from __future__ import annotations import hashlib import json import logging import os import re import ssl import uuid from collections.abc import Callable # noqa: TC003 from operator import itemgetter from typing import ( TYPE_CHECKING, Any, Literal, cast, ) import certifi 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, ModelProfile, ModelProfileRegistry, ) from langchain_core.language_models.chat_models import BaseChatModel, LangSmithParams 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.messages.tool import tool_call_chunk 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.runnables import Runnable, RunnableMap, RunnablePassthrough from langchain_core.tools import BaseTool from langchain_core.utils import get_pydantic_field_names, secret_from_env from langchain_core.utils.function_calling import convert_to_openai_tool from langchain_core.utils.pydantic import is_basemodel_subclass from langchain_core.utils.utils import _build_model_kwargs from pydantic import ( BaseModel, ConfigDict, Field, SecretStr, model_validator, ) from typing_extensions import Self from langchain_mistralai._compat import _convert_from_v1_to_mistral from langchain_mistralai.data._profiles import _PROFILES if TYPE_CHECKING: from collections.abc import AsyncIterator, Iterator, Sequence from contextlib import AbstractAsyncContextManager logger = logging.getLogger(__name__) # Mistral enforces a specific pattern for tool call IDs TOOL_CALL_ID_PATTERN = re.compile(r"^[a-zA-Z0-9]{9}$") # This SSL context is equivalent to the default `verify=True`. # https://www.python-httpx.org/advanced/ssl/#configuring-client-instances global_ssl_context = ssl.create_default_context(cafile=certifi.where()) _MODEL_PROFILES = cast("ModelProfileRegistry", _PROFILES) def _get_default_model_profile(model_name: str) -> ModelProfile: default = _MODEL_PROFILES.get(model_name) or {} return default.copy() def _create_retry_decorator( llm: ChatMistralAI, run_manager: AsyncCallbackManagerForLLMRun | CallbackManagerForLLMRun | None = None, ) -> Callable[[Any], Any]: """Return 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 _is_valid_mistral_tool_call_id(tool_call_id: str) -> bool: """Check if tool call ID is nine character string consisting of a-z, A-Z, 0-9.""" return bool(TOOL_CALL_ID_PATTERN.match(tool_call_id)) def _base62_encode(num: int) -> str: """Encode a number in base62 and ensures result is of a specified length.""" base62 = "0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ" if num == 0: return base62[0] arr = [] base = len(base62) while num: num, rem = divmod(num, base) arr.append(base62[rem]) arr.reverse() return "".join(arr) def _convert_tool_call_id_to_mistral_compatible(tool_call_id: str) -> str: """Convert a tool call ID to a Mistral-compatible format.""" if _is_valid_mistral_tool_call_id(tool_call_id): return tool_call_id hash_bytes = hashlib.sha256(tool_call_id.encode()).digest() hash_int = int.from_bytes(hash_bytes, byteorder="big") base62_str = _base62_encode(hash_int) if len(base62_str) >= 9: return base62_str[:9] return base62_str.rjust(9, "0") def _convert_mistral_chat_message_to_message( _message: dict, ) -> BaseMessage: role = _message["role"] if role != "assistant": msg = f"Expected role to be 'assistant', got {role}" raise ValueError(msg) # Mistral returns None for tool invocations content = _message.get("content", "") or "" 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"]: parsed["id"] = uuid.uuid4().hex[:] tool_calls.append(parsed) except Exception as e: invalid_tool_calls.append(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, response_metadata={"model_provider": "mistralai"}, ) 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") msg = ( f"Error response {response.status_code} " f"while fetching {response.url}: {error_message}" ) raise httpx.HTTPStatusError( msg, 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") msg = ( f"Error response {response.status_code} " f"while fetching {response.url}: {error_message}" ) raise httpx.HTTPStatusError( msg, request=response.request, response=response, ) async def _aiter_sse( event_source_mgr: AbstractAsyncContextManager[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: AsyncCallbackManagerForLLMRun | None = 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) 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_chunk_to_message_chunk( chunk: dict, default_class: type[BaseMessageChunk], index: int, index_type: str, output_version: str | None, ) -> tuple[BaseMessageChunk, int, str]: _choice = chunk["choices"][0] _delta = _choice["delta"] role = _delta.get("role") content = _delta.get("content") or "" if output_version == "v1" and isinstance(content, str): content = [{"type": "text", "text": content}] if isinstance(content, list): for block in content: if isinstance(block, dict): if "type" in block and block["type"] != index_type: index_type = block["type"] index = index + 1 if "index" not in block: block["index"] = index if block.get("type") == "thinking" and isinstance( block.get("thinking"), list ): for sub_block in block["thinking"]: if isinstance(sub_block, dict) and "index" not in sub_block: sub_block["index"] = 0 if role == "user" or default_class == HumanMessageChunk: return HumanMessageChunk(content=content), index, index_type if role == "assistant" or default_class == AIMessageChunk: additional_kwargs: dict = {} response_metadata = {} 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( tool_call_chunk( 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 = [] if token_usage := chunk.get("usage"): usage_metadata = { "input_tokens": token_usage.get("prompt_tokens", 0), "output_tokens": token_usage.get("completion_tokens", 0), "total_tokens": token_usage.get("total_tokens", 0), } else: usage_metadata = None if _choice.get("finish_reason") is not None and isinstance( chunk.get("model"), str ): response_metadata["model_name"] = chunk["model"] response_metadata["finish_reason"] = _choice["finish_reason"] return ( AIMessageChunk( content=content, additional_kwargs=additional_kwargs, tool_call_chunks=tool_call_chunks, # type: ignore[arg-type] usage_metadata=usage_metadata, # type: ignore[arg-type] response_metadata={"model_provider": "mistralai", **response_metadata}, ), index, index_type, ) if role == "system" or default_class == SystemMessageChunk: return SystemMessageChunk(content=content), index, index_type if role or default_class == ChatMessageChunk: return ChatMessageChunk(content=content, role=role), index, index_type return default_class(content=content), index, index_type # type: ignore[call-arg] 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"], ensure_ascii=False), } } if _id := tool_call.get("id"): result["id"] = _convert_tool_call_id_to_mistral_compatible(_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"] = _convert_tool_call_id_to_mistral_compatible(_id) return result def _clean_block(block: dict) -> dict: # Remove "index" key added for message aggregation in langchain-core new_block = {k: v for k, v in block.items() if k != "index"} if block.get("type") == "thinking" and isinstance(block.get("thinking"), list): new_block["thinking"] = [ ( {k: v for k, v in sb.items() if k != "index"} if isinstance(sb, dict) and "index" in sb else sb ) for sb in block["thinking"] ] return new_block def _convert_message_to_mistral_chat_message( message: BaseMessage, ) -> dict: if isinstance(message, ChatMessage): return {"role": message.role, "content": message.content} if isinstance(message, HumanMessage): return {"role": "user", "content": message.content} if isinstance(message, AIMessage): message_dict: dict[str, Any] = {"role": "assistant"} tool_calls: list = [] if message.tool_calls or message.invalid_tool_calls: if message.tool_calls: tool_calls.extend( _format_tool_call_for_mistral(tool_call) for tool_call in message.tool_calls ) if message.invalid_tool_calls: tool_calls.extend( _format_invalid_tool_call_for_mistral(invalid_tool_call) for invalid_tool_call in message.invalid_tool_calls ) 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 # Message content # Translate v1 content if message.response_metadata.get("output_version") == "v1": content = _convert_from_v1_to_mistral( message.content_blocks, message.response_metadata.get("model_provider") ) else: content = message.content if tool_calls and 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. content = "" elif isinstance(content, list): content = [ _clean_block(block) if isinstance(block, dict) and "index" in block else block for block in content ] else: content = message.content # if any blocks are dicts, cast strings to text blocks if any(isinstance(block, dict) for block in content): content = [ block if isinstance(block, dict) else {"type": "text", "text": block} for block in content ] message_dict["content"] = content if "prefix" in message.additional_kwargs: message_dict["prefix"] = message.additional_kwargs["prefix"] return message_dict if isinstance(message, SystemMessage): return {"role": "system", "content": message.content} if isinstance(message, ToolMessage): return { "role": "tool", "content": message.content, "name": message.name, "tool_call_id": _convert_tool_call_id_to_mistral_compatible( message.tool_call_id ), } msg = f"Got unknown type {message}" raise ValueError(msg) class ChatMistralAI(BaseChatModel): """A chat model that uses the Mistral AI API.""" # The type for client and async_client is ignored because the type is not # an Optional after the model is initialized and the model_validator # is run. client: httpx.Client = Field( # type: ignore[assignment] # : meta private: default=None, exclude=True ) async_client: httpx.AsyncClient = Field( # type: ignore[assignment] # : meta private: default=None, exclude=True ) mistral_api_key: SecretStr | None = Field( alias="api_key", default_factory=secret_from_env("MISTRAL_API_KEY", default=None), ) endpoint: str | None = Field(default=None, alias="base_url") 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: int | None = 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: int | None = None safe_mode: bool | None = None streaming: bool = False model_kwargs: dict[str, Any] = Field(default_factory=dict) """Holds any invocation parameters not explicitly specified.""" model_config = ConfigDict( populate_by_name=True, arbitrary_types_allowed=True, ) @model_validator(mode="before") @classmethod def build_extra(cls, values: dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) return _build_model_kwargs(values, all_required_field_names) @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, **self.model_kwargs, } return {k: v for k, v in defaults.items() if v is not None} def _get_ls_params( self, stop: list[str] | None = 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=params.get("model", 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: CallbackManagerForLLMRun | None = 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() response = self.client.post(url="/chat/completions", json=kwargs) _raise_on_error(response) return response.json() return _completion_with_retry(**kwargs) def _combine_llm_outputs(self, llm_outputs: list[dict | None]) -> 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 return {"token_usage": overall_token_usage, "model_name": self.model} @model_validator(mode="after") def validate_environment(self) -> Self: """Validate api key, python package exists, temperature, and top_p.""" if isinstance(self.mistral_api_key, SecretStr): api_key_str: str | None = self.mistral_api_key.get_secret_value() else: api_key_str = self.mistral_api_key # TODO: handle retries base_url_str = ( self.endpoint or os.environ.get("MISTRAL_BASE_URL") or "https://api.mistral.ai/v1" ) self.endpoint = base_url_str if not self.client: self.client = httpx.Client( base_url=base_url_str, headers={ "Content-Type": "application/json", "Accept": "application/json", "Authorization": f"Bearer {api_key_str}", }, timeout=self.timeout, verify=global_ssl_context, ) # TODO: handle retries and max_concurrency if not self.async_client: self.async_client = httpx.AsyncClient( base_url=base_url_str, headers={ "Content-Type": "application/json", "Accept": "application/json", "Authorization": f"Bearer {api_key_str}", }, timeout=self.timeout, verify=global_ssl_context, ) if self.temperature is not None and not 0 <= self.temperature <= 1: msg = "temperature must be in the range [0.0, 1.0]" raise ValueError(msg) if self.top_p is not None and not 0 <= self.top_p <= 1: msg = "top_p must be in the range [0.0, 1.0]" raise ValueError(msg) return self @model_validator(mode="after") def _set_model_profile(self) -> Self: """Set model profile if not overridden.""" if self.profile is None: self.profile = _get_default_model_profile(self.model) return self def _generate( self, messages: list[BaseMessage], stop: list[str] | None = None, run_manager: CallbackManagerForLLMRun | None = None, stream: bool | None = None, # noqa: FBT001 **kwargs: Any, ) -> ChatResult: 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 = [] token_usage = response.get("usage", {}) for res in response["choices"]: finish_reason = res.get("finish_reason") message = _convert_mistral_chat_message_to_message(res["message"]) if token_usage and isinstance(message, AIMessage): message.usage_metadata = { "input_tokens": token_usage.get("prompt_tokens", 0), "output_tokens": token_usage.get("completion_tokens", 0), "total_tokens": token_usage.get("total_tokens", 0), } gen = ChatGeneration( message=message, generation_info={"finish_reason": finish_reason}, ) generations.append(gen) llm_output = { "token_usage": token_usage, "model_name": self.model, "model": self.model, # Backwards compatibility } return ChatResult(generations=generations, llm_output=llm_output) def _create_message_dicts( self, messages: list[BaseMessage], stop: list[str] | None ) -> 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: list[str] | None = None, run_manager: CallbackManagerForLLMRun | None = 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 index = -1 index_type = "" for chunk in self.completion_with_retry( messages=message_dicts, run_manager=run_manager, **params ): if len(chunk.get("choices", [])) == 0: continue new_chunk, index, index_type = _convert_chunk_to_message_chunk( chunk, default_chunk_class, index, index_type, self.output_version ) # 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: list[str] | None = None, run_manager: AsyncCallbackManagerForLLMRun | None = 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 index = -1 index_type = "" async for chunk in await acompletion_with_retry( self, messages=message_dicts, run_manager=run_manager, **params ): if len(chunk.get("choices", [])) == 0: continue new_chunk, index, index_type = _convert_chunk_to_message_chunk( chunk, default_chunk_class, index, index_type, self.output_version ) # 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: list[str] | None = None, run_manager: AsyncCallbackManagerForLLMRun | None = None, stream: bool | None = None, # noqa: FBT001 **kwargs: Any, ) -> ChatResult: 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[dict[str, Any] | type | Callable | BaseTool], tool_choice: dict | str | Literal["auto", "any"] | None = None, # noqa: PYI051 **kwargs: Any, ) -> Runnable[LanguageModelInput, AIMessage]: """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. Supports any tool definition handled by [`convert_to_openai_tool`][langchain_core.utils.function_calling.convert_to_openai_tool]. 