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# Source code for langchain_mistralai.chat_models

    from __future__ import annotations

    import json
    import logging
    import uuid
    from operator import itemgetter
    from typing import (
        Any,
        AsyncContextManager,
        AsyncIterator,
        Callable,
        Dict,
        Iterator,
        List,
        Optional,
        Sequence,
        Tuple,
        Type,
        Union,
        cast,
    )

    import httpx
    from httpx_sse import EventSource, aconnect_sse, connect_sse
    from langchain_core.callbacks import (
        AsyncCallbackManagerForLLMRun,
        CallbackManagerForLLMRun,
    )
    from langchain_core.language_models import LanguageModelInput
    from langchain_core.language_models.chat_models import (
        BaseChatModel,
        agenerate_from_stream,
        generate_from_stream,
    )
    from langchain_core.language_models.llms import create_base_retry_decorator
    from langchain_core.messages import (
        AIMessage,
        AIMessageChunk,
        BaseMessage,
        BaseMessageChunk,
        ChatMessage,
        ChatMessageChunk,
        HumanMessage,
        HumanMessageChunk,
        InvalidToolCall,
        SystemMessage,
        SystemMessageChunk,
        ToolCall,
        ToolMessage,
    )
    from langchain_core.output_parsers.base import OutputParserLike
    from langchain_core.output_parsers.openai_tools import (
        JsonOutputKeyToolsParser,
        PydanticToolsParser,
        make_invalid_tool_call,
        parse_tool_call,
    )
    from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
    from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
    from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
    from langchain_core.tools import BaseTool
    from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
    from langchain_core.utils.function_calling import convert_to_openai_tool

    logger = logging.getLogger(__name__)

    def _create_retry_decorator(
        llm: ChatMistralAI,
        run_manager: Optional[
            Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
        ] = None,
    ) -> Callable[[Any], Any]:
        """Returns a tenacity retry decorator, preconfigured to handle exceptions"""

        errors = [httpx.RequestError, httpx.StreamError]
        return create_base_retry_decorator(
            error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
        )

    def _convert_mistral_chat_message_to_message(
        _message: Dict,
    ) -> BaseMessage:
        role = _message["role"]
        assert role == "assistant", f"Expected role to be 'assistant', got {role}"
        content = cast(str, _message["content"])

        additional_kwargs: Dict = {}
        tool_calls = []
        invalid_tool_calls = []
        if raw_tool_calls := _message.get("tool_calls"):
            additional_kwargs["tool_calls"] = raw_tool_calls
            for raw_tool_call in raw_tool_calls:
                try:
                    parsed: dict = cast(
                        dict, parse_tool_call(raw_tool_call, return_id=True)
                    )
                    if not parsed["id"]:
                        tool_call_id = uuid.uuid4().hex[:]
                        tool_calls.append(
                            {
                                **parsed,
                                **{"id": tool_call_id},
                            },
                        )
                    else:
                        tool_calls.append(parsed)
                except Exception as e:
                    invalid_tool_calls.append(
                        dict(make_invalid_tool_call(raw_tool_call, str(e)))
                    )
        return AIMessage(
            content=content,
            additional_kwargs=additional_kwargs,
            tool_calls=tool_calls,
            invalid_tool_calls=invalid_tool_calls,
        )

    def _raise_on_error(response: httpx.Response) -> None:
        """Raise an error if the response is an error."""
        if httpx.codes.is_error(response.status_code):
            error_message = response.read().decode("utf-8")
            raise httpx.HTTPStatusError(
                f"Error response {response.status_code} "
                f"while fetching {response.url}: {error_message}",
                request=response.request,
                response=response,
            )

    async def _araise_on_error(response: httpx.Response) -> None:
        """Raise an error if the response is an error."""
        if httpx.codes.is_error(response.status_code):
            error_message = (await response.aread()).decode("utf-8")
            raise httpx.HTTPStatusError(
                f"Error response {response.status_code} "
                f"while fetching {response.url}: {error_message}",
                request=response.request,
                response=response,
            )

    async def _aiter_sse(
        event_source_mgr: AsyncContextManager[EventSource],
    ) -> AsyncIterator[Dict]:
        """Iterate over the server-sent events."""
        async with event_source_mgr as event_source:
            await _araise_on_error(event_source.response)
            async for event in event_source.aiter_sse():
                if event.data == "[DONE]":
                    return
                yield event.json()

    async def acompletion_with_retry(
        llm: ChatMistralAI,
        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> Any:
        """Use tenacity to retry the async completion call."""
        retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)

