
+    """Response format type: always 'json_object' for this object. Required. Default value is
+     \"json_object\"."""
+
+    @overload
+    def __init__(
+        self,
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :param mapping: raw JSON to initialize the model.
+        :type mapping: Mapping[str, Any]
+        """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, type="json_object", **kwargs)
+
+
+class ChatCompletionsResponseFormatJsonSchema(ChatCompletionsResponseFormat, discriminator="json_schema"):
+    """A response format for Chat Completions that restricts responses to emitting valid JSON objects,
+    with a
+    JSON schema specified by the caller.
+
+    :ivar type: The type of response format being defined: ``json_schema``. Required. Default value
+     is "json_schema".
+    :vartype type: str
+    :ivar json_schema: The definition of the required JSON schema in the response, and associated
+     metadata. Required.
+    :vartype json_schema: ~azure.ai.inference.models.JsonSchemaFormat
+    """
+
+    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.JsonSchemaFormat" = rest_field()
+    """The definition of the required JSON schema in the response, and associated metadata. Required."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        json_schema: "_models.JsonSchemaFormat",
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :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 ChatCompletionsResponseFormatText(ChatCompletionsResponseFormat, discriminator="text"):
+    """A response format for Chat Completions that emits text responses. This is the default response
+    format.
+
+    :ivar type: Response format type: always 'text' for this object. Required. Default value is
+     "text".
+    :vartype type: str
+    """
+
+    type: Literal["text"] = rest_discriminator(name="type")  # type: ignore
+    """Response format type: always 'text' for this object. Required. Default value is \"text\"."""
+
+    @overload
+    def __init__(
+        self,
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :param mapping: raw JSON to initialize the model.
+        :type mapping: Mapping[str, Any]
+        """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, type="text", **kwargs)
+
+
+class ChatCompletionsToolCall(_model_base.Model):
+    """A function tool call requested by the AI model.
+
+    :ivar id: The ID of the tool call. Required.
+    :vartype id: str
+    :ivar type: The type of tool call. Currently, only ``function`` is supported. Required. Default
+     value is "function".
+    :vartype type: str
+    :ivar function: The details of the function call requested by the AI model. Required.
+    :vartype function: ~azure.ai.inference.models.FunctionCall
+    """
+
+    id: str = rest_field()
+    """The ID of the tool call. Required."""
+    type: Literal["function"] = rest_field()
+    """The type of tool call. Currently, only ``function`` is supported. Required. Default value is
+     \"function\"."""
+    function: "_models.FunctionCall" = rest_field()
+    """The details of the function call requested by the AI model. Required."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        id: str,  # pylint: disable=redefined-builtin
+        function: "_models.FunctionCall",
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :param mapping: raw JSON to initialize the model.
+        :type mapping: Mapping[str, Any]
+        """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, **kwargs)
+        self.type: Literal["function"] = "function"
+
+
+class ChatCompletionsToolDefinition(_model_base.Model):
+    """The definition of a chat completions tool that can call a function.
+
+    :ivar type: The type of the tool. Currently, only ``function`` is supported. Required. Default
+     value is "function".
+    :vartype type: str
+    :ivar function: The function definition details for the function tool. Required.
+    :vartype function: ~azure.ai.inference.models.FunctionDefinition
+    """
+
+    type: Literal["function"] = rest_field()
+    """The type of the tool. Currently, only ``function`` is supported. Required. Default value is
+     \"function\"."""
+    function: "_models.FunctionDefinition" = rest_field()
+    """The function definition details for the function tool. Required."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        function: "_models.FunctionDefinition",
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :param mapping: raw JSON to initialize the model.
+        :type mapping: Mapping[str, Any]
+        """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, **kwargs)
+        self.type: Literal["function"] = "function"
+
+
+class ChatRequestMessage(_model_base.Model):
+    """An abstract representation of a chat message as provided in a request.
