repo_description: Private chat with local GPT with document, images, video, etc. 100% private, Apache 2.0. Supports oLLaMa, Mixtral, llama.cpp, and more. Demo: https://gpt.h2o.ai/ https://gpt-docs.h2o.ai/
repo_topics: ai chatgpt embeddings fedramp generative gpt gpt4all llama2 llm mixtral pdf private privategpt vectorstore
code: from __future__ import annotations
  
  import json
  import logging
  import uuid
  from operator import itemgetter
  from typing import (
      Any,
      AsyncContextManager,
      AsyncIterator,
      Callable,
      Dict,
      Iterator,
      List,
      Literal,
      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,
      #LangSmithParams,
      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 import (
      JsonOutputParser,
      PydanticOutputParser,
  )
  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 = {}
  
          raw_tool_calls = _delta.get("tool_calls")
          tool_call_chunks = []
  
          # JSON mode using function calling
          if raw_tool_calls and _delta['tool_calls'][-1]['function']['name'] == 'JSON':
              content = _delta['tool_calls'][-1]['function']['arguments']
          elif raw_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
          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
      tools: Optional[List] = None
      tool_choice: str = 'auto'
  
      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,
              "tools": self.tools,
              "tool_choice": self.tool_choice,
          }
          filtered = {k: v for k, v in defaults.items() if v is not None}
          return filtered
  
      # def _get_ls_params(
      #     self, stop: Optional[List[str]] = None, **kwargs: Any
      # ) -> LangSmithParams:
      #     """Get standard params for tracing."""
      #     params = self._get_invocation_params(stop=stop, **kwargs)
      #     ls_params = LangSmithParams(
      #         ls_provider="mistral",
      #         ls_model_name=self.model,
      #         ls_model_type="chat",
      #         ls_temperature=params.get("temperature", self.temperature),
      #     )
      #     if ls_max_tokens := params.get("max_tokens", self.max_tokens):
      #         ls_params["ls_max_tokens"] = ls_max_tokens
      #     if ls_stop := stop or params.get("stop", None):
      #         ls_params["ls_stop"] = ls_stop
      #     return ls_params
  
      @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 = []
          if 'choices' not in response:
              raise ValueError(f"Expected 'choices' in response, got {response}")
          for res in response["choices"]:
              finish_reason = res.get("finish_reason")
              # JSON mode using function calling
              if finish_reason == 'tool_calls' and res["message"]['tool_calls'][-1]['function']['name'] == 'JSON':
                  res['message']['content'] = res["message"]['tool_calls'][-1]['function']['arguments']
              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: Optional[Union[Dict, Type[BaseModel]]] = None,
          *,
          method: Literal["function_calling", "json_mode"] = "function_calling",
          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.
              method: The method for steering model generation, either "function_calling"
                  or "json_mode". If "function_calling" then the schema will be converted
                  to an OpenAI function and the returned model will make use of the
                  function-calling API. If "json_mode" then OpenAI's JSON mode will be
                  used. Note that if using "json_mode" then you must include instructions
                  for formatting the output into the desired schema into the model call.
              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.'
                  # }
  
          Example: JSON mode, Pydantic schema (method="json_mode", include_raw=True):
              .. code-block::
  
                  from langchain_mistralai import ChatMistralAI
                  from langchain_core.pydantic_v1 import BaseModel
  
                  class AnswerWithJustification(BaseModel):
                      answer: str
                      justification: str
  
                  llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
                  structured_llm = llm.with_structured_output(
                      AnswerWithJustification,
                      method="json_mode",
                      include_raw=True
                  )
  
                  structured_llm.invoke(
                      "Answer the following question. "
                      "Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n"
                      "What's heavier a pound of bricks or a pound of feathers?"
                  )
                  # -> {
                  #     'raw': AIMessage(content='{\n    "answer": "They are both the same weight.",\n    "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'),
                  #     'parsed': AnswerWithJustification(answer='They are both the same weight.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'),
                  #     'parsing_error': None
                  # }
  
          Example: JSON mode, no schema (schema=None, method="json_mode", include_raw=True):
              .. code-block::
  
                  from langchain_mistralai import ChatMistralAI
  
                  structured_llm = llm.with_structured_output(method="json_mode", include_raw=True)
  
                  structured_llm.invoke(
                      "Answer the following question. "
                      "Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n"
                      "What's heavier a pound of bricks or a pound of feathers?"
                  )
                  # -> {
                  #     'raw': AIMessage(content='{\n    "answer": "They are both the same weight.",\n    "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'),
                  #     'parsed': {
                  #         'answer': 'They are both the same weight.',
                  #         'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'
                  #     },
                  #     'parsing_error': None
                  # }
          """  # noqa: E501
          if kwargs:
              raise ValueError(f"Received unsupported arguments {kwargs}")
          is_pydantic_schema = isinstance(schema, type) and issubclass(schema, BaseModel)
          if method == "function_calling":
              if schema is None:
                  raise ValueError(
                      "schema must be specified when method is 'function_calling'. "
                      "Received None."
                  )
              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
                  )
          elif method == "json_mode":
              llm = self.bind(response_format={"type": "json_object"})
              output_parser = (
                  PydanticOutputParser(pydantic_object=schema)
                  if is_pydantic_schema
                  else JsonOutputParser()
              )
          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"]