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# `mistral_common.protocol.instruct.request`

## `ChatCompletionRequest(**data)`

Bases: `BaseCompletionRequest`, `Generic[ChatMessageType]`

Request for a chat completion.

Attributes:

Name | Type | Description
--- | --- | ---
`model` | `str | None` | The model to use for the chat completion.
`messages` | `list[ChatMessageType]` | The messages to use for the chat completion.
`response_format` | `ResponseFormat` | The format of the response.
`tools` | `list[Tool] | None` | The tools to use for the chat completion.
`tool_choice` | `ToolChoice` | The tool choice to use for the chat completion.
`truncate_for_context_length` | `bool` | Whether to truncate the messages for the context length.
`continue_final_message` | `bool` | Whether to continue the final message.
`reasoning_effort` | `ReasoningEffort | None` | Controls how much reasoning effort the model should apply.

Examples:

    >>> from mistral_common.protocol.instruct.messages import UserMessage, AssistantMessage
    >>> from mistral_common.protocol.instruct.tool_calls import ToolTypes, Function
    >>> request = ChatCompletionRequest(
    ...     messages=[
    ...         UserMessage(content="Hello!"),
    ...         AssistantMessage(content="Hi! How can I help you?"),
    ...     ],
    ...     response_format=ResponseFormat(type=ResponseFormats.text),
    ...     tools=[Tool(type=ToolTypes.function, function=Function(name="get_weather", parameters={}))],
    ...     tool_choice=ToolChoiceEnum.auto,
    ...     truncate_for_context_length=True,
    ... )

Source code in `pydantic/main.py`

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    def __init__(self, /, **data: Any) -> None:
        """Create a new model by parsing and validating input data from keyword arguments.

        Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
        validated to form a valid model.

        `self` is explicitly positional-only to allow `self` as a field name.
        """
        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
        __tracebackhide__ = True
        validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
        if self is not validated_self:
            warnings.warn(
                'A custom validator is returning a value other than `self`.\n'
                "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n"
                'See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.',
                stacklevel=2,
            )

### `from_openai(messages, tools=None, continue_final_message=False, **kwargs)` `classmethod`

Create a chat completion request from the OpenAI format.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`messages` | `list[dict[str, str | list[dict[str, str | dict[str, Any]]]]]` | The messages in the OpenAI format. | required
`tools` | `list[dict[str, Any]] | None` | The tools in the OpenAI format. | `None`
`continue_final_message` | `bool` | Whether to continue the final message. | `False`
`**kwargs` | `Any` | Additional keyword arguments to pass to the constructor. These should be the same as the fields of the request class or the OpenAI API equivalent. | `{}`

Returns:

Type | Description
--- | ---
`ChatCompletionRequest` | The chat completion request.

Source code in `src/mistral_common/protocol/instruct/request.py`

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    @classmethod
    def from_openai(
        cls,
        messages: list[dict[str, str | list[dict[str, str | dict[str, Any]]]]],
        tools: list[dict[str, Any]] | None = None,
        continue_final_message: bool = False,
        **kwargs: Any,
    ) -> "ChatCompletionRequest":
        r"""Create a chat completion request from the OpenAI format.

        Args:
            messages: The messages in the OpenAI format.
            tools: The tools in the OpenAI format.
            continue_final_message: Whether to continue the final message.
            **kwargs: Additional keyword arguments to pass to the constructor. These should be the same as the fields
                of the request class or the OpenAI API equivalent.

        Returns:
            The chat completion request.
        """
        if "seed" in kwargs and "random_seed" in kwargs:
            raise ValueError("Cannot specify both `seed` and `random_seed`.")

        random_seed = kwargs.pop("seed", None)
        if random_seed is None:
            random_seed = kwargs.pop("random_seed", None)

        filtered_kwargs = cls._filter_cls_fields(kwargs)

        converted_messages: list[ChatMessage] = convert_openai_messages(messages)

        converted_tools = convert_openai_tools(tools) if tools is not None else None

        return cls(
            messages=converted_messages,  # type: ignore[arg-type]
            tools=converted_tools,
            random_seed=random_seed,
            continue_final_message=continue_final_message,
            **filtered_kwargs,
        )

### `to_openai(reasoning_field_format=None, **kwargs)`

Convert the request messages and tools into the OpenAI format.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`reasoning_field_format` | `ReasoningFieldFormat | None` | Format for converting thinking chunks in assistant messages. See `AssistantMessage.to_openai` for details. | `None`
`kwargs` | `Any` | Additional parameters to be added to the request. | `{}`

Returns:

Type | Description
--- | ---
`dict[str, Any]` | The request in the OpenAI format.