+
+    You probably want to use the sub-classes and not this class directly. Known sub-classes are:
+    ChatRequestAssistantMessage, ChatRequestDeveloperMessage, ChatRequestSystemMessage,
+    ChatRequestToolMessage, ChatRequestUserMessage
+
+    :ivar role: The chat role associated with this message. Required. Known values are: "system",
+     "user", "assistant", "tool", and "developer".
+    :vartype role: str or ~azure.ai.inference.models.ChatRole
+    """
+
+    __mapping__: Dict[str, _model_base.Model] = {}
+    role: str = rest_discriminator(name="role")
+    """The chat role associated with this message. Required. Known values are: \"system\", \"user\",
+     \"assistant\", \"tool\", and \"developer\"."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        role: str,
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :param mapping: raw JSON to initialize the model.
+        :type mapping: Mapping[str, Any]
+        """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, **kwargs)
+
+
+class ChatRequestAssistantMessage(ChatRequestMessage, discriminator="assistant"):
+    """A request chat message representing response or action from the assistant.
+
+    :ivar role: The chat role associated with this message, which is always 'assistant' for
+     assistant messages. Required. The role that provides responses to system-instructed,
+     user-prompted input.
+    :vartype role: str or ~azure.ai.inference.models.ASSISTANT
+    :ivar content: The content of the message.
+    :vartype content: str
+    :ivar tool_calls: The tool calls that must be resolved and have their outputs appended to
+     subsequent input messages for the chat
+     completions request to resolve as configured.
+    :vartype tool_calls: list[~azure.ai.inference.models.ChatCompletionsToolCall]
+    """
+
+    role: Literal[ChatRole.ASSISTANT] = rest_discriminator(name="role")  # type: ignore
+    """The chat role associated with this message, which is always 'assistant' for assistant messages.
+     Required. The role that provides responses to system-instructed, user-prompted input."""
+    content: Optional[str] = rest_field()
+    """The content of the message."""
+    tool_calls: Optional[List["_models.ChatCompletionsToolCall"]] = rest_field()
+    """The tool calls that must be resolved and have their outputs appended to subsequent input
+     messages for the chat
+     completions request to resolve as configured."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        content: Optional[str] = None,
+        tool_calls: Optional[List["_models.ChatCompletionsToolCall"]] = 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, role=ChatRole.ASSISTANT, **kwargs)
+
+
+class ChatRequestDeveloperMessage(ChatRequestMessage, discriminator="developer"):
+    """A request chat message containing system instructions that influence how the model will
+    generate a chat completions
+    response. Some AI models support a developer message instead of a system message.
+
+    :ivar role: The chat role associated with this message, which is always 'developer' for
+     developer messages. Required. The role that instructs or sets the behavior of the assistant.
+     Some AI models support this role instead of the 'system' role.
+    :vartype role: str or ~azure.ai.inference.models.DEVELOPER
+    :ivar content: The contents of the developer message. Required.
+    :vartype content: str
+    """
+
+    role: Literal[ChatRole.DEVELOPER] = rest_discriminator(name="role")  # type: ignore
+    """The chat role associated with this message, which is always 'developer' for developer messages.
+     Required. The role that instructs or sets the behavior of the assistant. Some AI models support
+     this role instead of the 'system' role."""
+    content: str = rest_field()
+    """The contents of the developer message. Required."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        content: str,
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :param mapping: raw JSON to initialize the model.
+        :type mapping: Mapping[str, Any]
+        """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, role=ChatRole.DEVELOPER, **kwargs)
+
+
+class ChatRequestSystemMessage(ChatRequestMessage, discriminator="system"):
+    """A request chat message containing system instructions that influence how the model will
+    generate a chat completions
+    response.
+
+    :ivar role: The chat role associated with this message, which is always 'system' for system
+     messages. Required. The role that instructs or sets the behavior of the assistant.
+    :vartype role: str or ~azure.ai.inference.models.SYSTEM
+    :ivar content: The contents of the system message. Required.
+    :vartype content: str
+    """
+
+    role: Literal[ChatRole.SYSTEM] = rest_discriminator(name="role")  # type: ignore
+    """The chat role associated with this message, which is always 'system' for system messages.