        @retry_decorator
        async def _completion_with_retry(**kwargs: Any) -> Any:
            if "stream" not in kwargs:
                kwargs["stream"] = False
            stream = kwargs["stream"]
            if stream:
                event_source = aconnect_sse(
                    llm.async_client, "POST", "/chat/completions", json=kwargs
                )
                return _aiter_sse(event_source)
            else:
                response = await llm.async_client.post(url="/chat/completions", json=kwargs)
                await _araise_on_error(response)
                return response.json()

        return await _completion_with_retry(**kwargs)

    def _convert_delta_to_message_chunk(
        _delta: Dict, default_class: Type[BaseMessageChunk]
    ) -> BaseMessageChunk:
        role = _delta.get("role")
        content = _delta.get("content") or ""
        if role == "user" or default_class == HumanMessageChunk:
            return HumanMessageChunk(content=content)
        elif role == "assistant" or default_class == AIMessageChunk:
            additional_kwargs: Dict = {}
            if raw_tool_calls := _delta.get("tool_calls"):
                additional_kwargs["tool_calls"] = raw_tool_calls
                try:
                    tool_call_chunks = []
                    for raw_tool_call in raw_tool_calls:
                        if not raw_tool_call.get("index") and not raw_tool_call.get("id"):
                            tool_call_id = uuid.uuid4().hex[:]
                        else:
                            tool_call_id = raw_tool_call.get("id")
                        tool_call_chunks.append(
                            {
                                "name": raw_tool_call["function"].get("name"),
                                "args": raw_tool_call["function"].get("arguments"),
                                "id": tool_call_id,
                                "index": raw_tool_call.get("index"),
                            }
                        )
                except KeyError:
                    pass
            else:
                tool_call_chunks = []
            return AIMessageChunk(
                content=content,
                additional_kwargs=additional_kwargs,
                tool_call_chunks=tool_call_chunks,
            )
        elif role == "system" or default_class == SystemMessageChunk:
            return SystemMessageChunk(content=content)
        elif role or default_class == ChatMessageChunk:
            return ChatMessageChunk(content=content, role=role)
        else:
            return default_class(content=content)

    def _format_tool_call_for_mistral(tool_call: ToolCall) -> dict:
        """Format Langchain ToolCall to dict expected by Mistral."""
        result: Dict[str, Any] = {
            "function": {
                "name": tool_call["name"],
                "arguments": json.dumps(tool_call["args"]),
            }
        }
        if _id := tool_call.get("id"):
            result["id"] = _id

        return result

    def _format_invalid_tool_call_for_mistral(invalid_tool_call: InvalidToolCall) -> dict:
        """Format Langchain InvalidToolCall to dict expected by Mistral."""
        result: Dict[str, Any] = {
            "function": {
                "name": invalid_tool_call["name"],
                "arguments": invalid_tool_call["args"],
            }
        }
        if _id := invalid_tool_call.get("id"):
            result["id"] = _id

