diff --git a/libs/partners/mistralai/langchain_mistralai/chat_models.py b/libs/partners/mistralai/langchain_mistralai/chat_models.py index d4cd631b9b4f34..6d9fbbb18d2ebb 100644 --- a/libs/partners/mistralai/langchain_mistralai/chat_models.py +++ b/libs/partners/mistralai/langchain_mistralai/chat_models.py @@ -2,6 +2,7 @@ import importlib.util import logging +from operator import itemgetter from typing import ( Any, AsyncIterator, @@ -10,15 +11,19 @@ Iterator, List, Optional, + Sequence, Tuple, Type, Union, + cast, ) +from langchain_core._api import beta 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, @@ -36,19 +41,22 @@ HumanMessageChunk, SystemMessage, SystemMessageChunk, + ToolMessage, ) -from langchain_core.outputs import ( - ChatGeneration, - ChatGenerationChunk, - ChatResult, +from langchain_core.output_parsers.base import OutputParserLike +from langchain_core.output_parsers.openai_tools import ( + JsonOutputKeyToolsParser, + PydanticToolsParser, ) -from langchain_core.pydantic_v1 import Field, SecretStr, root_validator +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 from mistralai.async_client import MistralAsyncClient from mistralai.client import MistralClient -from mistralai.constants import ( - ENDPOINT as DEFAULT_MISTRAL_ENDPOINT, -) +from mistralai.constants import ENDPOINT as DEFAULT_MISTRAL_ENDPOINT from mistralai.exceptions import ( MistralAPIException, MistralConnectionException, @@ -57,12 +65,8 @@ from mistralai.models.chat_completion import ( ChatCompletionResponse as MistralChatCompletionResponse, ) -from mistralai.models.chat_completion import ( - ChatMessage as MistralChatMessage, -) -from mistralai.models.chat_completion import ( - DeltaMessage as MistralDeltaMessage, -) +from mistralai.models.chat_completion import ChatMessage as MistralChatMessage +from mistralai.models.chat_completion import DeltaMessage as MistralDeltaMessage logger = logging.getLogger(__name__) @@ -89,14 +93,22 @@ def _convert_mistral_chat_message_to_message( _message: MistralChatMessage, ) -> BaseMessage: role = _message.role + content = cast(Union[str, List], _message.content) if role == "user": - return HumanMessage(content=_message.content) + return HumanMessage(content=content) elif role == "assistant": - return AIMessage(content=_message.content) + additional_kwargs: Dict = {} + if hasattr(_message, "tool_calls") and getattr(_message, "tool_calls"): + additional_kwargs["tool_calls"] = [ + tc.model_dump() for tc in getattr(_message, "tool_calls") + ] + return AIMessage(content=content, additional_kwargs=additional_kwargs) elif role == "system": - return SystemMessage(content=_message.content) + return SystemMessage(content=content) + elif role == "tool": + return ToolMessage(content=content, name=_message.name) # type: ignore[attr-defined] else: - return ChatMessage(content=_message.content, role=role) + return ChatMessage(content=content, role=role) async def acompletion_with_retry( @@ -119,14 +131,19 @@ async def _completion_with_retry(**kwargs: Any) -> Any: def _convert_delta_to_message_chunk( - _obj: MistralDeltaMessage, default_class: Type[BaseMessageChunk] + _delta: MistralDeltaMessage, default_class: Type[BaseMessageChunk] ) -> BaseMessageChunk: - role = getattr(_obj, "role") - content = getattr(_obj, "content", "") + role = getattr(_delta, "role") + content = getattr(_delta, "content", "") if role == "user" or default_class == HumanMessageChunk: return HumanMessageChunk(content=content) elif role == "assistant" or default_class == AIMessageChunk: - return AIMessageChunk(content=content) + additional_kwargs: Dict = {} + if hasattr(_delta, "tool_calls") and getattr(_delta, "tool_calls"): + additional_kwargs["tool_calls"] = [ + tc.model_dump() for tc in getattr(_delta, "tool_calls") + ] + return AIMessageChunk(content=content, additional_kwargs=additional_kwargs) elif role == "system" or default_class == SystemMessageChunk: return SystemMessageChunk(content=content) elif role or default_class == ChatMessageChunk: @@ -143,9 +160,26 @@ def _convert_message_to_mistral_chat_message( elif isinstance(message, HumanMessage): mistral_message = MistralChatMessage(role="user", content=message.content) elif isinstance(message, AIMessage): - mistral_message = MistralChatMessage(role="assistant", content=message.content) + if "tool_calls" in message.additional_kwargs: + from mistralai.models.chat_completion import ( # type: ignore[attr-defined] + ToolCall as MistralToolCall, + ) + + tool_calls = [ + MistralToolCall.model_validate(tc) + for tc in message.additional_kwargs["tool_calls"] + ] + else: + tool_calls = None + mistral_message = MistralChatMessage( + role="assistant", content=message.content, tool_calls=tool_calls + ) elif isinstance(message, SystemMessage): mistral_message = MistralChatMessage(role="system", content=message.content) + elif isinstance(message, ToolMessage): + mistral_message = MistralChatMessage( + role="tool", content=message.content, name=message.name + ) else: raise ValueError(f"Got unknown type {message}") return mistral_message @@ -368,6 +402,163 @@ async def _agenerate( ) 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": <>}}. + **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) + + @beta() + 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.""" diff --git a/libs/partners/mistralai/poetry.lock b/libs/partners/mistralai/poetry.lock index 92478c94e0e20f..9aa446e602fff4 100644 --- a/libs/partners/mistralai/poetry.lock +++ b/libs/partners/mistralai/poetry.lock @@ -16,13 +16,13 @@ typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""} [[package]] name = "anyio" -version = "4.2.0" +version = "4.3.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false python-versions = ">=3.8" files = [ - {file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"}, - {file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"}, + {file = "anyio-4.3.0-py3-none-any.whl", hash = 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