+     Required. The role that instructs or sets the behavior of the assistant."""
+    content: str = rest_field()
+    """The contents of the system message. Required."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        content: str,
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :param mapping: raw JSON to initialize the model.
+        :type mapping: Mapping[str, Any]
+        """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, role=ChatRole.SYSTEM, **kwargs)
+
+
+class ChatRequestToolMessage(ChatRequestMessage, discriminator="tool"):
+    """A request chat message representing requested output from a configured tool.
+
+    :ivar role: The chat role associated with this message, which is always 'tool' for tool
+     messages. Required. The role that represents extension tool activity within a chat completions
+     operation.
+    :vartype role: str or ~azure.ai.inference.models.TOOL
+    :ivar content: The content of the message.
+    :vartype content: str
+    :ivar tool_call_id: The ID of the tool call resolved by the provided content. Required.
+    :vartype tool_call_id: str
+    """
+
+    role: Literal[ChatRole.TOOL] = rest_discriminator(name="role")  # type: ignore
+    """The chat role associated with this message, which is always 'tool' for tool messages. Required.
+     The role that represents extension tool activity within a chat completions operation."""
+    content: Optional[str] = rest_field()
+    """The content of the message."""
+    tool_call_id: str = rest_field()
+    """The ID of the tool call resolved by the provided content. Required."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        tool_call_id: str,
+        content: Optional[str] = None,
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :param mapping: raw JSON to initialize the model.
+        :type mapping: Mapping[str, Any]
+        """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, role=ChatRole.TOOL, **kwargs)
+
+
+class ChatRequestUserMessage(ChatRequestMessage, discriminator="user"):
+    """A request chat message representing user input to the assistant.
+
+    :ivar role: The chat role associated with this message, which is always 'user' for user
+     messages. Required. The role that provides input for chat completions.
+    :vartype role: str or ~azure.ai.inference.models.USER
+    :ivar content: The contents of the user message, with available input types varying by selected
+     model. Required. Is either a str type or a [ContentItem] type.
+    :vartype content: str or list[~azure.ai.inference.models.ContentItem]
+    """
+
+    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()
+    """The contents of the user message, with available input types varying by selected model.
+     Required. Is either a str type or a [ContentItem] type."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        content: Union[str, List["_models.ContentItem"]],
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :param mapping: raw JSON to initialize the model.
+        :type mapping: Mapping[str, Any]
+        """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, role=ChatRole.USER, **kwargs)
+
+
+class ChatResponseMessage(_model_base.Model):
+    """A representation of a chat message as received in a response.
+
+
+    :ivar role: The chat role associated with the message. Required. Known values are: "system",
+     "user", "assistant", "tool", and "developer".
+    :vartype role: str or ~azure.ai.inference.models.ChatRole
+    :ivar content: The content of the message. Required.
+    :vartype content: str
+    :ivar tool_calls: The tool calls that must be resolved and have their outputs appended to
+     subsequent input messages for the chat
+     completions request to resolve as configured.
+    :vartype tool_calls: list[~azure.ai.inference.models.ChatCompletionsToolCall]
+    """
+
+    role: Union[str, "_models.ChatRole"] = rest_field()
+    """The chat role associated with the message. Required. Known values are: \"system\", \"user\",
+     \"assistant\", \"tool\", and \"developer\"."""
+    content: str = rest_field()
+    """The content of the message. Required."""
+    tool_calls: Optional[List["_models.ChatCompletionsToolCall"]] = rest_field()
+    """The tool calls that must be resolved and have their outputs appended to subsequent input
+     messages for the chat
+     completions request to resolve as configured."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        role: Union[str, "_models.ChatRole"],
+        content: str,
+        tool_calls: Optional[List["_models.ChatCompletionsToolCall"]] = 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 CompletionsUsage(_model_base.Model):
+    """Representation of the token counts processed for a completions request.
+    Counts consider all tokens across prompts, choices, choice alternates, best_of generations, and
+    other consumers.