        return result

    def _convert_message_to_mistral_chat_message(
        message: BaseMessage,
    ) -> Dict:
        if isinstance(message, ChatMessage):
            return dict(role=message.role, content=message.content)
        elif isinstance(message, HumanMessage):
            return dict(role="user", content=message.content)
        elif isinstance(message, AIMessage):
            message_dict: Dict[str, Any] = {"role": "assistant"}
            tool_calls = []
            if message.tool_calls or message.invalid_tool_calls:
                for tool_call in message.tool_calls:
                    tool_calls.append(_format_tool_call_for_mistral(tool_call))
                for invalid_tool_call in message.invalid_tool_calls:
                    tool_calls.append(
                        _format_invalid_tool_call_for_mistral(invalid_tool_call)
                    )
            elif "tool_calls" in message.additional_kwargs:
                for tc in message.additional_kwargs["tool_calls"]:
                    chunk = {
                        "function": {
                            "name": tc["function"]["name"],
                            "arguments": tc["function"]["arguments"],
                        }
                    }
                    if _id := tc.get("id"):
                        chunk["id"] = _id
                    tool_calls.append(chunk)
            else:
                pass
            if tool_calls:  # do not populate empty list tool_calls
                message_dict["tool_calls"] = tool_calls
            if tool_calls and message.content:
                # Assistant message must have either content or tool_calls, but not both.
                # Some providers may not support tool_calls in the same message as content.
                # This is done to ensure compatibility with messages from other providers.
                message_dict["content"] = ""
            else:
                message_dict["content"] = message.content
            return message_dict
        elif isinstance(message, SystemMessage):
            return dict(role="system", content=message.content)
        elif isinstance(message, ToolMessage):
            return {
                "role": "tool",
                "content": message.content,
                "name": message.name,
            }
        else:
            raise ValueError(f"Got unknown type {message}")

    class ChatMistralAI(BaseChatModel):
        """A chat model that uses the MistralAI API."""

        client: httpx.Client = Field(default=None)  #: :meta private:
        async_client: httpx.AsyncClient = Field(default=None)  #: :meta private:
        mistral_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
        endpoint: str = "https://api.mistral.ai/v1"
        max_retries: int = 5
        timeout: int = 120
        max_concurrent_requests: int = 64
        model: str = Field(default="mistral-small", alias="model_name")
        temperature: float = 0.7
        max_tokens: Optional[int] = None
        top_p: float = 1
        """Decode using nucleus sampling: consider the smallest set of tokens whose
           probability sum is at least top_p. Must be in the closed interval [0.0, 1.0]."""
        random_seed: Optional[int] = None
        safe_mode: bool = False
        streaming: bool = False

        class Config:
            """Configuration for this pydantic object."""

            allow_population_by_field_name = True
            arbitrary_types_allowed = True

        @property
        def _default_params(self) -> Dict[str, Any]:
            """Get the default parameters for calling the API."""
            defaults = {
                "model": self.model,
                "temperature": self.temperature,
                "max_tokens": self.max_tokens,
                "top_p": self.top_p,
                "random_seed": self.random_seed,
                "safe_prompt": self.safe_mode,
            }
            filtered = {k: v for k, v in defaults.items() if v is not None}
            return filtered

        @property
        def _client_params(self) -> Dict[str, Any]:
            """Get the parameters used for the client."""
            return self._default_params

        def completion_with_retry(
            self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any
        ) -> Any:
            """Use tenacity to retry the completion call."""
            # retry_decorator = _create_retry_decorator(self, run_manager=run_manager)

            # @retry_decorator
            def _completion_with_retry(**kwargs: Any) -> Any:
                if "stream" not in kwargs:
                    kwargs["stream"] = False
                stream = kwargs["stream"]
                if stream:

                    def iter_sse() -> Iterator[Dict]:
                        with connect_sse(
                            self.client, "POST", "/chat/completions", json=kwargs
                        ) as event_source:
                            _raise_on_error(event_source.response)
                            for event in event_source.iter_sse():
                                if event.data == "[DONE]":
                                    return
                                yield event.json()

                    return iter_sse()
                else:
                    response = self.client.post(url="/chat/completions", json=kwargs)
                    _raise_on_error(response)
                    return response.json()

            rtn = _completion_with_retry(**kwargs)
            return rtn

        def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
            overall_token_usage: dict = {}
            for output in llm_outputs:
                if output is None:
                    # Happens in streaming
                    continue
                token_usage = output["token_usage"]
                if token_usage is not None:
                    for k, v in token_usage.items():
                        if k in overall_token_usage:
                            overall_token_usage[k] += v
                        else:
                            overall_token_usage[k] = v
            combined = {"token_usage": overall_token_usage, "model_name": self.model}
            return combined