Examples:

    >>> from mistral_common.protocol.instruct.messages import UserMessage
    >>> from mistral_common.protocol.instruct.tool_calls import Tool, Function
    >>> request = ChatCompletionRequest(messages=[UserMessage(content="Hello, how are you?")], temperature=0.15)
    >>> request.to_openai(stream=True)
    {'temperature': 0.15, 'top_p': 1.0, 'response_format': {'type': 'text'}, 'continue_final_message': False, 'messages': [{'role': 'user', 'content': 'Hello, how are you?'}], 'tool_choice': 'auto', 'stream': True}
    >>> request = ChatCompletionRequest(messages=[UserMessage(content="Hello, how are you?")], tools=[
    ...     Tool(function=Function(
    ...         name="get_current_weather",
    ...         description="Get the current weather in a given location",
    ...         parameters={
    ...             "type": "object",
    ...             "properties": {
    ...                 "location": {
    ...                     "type": "string",
    ...                     "description": "The city and state, e.g. San Francisco, CA",
    ...                 },
    ...                 "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
    ...             },
    ...             "required": ["location"],
    ...         },
    ...     ),
    ... )])
    >>> request.to_openai()
    {'temperature': 0.7, 'top_p': 1.0, 'response_format': {'type': 'text'}, 'continue_final_message': False, 'messages': [{'role': 'user', 'content': 'Hello, how are you?'}], 'tools': [{'type': 'function', 'function': {'name': 'get_current_weather', 'description': 'Get the current weather in a given location', 'parameters': {'type': 'object', 'properties': {'location': {'type': 'string', 'description': 'The city and state, e.g. San Francisco, CA'}, 'unit': {'type': 'string', 'enum': ['celsius', 'fahrenheit']}}, 'required': ['location']}, 'strict': False}}], 'tool_choice': 'auto'}

Source code in `src/mistral_common/protocol/instruct/request.py`

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    def to_openai(
        self,
        reasoning_field_format: ReasoningFieldFormat | None = None,
        **kwargs: Any,
    ) -> dict[str, Any]:
        r"""Convert the request messages and tools into the OpenAI format.

        Args:
            reasoning_field_format: Format for converting thinking chunks in assistant messages.
                See `AssistantMessage.to_openai` for details.
            kwargs: Additional parameters to be added to the request.

        Returns:
            The request in the OpenAI format.

        Examples:
            >>> from mistral_common.protocol.instruct.messages import UserMessage
            >>> from mistral_common.protocol.instruct.tool_calls import Tool, Function
            >>> request = ChatCompletionRequest(messages=[UserMessage(content="Hello, how are you?")], temperature=0.15)
            >>> request.to_openai(stream=True)
            {'temperature': 0.15, 'top_p': 1.0, 'response_format': {'type': 'text'}, 'continue_final_message': False, 'messages': [{'role': 'user', 'content': 'Hello, how are you?'}], 'tool_choice': 'auto', 'stream': True}
            >>> request = ChatCompletionRequest(messages=[UserMessage(content="Hello, how are you?")], tools=[
            ...     Tool(function=Function(
            ...         name="get_current_weather",
            ...         description="Get the current weather in a given location",
            ...         parameters={
            ...             "type": "object",
            ...             "properties": {
            ...                 "location": {
            ...                     "type": "string",
            ...                     "description": "The city and state, e.g. San Francisco, CA",
            ...                 },
            ...                 "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
            ...             },
            ...             "required": ["location"],
            ...         },
            ...     ),
            ... )])
            >>> request.to_openai()
            {'temperature': 0.7, 'top_p': 1.0, 'response_format': {'type': 'text'}, 'continue_final_message': False, 'messages': [{'role': 'user', 'content': 'Hello, how are you?'}], 'tools': [{'type': 'function', 'function': {'name': 'get_current_weather', 'description': 'Get the current weather in a given location', 'parameters': {'type': 'object', 'properties': {'location': {'type': 'string', 'description': 'The city and state, e.g. San Francisco, CA'}, 'unit': {'type': 'string', 'enum': ['celsius', 'fahrenheit']}}, 'required': ['location']}, 'strict': False}}], 'tool_choice': 'auto'}
        """  # noqa: E501

        # Handle messages and tools separately.
        openai_request: dict[str, Any] = self.model_dump(
            exclude={"messages", "tools", "truncate_for_context_length", "tool_choice"}, exclude_none=True
        )

        # Rename random_seed to seed.
        seed = openai_request.pop("random_seed", None)
        if seed is not None:
            openai_request["seed"] = seed

        if self.truncate_for_context_length:
            raise NotImplementedError("Truncating for context length is not implemented for OpenAI requests.")

        for kwarg in kwargs:
            if kwarg in openai_request:
                raise ValueError(f"Duplicate keyword argument: {kwarg}")
            elif kwarg in ChatCompletionRequest.model_fields:
                raise ValueError(f"Keyword argument {kwarg} is already set in the request.")