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": <>}}. kwargs: Any additional parameters are passed directly to `self.bind(**kwargs)`. """ # noqa: E501 formatted_tools = [convert_to_openai_tool(tool) for tool in tools] if tool_choice: tool_names = [] for tool in formatted_tools: if ("function" in tool and (name := tool["function"].get("name"))) or ( name := tool.get("name") ): tool_names.append(name) else: pass if tool_choice in tool_names: kwargs["tool_choice"] = { "type": "function", "function": {"name": tool_choice}, } else: kwargs["tool_choice"] = tool_choice return super().bind(tools=formatted_tools, **kwargs) def with_structured_output( self, schema: dict | type | None = None, *, method: Literal[ "function_calling", "json_mode", "json_schema" ] = "function_calling", include_raw: bool = False, **kwargs: Any, ) -> Runnable[LanguageModelInput, dict | BaseModel]: r"""Model wrapper that returns outputs formatted to match the given schema. Args: schema: The output schema. Can be passed in as: - An OpenAI function/tool schema, - A JSON Schema, - A `TypedDict` class, - Or a Pydantic class. If `schema` is a Pydantic class then the model output will be a Pydantic instance of that class, and the model-generated fields will be validated by the Pydantic class. Otherwise the model output will be a dict and will not be validated. See `langchain_core.utils.function_calling.convert_to_openai_tool` for more on how to properly specify types and descriptions of schema fields when specifying a Pydantic or `TypedDict` class. method: The method for steering model generation, one of: - `'function_calling'`: Uses Mistral's [function-calling feature](https://docs.mistral.ai/capabilities/function_calling/). - `'json_schema'`: Uses Mistral's [structured output feature](https://docs.mistral.ai/capabilities/structured-output/custom_structured_output/). - `'json_mode'`: Uses Mistral's [JSON mode](https://docs.mistral.ai/capabilities/structured-output/json_mode/). Note that if using JSON mode then you must include instructions for formatting the output into the desired schema into the model call. !!! warning "Behavior changed in `langchain-mistralai` 0.2.5" Added method="json_schema" 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'`. kwargs: Any additional parameters are passed directly to `self.bind(**kwargs)`. This is useful for passing in parameters such as `tool_choice` or `tools` to control which tool the model should call, or to pass in parameters such as `stop` to control when the model should stop generating output. Returns: A `Runnable` that takes same inputs as a `langchain_core.language_models.chat.BaseChatModel`. If `include_raw` is `False` and `schema` is a Pydantic class, `Runnable` outputs an instance of `schema` (i.e., a Pydantic object). Otherwise, if `include_raw` is `False` then `Runnable` outputs a `dict`. If `include_raw` is `True`, then `Runnable` outputs a `dict` with keys: - `'raw'`: `BaseMessage` - `'parsed'`: `None` if there was a parsing error, otherwise the type depends on the `schema` as described above. - `'parsing_error'`: `BaseException | None` Example: schema=Pydantic class, method="function_calling", include_raw=False: ```python from typing import Optional from langchain_mistralai import ChatMistralAI from pydantic import BaseModel, Field class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' answer: str # If we provide default values and/or descriptions for fields, these will be passed # to the model. This is an important part of improving a model's ability to # correctly return structured outputs. justification: str | None = Field( default=None, description="A justification for the answer." ) model = ChatMistralAI(model="mistral-large-latest", temperature=0) structured_model = model.with_structured_output(AnswerWithJustification) structured_model.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: schema=Pydantic class, method="function_calling", include_raw=True: ```python from langchain_mistralai import ChatMistralAI from pydantic import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' answer: str justification: str model = ChatMistralAI(model="mistral-large-latest", temperature=0) structured_model = model.with_structured_output( AnswerWithJustification, include_raw=True ) structured_model.