        @root_validator()
        def validate_environment(cls, values: Dict) -> Dict:
            """Validate api key, python package exists, temperature, and top_p."""

            values["mistral_api_key"] = convert_to_secret_str(
                get_from_dict_or_env(
                    values, "mistral_api_key", "MISTRAL_API_KEY", default=""
                )
            )
            api_key_str = values["mistral_api_key"].get_secret_value()
            # todo: handle retries
            if not values.get("client"):
                values["client"] = httpx.Client(
                    base_url=values["endpoint"],
                    headers={
                        "Content-Type": "application/json",
                        "Accept": "application/json",
                        "Authorization": f"Bearer {api_key_str}",
                    },
                    timeout=values["timeout"],
                )
            # todo: handle retries and max_concurrency
            if not values.get("async_client"):
                values["async_client"] = httpx.AsyncClient(
                    base_url=values["endpoint"],
                    headers={
                        "Content-Type": "application/json",
                        "Accept": "application/json",
                        "Authorization": f"Bearer {api_key_str}",
                    },
                    timeout=values["timeout"],
                )

            if values["temperature"] is not None and not 0 <= values["temperature"] <= 1:
                raise ValueError("temperature must be in the range [0.0, 1.0]")

            if values["top_p"] is not None and not 0 <= values["top_p"] <= 1:
                raise ValueError("top_p must be in the range [0.0, 1.0]")

            return values

        def _generate(
            self,
            messages: List[BaseMessage],
            stop: Optional[List[str]] = None,
            run_manager: Optional[CallbackManagerForLLMRun] = None,
            stream: Optional[bool] = None,
            **kwargs: Any,
        ) -> ChatResult:
            should_stream = stream if stream is not None else self.streaming
            if should_stream:
                stream_iter = self._stream(
                    messages, stop=stop, run_manager=run_manager, **kwargs
                )
                return generate_from_stream(stream_iter)

            message_dicts, params = self._create_message_dicts(messages, stop)
            params = {**params, **kwargs}
            response = self.completion_with_retry(
                messages=message_dicts, run_manager=run_manager, **params
            )
            return self._create_chat_result(response)

        def _create_chat_result(self, response: Dict) -> ChatResult:
            generations = []
            for res in response["choices"]:
                finish_reason = res.get("finish_reason")
                gen = ChatGeneration(
                    message=_convert_mistral_chat_message_to_message(res["message"]),
                    generation_info={"finish_reason": finish_reason},
                )
                generations.append(gen)
            token_usage = response.get("usage", {})

            llm_output = {"token_usage": token_usage, "model": self.model}
            return ChatResult(generations=generations, llm_output=llm_output)

        def _create_message_dicts(
            self, messages: List[BaseMessage], stop: Optional[List[str]]
        ) -> Tuple[List[Dict], Dict[str, Any]]:
            params = self._client_params
            if stop is not None or "stop" in params:
                if "stop" in params:
                    params.pop("stop")
                logger.warning(
                    "Parameter `stop` not yet supported (https://docs.mistral.ai/api)"
                )
            message_dicts = [_convert_message_to_mistral_chat_message(m) for m in messages]
            return message_dicts, params

        def _stream(
            self,
            messages: List[BaseMessage],
            stop: Optional[List[str]] = None,
            run_manager: Optional[CallbackManagerForLLMRun] = None,
            **kwargs: Any,
        ) -> Iterator[ChatGenerationChunk]:
            message_dicts, params = self._create_message_dicts(messages, stop)
            params = {**params, **kwargs, "stream": True}