+
+
+    :ivar completion_tokens: The number of tokens generated across all completions emissions.
+     Required.
+    :vartype completion_tokens: int
+    :ivar prompt_tokens: The number of tokens in the provided prompts for the completions request.
+     Required.
+    :vartype prompt_tokens: int
+    :ivar total_tokens: The total number of tokens processed for the completions request and
+     response. Required.
+    :vartype total_tokens: int
+    """
+
+    completion_tokens: int = rest_field()
+    """The number of tokens generated across all completions emissions. Required."""
+    prompt_tokens: int = rest_field()
+    """The number of tokens in the provided prompts for the completions request. Required."""
+    total_tokens: int = rest_field()
+    """The total number of tokens processed for the completions request and response. Required."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        completion_tokens: int,
+        prompt_tokens: int,
+        total_tokens: int,
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :param mapping: raw JSON to initialize the model.
+        :type mapping: Mapping[str, Any]
+        """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, **kwargs)
+
+
+class EmbeddingItem(_model_base.Model):
+    """Representation of a single embeddings relatedness comparison.
+
+
+    :ivar embedding: 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.
+    :vartype embedding: str or list[float]
+    :ivar index: Index of the prompt to which the EmbeddingItem corresponds. Required.
+    :vartype index: int
+    """
+
+    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."""
+    index: int = rest_field()
+    """Index of the prompt to which the EmbeddingItem corresponds. Required."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        embedding: Union[str, List[float]],
+        index: int,
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :param mapping: raw JSON to initialize the model.
+        :type mapping: Mapping[str, Any]
+        """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, **kwargs)
+
+
+class EmbeddingsResult(_model_base.Model):
+    """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 id: Unique identifier for the embeddings result. Required.
+    :vartype id: str
+    :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
+    """
+
+    id: str = rest_field()
+    """Unique identifier for the embeddings result. Required."""
+    data: List["_models.EmbeddingItem"] = rest_field()
+    """Embedding values for the prompts submitted in the request. Required."""
+    usage: "_models.EmbeddingsUsage" = rest_field()
+    """Usage counts for tokens input using the embeddings API. Required."""
+    model: str = rest_field()
+    """The model ID used to generate this result. Required."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        id: str,  # pylint: disable=redefined-builtin
+        data: List["_models.EmbeddingItem"],
+        usage: "_models.EmbeddingsUsage",
+        model: str,
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :param mapping: raw JSON to initialize the model.
+        :type mapping: Mapping[str, Any]
+        """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, **kwargs)
+
+
+class EmbeddingsUsage(_model_base.Model):
+    """Measurement of the amount of tokens used in this request and response.
+
+
+    :ivar prompt_tokens: Number of tokens in the request. Required.
+    :vartype prompt_tokens: int
+    :ivar total_tokens: Total number of tokens transacted in this request/response. Should equal
+     the
+     number of tokens in the request. Required.
+    :vartype total_tokens: int
+    """
+
+    prompt_tokens: int = rest_field()
+    """Number of tokens in the request. Required."""
+    total_tokens: int = rest_field()
+    """Total number of tokens transacted in this request/response. Should equal the
+     number of tokens in the request. Required."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        prompt_tokens: int,
+        total_tokens: int,
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :param mapping: raw JSON to initialize the model.
+        :type mapping: Mapping[str, Any]
+        """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, **kwargs)
+
+
+class FunctionCall(_model_base.Model):
+    """The name and arguments of a function that should be called, as generated by the model.
+
+
+    :ivar name: The name of the function to call. Required.
+    :vartype name: str
+    :ivar arguments: The arguments to call the function with, as generated by the model in JSON
+     format.
+     Note that the model does not always generate valid JSON, and may hallucinate parameters
+     not defined by your function schema. Validate the arguments in your code before calling
+     your function. Required.
+    :vartype arguments: str
+    """
+
+    name: str = rest_field()
+    """The name of the function to call. Required."""
+    arguments: str = rest_field()
+    """The arguments to call the function with, as generated by the model in JSON format.