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: schema=TypedDict class, method="function_calling", include_raw=False: ```python from typing_extensions import Annotated, TypedDict from langchain_mistralai import ChatMistralAI class AnswerWithJustification(TypedDict): '''An answer to the user question along with justification for the answer.''' answer: str justification: Annotated[ str | None, None, "A justification for the answer." ] model = ChatMistralAI(model="mistral-large-latest", temperature=0) structured_model = model.with_structured_output(AnswerWithJustification) structured_model.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: schema=OpenAI function schema, method="function_calling", include_raw=False: ```python from langchain_mistralai import ChatMistralAI oai_schema = { 'name': 'AnswerWithJustification', 'description': 'An answer to the user question along with justification for the answer.', 'parameters': { 'type': 'object', 'properties': { 'answer': {'type': 'string'}, 'justification': {'description': 'A justification for the answer.', 'type': 'string'} }, 'required': ['answer'] } model = ChatMistralAI(model="mistral-large-latest", temperature=0) structured_model = model.with_structured_output(oai_schema) structured_model.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: schema=Pydantic class, method="json_mode", include_raw=True: ```python from langchain_mistralai import ChatMistralAI from pydantic import BaseModel class AnswerWithJustification(BaseModel): answer: str justification: str model = ChatMistralAI(model="mistral-large-latest", temperature=0) structured_model = model.with_structured_output( AnswerWithJustification, method="json_mode", include_raw=True ) structured_model.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: schema=None, method="json_mode", include_raw=True: ```python structured_model = model.with_structured_output( method="json_mode", include_raw=True ) structured_model.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 _ = kwargs.pop("strict", None) if kwargs: msg = f"Received unsupported arguments {kwargs}" raise ValueError(msg) is_pydantic_schema = isinstance(schema, type) and is_basemodel_subclass(schema) if method == "function_calling": if schema is None: msg = ( "schema must be specified when method is 'function_calling'. " "Received None." ) raise ValueError(msg) # TODO: Update to pass in tool name as tool_choice if/when Mistral supports # specifying a tool. llm = self.bind_tools( [schema], tool_choice="any", ls_structured_output_format={ "kwargs": {"method": "function_calling"}, "schema": schema, }, ) if is_pydantic_schema: output_parser: OutputParserLike = PydanticToolsParser( tools=[schema], # type: ignore[list-item] first_tool_only=True, # type: ignore[list-item] ) 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"}, ls_structured_output_format={ "kwargs": { # this is correct - name difference with mistral api "method": "json_mode" }, "schema": schema, }, ) output_parser = ( PydanticOutputParser(pydantic_object=schema) # type: ignore[type-var, arg-type] if is_pydantic_schema else JsonOutputParser() ) elif method == "json_schema": if schema is None: msg = ( "schema must be specified when method is 'json_schema'. " "Received None." ) raise ValueError(msg) response_format = _convert_to_openai_response_format(schema, strict=True) llm = self.bind( response_format=response_format, ls_structured_output_format={ "kwargs": {"method": "json_schema"}, "schema": schema, }, ) output_parser = ( PydanticOutputParser(pydantic_object=schema) # type: ignore[arg-type] 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 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. Returns: `["langchain", "chat_models", "mistralai"]` """ return ["langchain", "chat_models", "mistralai"] def _convert_to_openai_response_format( schema: dict[str, Any] | type, *, strict: bool | None = None ) -> dict: """Perform same op as in ChatOpenAI, but do not pass through Pydantic BaseModels.""" if ( isinstance(schema, dict) and "json_schema" in schema and schema.get("type") == "json_schema" ): response_format = schema elif isinstance(schema, dict) and "name" in schema and "schema" in schema: response_format = {"type": "json_schema", "json_schema": schema} else: if strict is None: if isinstance(schema, dict) and isinstance(schema.get("strict"), bool): strict = schema["strict"] else: strict = False function = convert_to_openai_tool(schema, strict=strict)["function"] function["schema"] = function.pop("parameters") response_format = {"type": "json_schema", "json_schema": function} if ( strict is not None and strict is not response_format["json_schema"].get("strict") and isinstance(schema, dict) ): msg = ( f"Output schema already has 'strict' value set to " f"{schema['json_schema']['strict']} but 'strict' also passed in to " f"with_structured_output as {strict}. Please make sure that " f"'strict' is only specified in one place." ) raise ValueError(msg) return response_format