        openai_messages = []
        for message in self.messages:
            if isinstance(message, AssistantMessage):
                openai_messages.append(message.to_openai(reasoning_field_format=reasoning_field_format))
            else:
                openai_messages.append(message.to_openai())

        openai_request["messages"] = openai_messages
        if self.tools is not None:
            openai_request["tools"] = [tool.to_openai() for tool in self.tools]

        openai_tool_choice: str | dict[str, Any]
        match self.tool_choice:
            case ToolChoiceEnum.auto | ToolChoiceEnum.none:
                openai_tool_choice = self.tool_choice
            case ToolChoiceEnum.required | ToolChoiceEnum.any:
                openai_tool_choice = ToolChoiceEnum.required.value
            case _:
                openai_tool_choice = self.tool_choice.model_dump()

        openai_request["tool_choice"] = openai_tool_choice

        openai_request.update(kwargs)

        return openai_request

## `InstructRequest(**data)`

Bases: `MistralBase`, `Generic[ChatMessageType, ToolType]`

A valid Instruct request to be tokenized.

Note

This class is intended for internal use only. External users should use `ChatCompletionRequest` to build requests and to convert to and from the OpenAI format.

Attributes:

Name | Type | Description
--- | --- | ---
`messages` | `list[ChatMessageType]` | The history of the conversation.
`system_prompt` | `str | None` | The system prompt to be used for the conversation.
`available_tools` | `list[ToolType] | None` | The tools available to the assistant.
`truncate_at_max_tokens` | `int | None` | The maximum number of tokens to truncate the conversation at.
`continue_final_message` | `bool` | Whether to continue the final message.
`settings` | `ModelSettings` | Model configuration settings for the request.

Examples:

    >>> from mistral_common.protocol.instruct.messages import UserMessage, SystemMessage
    >>> request = InstructRequest(
    ...     messages=[UserMessage(content="Hello, how are you?")], system_prompt="You are a helpful assistant."
    ... )

Source code in `pydantic/main.py`

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    def __init__(self, /, **data: Any) -> None:
        """Create a new model by parsing and validating input data from keyword arguments.

        Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
        validated to form a valid model.

        `self` is explicitly positional-only to allow `self` as a field name.
        """
        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
        __tracebackhide__ = True
        validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
        if self is not validated_self:
            warnings.warn(
                'A custom validator is returning a value other than `self`.\n'
                "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n"
                'See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.',
                stacklevel=2,
            )

## `ModelSettings(**data)`

Bases: `MistralBase`

Model configuration settings for instruct requests.

This class encapsulates various model configuration options that can be passed to the model during inference. Currently supports reasoning effort configuration, but can be extended with additional settings in the future.

Attributes:

Name | Type | Description
--- | --- | ---
`reasoning_effort` | `ReasoningEffort | None` | Controls how much reasoning effort the model should apply when generating responses. Supported for tokenizer >= v15 and not supported for earlier versions.

Source code in `pydantic/main.py`

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    def __init__(self, /, **data: Any) -> None:
        """Create a new model by parsing and validating input data from keyword arguments.

        Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
        validated to form a valid model.

        `self` is explicitly positional-only to allow `self` as a field name.
        """
        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
        __tracebackhide__ = True
        validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
        if self is not validated_self:
            warnings.warn(
                'A custom validator is returning a value other than `self`.\n'
                "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n"
                'See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.',
                stacklevel=2,
            )

### `none()` `staticmethod`

Create a ModelSettings instance with default (None) values.

Source code in `src/mistral_common/protocol/instruct/request.py`

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    @staticmethod
    def none() -> "ModelSettings":
        r"""Create a ModelSettings instance with default (None) values."""
        return ModelSettings()

## `ReasoningEffort`

Bases: `str`, `Enum`

Controls how much reasoning effort the model should apply.

Attributes:

Name | Type | Description
--- | --- | ---
`none` |  | No additional reasoning effort.
`high` |  | High reasoning effort for complex tasks.

## `ResponseFormat(**data)`

Bases: `MistralBase`

The format of the response.

Attributes:

Name | Type | Description
--- | --- | ---
`type` | `ResponseFormats` | The type of the response.

Examples:

    >>> response_format = ResponseFormat(type=ResponseFormats.text)

Source code in `pydantic/main.py`

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    def __init__(self, /, **data: Any) -> None:
        """Create a new model by parsing and validating input data from keyword arguments.

        Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
        validated to form a valid model.

        `self` is explicitly positional-only to allow `self` as a field name.
        """
        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
        __tracebackhide__ = True
        validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
        if self is not validated_self:
            warnings.warn(
                'A custom validator is returning a value other than `self`.\n'
                "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n"
                'See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.',
                stacklevel=2,
            )

## `ResponseFormats`

Bases: `str`, `Enum`

Enum of the different formats of an instruct response.

Attributes:

Name | Type | Description
--- | --- | ---
`text` |  | The response is a plain text.
`json` |  | The response is a JSON object.

Examples:

    >>> response_format = ResponseFormats.text