            default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk
            for chunk in self.completion_with_retry(
                messages=message_dicts, run_manager=run_manager, **params
            ):
                if len(chunk["choices"]) == 0:
                    continue
                delta = chunk["choices"][0]["delta"]
                new_chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
                # make future chunks same type as first chunk
                default_chunk_class = new_chunk.__class__
                gen_chunk = ChatGenerationChunk(message=new_chunk)
                if run_manager:
                    run_manager.on_llm_new_token(
                        token=cast(str, new_chunk.content), chunk=gen_chunk
                    )
                yield gen_chunk

        async def _astream(
            self,
            messages: List[BaseMessage],
            stop: Optional[List[str]] = None,
            run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
            **kwargs: Any,
        ) -> AsyncIterator[ChatGenerationChunk]:
            message_dicts, params = self._create_message_dicts(messages, stop)
            params = {**params, **kwargs, "stream": True}

            default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk
            async for chunk in await acompletion_with_retry(
                self, messages=message_dicts, run_manager=run_manager, **params
            ):
                if len(chunk["choices"]) == 0:
                    continue
                delta = chunk["choices"][0]["delta"]
                new_chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
                # make future chunks same type as first chunk
                default_chunk_class = new_chunk.__class__
                gen_chunk = ChatGenerationChunk(message=new_chunk)
                if run_manager:
                    await run_manager.on_llm_new_token(
                        token=cast(str, new_chunk.content), chunk=gen_chunk
                    )
                yield gen_chunk

        async def _agenerate(
            self,
            messages: List[BaseMessage],
            stop: Optional[List[str]] = None,
            run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
            stream: Optional[bool] = None,
            **kwargs: Any,
        ) -> ChatResult:
            should_stream = stream if stream is not None else False
            if should_stream:
                stream_iter = self._astream(
                    messages=messages, stop=stop, run_manager=run_manager, **kwargs
                )
                return await agenerate_from_stream(stream_iter)

            message_dicts, params = self._create_message_dicts(messages, stop)
            params = {**params, **kwargs}
            response = await acompletion_with_retry(
                self, messages=message_dicts, run_manager=run_manager, **params
            )
            return self._create_chat_result(response)

        def bind_tools(
            self,
            tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
            **kwargs: Any,
        ) -> Runnable[LanguageModelInput, BaseMessage]:
            """Bind tool-like objects to this chat model.

            Assumes model is compatible with OpenAI tool-calling API.

            Args:
                tools: A list of tool definitions to bind to this chat model.
                    Can be  a dictionary, pydantic model, callable, or BaseTool. Pydantic
                    models, callables, and BaseTools will be automatically converted to
                    their schema dictionary representation.
                tool_choice: Which tool to require the model to call.
                    Must be the name of the single provided function or
                    "auto" to automatically determine which function to call
                    (if any), or a dict of the form:
                    {"type": "function", "function": {"name": <<tool_name>>}}.
                **kwargs: Any additional parameters to pass to the
                    :class:`~langchain.runnable.Runnable` constructor.
            """

            formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
            return super().bind(tools=formatted_tools, **kwargs)

        def with_structured_output(
            self,
            schema: Union[Dict, Type[BaseModel]],
            *,
            include_raw: bool = False,
            **kwargs: Any,
        ) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]:
            """Model wrapper that returns outputs formatted to match the given schema.

            Args:
                schema: The output schema as a dict or a Pydantic class. If a Pydantic class
                    then the model output will be an object of that class. If a dict then
                    the model output will be a dict. With a Pydantic class the returned
                    attributes will be validated, whereas with a dict they will not be. If
                    `method` is "function_calling" and `schema` is a dict, then the dict
                    must match the OpenAI function-calling spec.
                include_raw: If False then only the parsed structured output is returned. If
                    an error occurs during model output parsing it will be raised. If True
                    then both the raw model response (a BaseMessage) and the parsed model
                    response will be returned. If an error occurs during output parsing it
                    will be caught and returned as well. The final output is always a dict
                    with keys "raw", "parsed", and "parsing_error".