+     Note that the model does not always generate valid JSON, and may hallucinate parameters
+     not defined by your function schema. Validate the arguments in your code before calling
+     your function. Required."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        name: str,
+        arguments: str,
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :param mapping: raw JSON to initialize the model.
+        :type mapping: Mapping[str, Any]
+        """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, **kwargs)
+
+
+class FunctionDefinition(_model_base.Model):
+    """The definition of a caller-specified function that chat completions may invoke in response to
+    matching user input.
+
+    :ivar name: The name of the function to be called. Required.
+    :vartype name: str
+    :ivar description: A description of what the function does. The model will use this description
+     when selecting the function and
+     interpreting its parameters.
+    :vartype description: str
+    :ivar parameters: The parameters the function accepts, described as a JSON Schema object.
+    :vartype parameters: any
+    """
+
+    name: str = rest_field()
+    """The name of the function to be called. Required."""
+    description: Optional[str] = rest_field()
+    """A description of what the function does. The model will use this description when selecting the
+     function and
+     interpreting its parameters."""
+    parameters: Optional[Any] = rest_field()
+    """The parameters the function accepts, described as a JSON Schema object."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        name: str,
+        description: Optional[str] = None,
+        parameters: Optional[Any] = None,
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :param mapping: raw JSON to initialize the model.
+        :type mapping: Mapping[str, Any]
+        """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, **kwargs)
+
+
+class ImageContentItem(ContentItem, discriminator="image_url"):
+    """A structured chat content item containing an image reference.
+
+    :ivar type: The discriminated object type: always 'image_url' for this type. Required. Default
+     value is "image_url".
+    :vartype type: str
+    :ivar image_url: An internet location, which must be accessible to the model,from which the
+     image may be retrieved. Required.
+    :vartype image_url: ~azure.ai.inference.models.ImageUrl
+    """
+
+    type: Literal["image_url"] = rest_discriminator(name="type")  # type: ignore
+    """The discriminated object type: always 'image_url' for this type. Required. Default value is
+     \"image_url\"."""
+    image_url: "_models.ImageUrl" = rest_field()
+    """An internet location, which must be accessible to the model,from which the image may be
+     retrieved. Required."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        image_url: "_models.ImageUrl",
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :param mapping: raw JSON to initialize the model.
+        :type mapping: Mapping[str, Any]
+        """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, type="image_url", **kwargs)
+
+
+class ImageEmbeddingInput(_model_base.Model):
+    """Represents an image with optional text.
+
+    :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.
+
+    :ivar url: The URL of the image. Required.
+    :vartype url: 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
+    """
+
+    url: str = rest_field()
+    """The URL of the image. Required."""
+    detail: Optional[Union[str, "_models.ImageDetailLevel"]] = rest_field()
+    """The evaluation quality setting to use, which controls relative prioritization of speed, token
+     consumption, and
+     accuracy. Known values are: \"auto\", \"low\", and \"high\"."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        url: str,
+        detail: Optional[Union[str, "_models.ImageDetailLevel"]] = 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 InputAudio(_model_base.Model):
+    """The details of an audio chat message content part.
+
+    :ivar data: Base64 encoded audio data. Required.
+    :vartype data: str
+    :ivar format: The audio format of the audio content. Required. Known values are: "wav" and
+     "mp3".
+    :vartype format: str or ~azure.ai.inference.models.AudioContentFormat
+    """
+
+    data: str = rest_field()
+    """Base64 encoded audio data. Required."""
+    format: Union[str, "_models.AudioContentFormat"] = rest_field()
+    """The audio format of the audio content. Required. Known values are: \"wav\" and \"mp3\"."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        data: str,
+        format: Union[str, "_models.AudioContentFormat"],
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :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 JsonSchemaFormat(_model_base.Model):
+    """Defines the response format for chat completions as JSON with a given schema.
+    The AI model will need to adhere to this schema when generating completions.
+
+    :ivar name: A name that labels this JSON schema. 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: The definition of the JSON schema. See
+     https://json-schema.org/overview/what-is-jsonschema.