            Returns:
                A Runnable that takes any ChatModel input and returns as output:

                    If include_raw is True then a dict with keys:
                        raw: BaseMessage
                        parsed: Optional[_DictOrPydantic]
                        parsing_error: Optional[BaseException]

                    If include_raw is False then just _DictOrPydantic is returned,
                    where _DictOrPydantic depends on the schema:

                    If schema is a Pydantic class then _DictOrPydantic is the Pydantic
                        class.

                    If schema is a dict then _DictOrPydantic is a dict.

            Example: Function-calling, Pydantic schema (method="function_calling", include_raw=False):
                .. code-block:: python

                    from langchain_mistralai import ChatMistralAI
                    from langchain_core.pydantic_v1 import BaseModel

                    class AnswerWithJustification(BaseModel):
                        '''An answer to the user question along with justification for the answer.'''
                        answer: str
                        justification: str

                    llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
                    structured_llm = llm.with_structured_output(AnswerWithJustification)

                    structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")

                    # -> AnswerWithJustification(
                    #     answer='They weigh the same',
                    #     justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
                    # )

            Example: Function-calling, Pydantic schema (method="function_calling", include_raw=True):
                .. code-block:: python

                    from langchain_mistralai import ChatMistralAI
                    from langchain_core.pydantic_v1 import BaseModel

                    class AnswerWithJustification(BaseModel):
                        '''An answer to the user question along with justification for the answer.'''
                        answer: str
                        justification: str

                    llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
                    structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True)

                    structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
                    # -> {
                    #     'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
                    #     'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
                    #     'parsing_error': None
                    # }

            Example: Function-calling, dict schema (method="function_calling", include_raw=False):
                .. code-block:: python

                    from langchain_mistralai import ChatMistralAI
                    from langchain_core.pydantic_v1 import BaseModel
                    from langchain_core.utils.function_calling import convert_to_openai_tool

                    class AnswerWithJustification(BaseModel):
                        '''An answer to the user question along with justification for the answer.'''
                        answer: str
                        justification: str

                    dict_schema = convert_to_openai_tool(AnswerWithJustification)
                    llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
                    structured_llm = llm.with_structured_output(dict_schema)

                    structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
                    # -> {
                    #     'answer': 'They weigh the same',
                    #     'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
                    # }

            """  # noqa: E501
            if kwargs:
                raise ValueError(f"Received unsupported arguments {kwargs}")
            is_pydantic_schema = isinstance(schema, type) and issubclass(schema, BaseModel)
            llm = self.bind_tools([schema], tool_choice="any")
            if is_pydantic_schema:
                output_parser: OutputParserLike = PydanticToolsParser(
                    tools=[schema], first_tool_only=True
                )
            else:
                key_name = convert_to_openai_tool(schema)["function"]["name"]
                output_parser = JsonOutputKeyToolsParser(
                    key_name=key_name, first_tool_only=True
                )

            if include_raw:
                parser_assign = RunnablePassthrough.assign(
                    parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
                )
                parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
                parser_with_fallback = parser_assign.with_fallbacks(
                    [parser_none], exception_key="parsing_error"
                )
                return RunnableMap(raw=llm) | parser_with_fallback
            else:
                return llm | output_parser

        @property
        def _identifying_params(self) -> Dict[str, Any]:
            """Get the identifying parameters."""
            return self._default_params

        @property
        def _llm_type(self) -> str:
            """Return type of chat model."""
            return "mistralai-chat"

        @property
        def lc_secrets(self) -> Dict[str, str]:
            return {"mistral_api_key": "MISTRAL_API_KEY"}

        @classmethod
        def is_lc_serializable(cls) -> bool:
            """Return whether this model can be serialized by Langchain."""
            return True

        @classmethod
        def get_lc_namespace(cls) -> List[str]:
            """Get the namespace of the langchain object."""
            return ["langchain", "chat_models", "mistralai"]