+     Note that AI models usually only support a subset of the keywords defined by JSON schema.
+     Consult your AI model documentation to determine what is supported. Required.
+    :vartype schema: dict[str, any]
+    :ivar description: A description of the response format, used by the AI model to determine how
+     to generate responses in this format.
+    :vartype description: str
+    :ivar strict: If set to true, the service will error out if the provided JSON schema contains
+     keywords
+     not supported by the AI model. An example of such keyword may be ``maxLength`` for JSON type
+     ``string``.
+     If false, and the provided JSON schema contains keywords not supported by the AI model,
+     the AI model will not error out. Instead it will ignore the unsupported keywords.
+    :vartype strict: bool
+    """
+
+    name: str = rest_field()
+    """A name that labels this JSON schema. Must be a-z, A-Z, 0-9, or contain underscores and dashes,
+     with a maximum length of 64. Required."""
+    schema: Dict[str, Any] = rest_field()
+    """The definition of the JSON schema. See https://json-schema.org/overview/what-is-jsonschema.
+     Note that AI models usually only support a subset of the keywords defined by JSON schema.
+     Consult your AI model documentation to determine what is supported. Required."""
+    description: Optional[str] = rest_field()
+    """A description of the response format, used by the AI model to determine how to generate
+     responses in this format."""
+    strict: Optional[bool] = rest_field()
+    """If set to true, the service will error out if the provided JSON schema contains keywords
+     not supported by the AI model. An example of such keyword may be ``maxLength`` for JSON type
+     ``string``.
+     If false, and the provided JSON schema contains keywords not supported by the AI model,
+     the AI model will not error out. Instead it will ignore the unsupported keywords."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        name: str,
+        schema: Dict[str, Any],
+        description: Optional[str] = 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 ModelInfo(_model_base.Model):
+    """Represents some basic information about the AI model.
+
+
+    :ivar model_name: The name of the AI model. For example: ``Phi21``. Required.
+    :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_completion".
+    :vartype model_type: str or ~azure.ai.inference.models.ModelType
+    :ivar model_provider_name: The model provider name. For example: ``Microsoft Research``.
+     Required.
+    :vartype model_provider_name: str
+    """
+
+    model_name: str = rest_field()
+    """The name of the AI model. For example: ``Phi21``. Required."""
+    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_completion\"."""
+    model_provider_name: str = rest_field()
+    """The model provider name. For example: ``Microsoft Research``. Required."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        model_name: str,
+        model_type: Union[str, "_models.ModelType"],
+        model_provider_name: str,
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :param mapping: raw JSON to initialize the model.
+        :type mapping: Mapping[str, Any]
+        """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, **kwargs)
+
+
+class StreamingChatChoiceUpdate(_model_base.Model):
+    """Represents an update to a single prompt completion when the service is streaming updates
+    using Server Sent Events (SSE).
+    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.
+
+
+    :ivar index: The ordered index associated with this chat completions choice. Required.
+    :vartype index: int
+    :ivar finish_reason: The reason that this chat completions choice completed its generated.
+     Required. Known values are: "stop", "length", "content_filter", and "tool_calls".
+    :vartype finish_reason: str or ~azure.ai.inference.models.CompletionsFinishReason
+    :ivar delta: An update to the chat message for a given chat completions prompt. Required.
+    :vartype delta: ~azure.ai.inference.models.StreamingChatResponseMessageUpdate
+    """
+
+    index: int = rest_field()
+    """The ordered index associated with this chat completions choice. Required."""
+    finish_reason: Union[str, "_models.CompletionsFinishReason"] = rest_field()
+    """The reason that this chat completions choice completed its generated. Required. Known values
+     are: \"stop\", \"length\", \"content_filter\", and \"tool_calls\"."""
+    delta: "_models.StreamingChatResponseMessageUpdate" = rest_field()
+    """An update to the chat message for a given chat completions prompt. Required."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        index: int,
+        finish_reason: Union[str, "_models.CompletionsFinishReason"],
+        delta: "_models.StreamingChatResponseMessageUpdate",
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :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 StreamingChatCompletionsUpdate(_model_base.Model):
+    """Represents a response update to a chat completions request, when the service is streaming
+    updates
+    using Server Sent Events (SSE).
+    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 choices: An update to the collection of completion 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.StreamingChatChoiceUpdate]
+    :ivar usage: Usage information for tokens processed and generated as part of this completions
+     operation.
+    :vartype usage: ~azure.ai.inference.models.CompletionsUsage
+    """
+
+    id: str = rest_field()
+    """A unique identifier associated with this chat completions response. Required."""
+    created: datetime.datetime = rest_field(format="unix-timestamp")
+    """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."""
+    model: str = rest_field()
+    """The model used for the chat completion. Required."""
+    choices: List["_models.StreamingChatChoiceUpdate"] = rest_field()
+    """An update to the collection of completion 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."""
+    usage: Optional["_models.CompletionsUsage"] = rest_field()
+    """Usage information for tokens processed and generated as part of this completions operation."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        id: str,  # pylint: disable=redefined-builtin
+        created: datetime.datetime,
+        model: str,
+        choices: List["_models.StreamingChatChoiceUpdate"],
+        usage: Optional["_models.CompletionsUsage"] = 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 StreamingChatResponseMessageUpdate(_model_base.Model):
+    """A representation of a chat message update as received in a streaming response.
+
+    :ivar role: The chat role associated with the message. If present, should always be
+     'assistant'. Known values are: "system", "user", "assistant", "tool", and "developer".
+    :vartype role: str or ~azure.ai.inference.models.ChatRole
+    :ivar content: The content of the message.
+    :vartype content: str
+    :ivar tool_calls: The tool calls that must be resolved and have their outputs appended to
+     subsequent input messages for the chat
+     completions request to resolve as configured.
+    :vartype tool_calls: list[~azure.ai.inference.models.StreamingChatResponseToolCallUpdate]
+    """
+
+    role: Optional[Union[str, "_models.ChatRole"]] = rest_field()
+    """The chat role associated with the message. If present, should always be 'assistant'. Known
+     values are: \"system\", \"user\", \"assistant\", \"tool\", and \"developer\"."""
+    content: Optional[str] = rest_field()
+    """The content of the message."""
+    tool_calls: Optional[List["_models.StreamingChatResponseToolCallUpdate"]] = rest_field()
+    """The tool calls that must be resolved and have their outputs appended to subsequent input
+     messages for the chat
+     completions request to resolve as configured."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        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]) -> None:
+        """
+        :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 StreamingChatResponseToolCallUpdate(_model_base.Model):
+    """An update to the function tool call information requested by the AI model.
+
+
+    :ivar id: The ID of the tool call. Required.
+    :vartype id: str
+    :ivar function: Updates to the function call requested by the AI model. Required.
+    :vartype function: ~azure.ai.inference.models.FunctionCall
+    """
+
+    id: str = rest_field()
+    """The ID of the tool call. Required."""
+    function: "_models.FunctionCall" = rest_field()
+    """Updates to the function call requested by the AI model. Required."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        id: str,  # pylint: disable=redefined-builtin
+        function: "_models.FunctionCall",
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :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 TextContentItem(ContentItem, discriminator="text"):
+    """A structured chat content item containing plain text.
+
+    :ivar type: The discriminated object type: always 'text' for this type. Required. Default value
+     is "text".
+    :vartype type: str
+    :ivar text: The content of the message. Required.
+    :vartype text: str
+    """
+
+    type: Literal["text"] = rest_discriminator(name="type")  # type: ignore
+    """The discriminated object type: always 'text' for this type. Required. Default value is
+     \"text\"."""
+    text: str = rest_field()
+    """The content of the message. Required."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        text: str,
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :param mapping: raw JSON to initialize the model.
+        :type mapping: Mapping[str, Any]
+        """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, type="text", **kwargs)