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mistralai: Add langchain-mistralai partner package (#14783)
Co-authored-by: Chad Phillips <chad@apartmentlines.com>
This commit is contained in:
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GitHub
3
libs/partners/mistralai/langchain_mistralai/__init__.py
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3
libs/partners/mistralai/langchain_mistralai/__init__.py
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from langchain_mistralai.chat_models import ChatMistralAI
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__all__ = ["ChatMistralAI"]
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390
libs/partners/mistralai/langchain_mistralai/chat_models.py
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390
libs/partners/mistralai/langchain_mistralai/chat_models.py
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from __future__ import annotations
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import importlib.util
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import logging
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from typing import (
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Any,
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AsyncIterator,
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Callable,
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Dict,
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Iterator,
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List,
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Optional,
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Tuple,
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Type,
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Union,
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)
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from langchain_core.callbacks import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models.chat_models import (
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BaseChatModel,
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agenerate_from_stream,
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generate_from_stream,
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)
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from langchain_core.language_models.llms import create_base_retry_decorator
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from langchain_core.messages import (
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AIMessage,
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AIMessageChunk,
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BaseMessage,
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BaseMessageChunk,
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ChatMessage,
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ChatMessageChunk,
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HumanMessage,
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HumanMessageChunk,
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SystemMessage,
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SystemMessageChunk,
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)
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from langchain_core.outputs import (
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ChatGeneration,
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ChatGenerationChunk,
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ChatResult,
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)
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from langchain_core.pydantic_v1 import root_validator
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from langchain_core.utils import get_from_dict_or_env
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# TODO: Remove 'type: ignore' once mistralai has stubs or py.typed marker.
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from mistralai.async_client import MistralAsyncClient # type: ignore[import]
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from mistralai.client import MistralClient # type: ignore[import]
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from mistralai.constants import ( # type: ignore[import]
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ENDPOINT as DEFAULT_MISTRAL_ENDPOINT,
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)
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from mistralai.exceptions import ( # type: ignore[import]
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MistralAPIException,
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MistralConnectionException,
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MistralException,
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)
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from mistralai.models.chat_completion import ( # type: ignore[import]
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ChatCompletionResponse as MistralChatCompletionResponse,
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)
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from mistralai.models.chat_completion import ( # type: ignore[import]
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ChatMessage as MistralChatMessage,
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)
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from mistralai.models.chat_completion import ( # type: ignore[import]
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DeltaMessage as MistralDeltaMessage,
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)
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logger = logging.getLogger(__name__)
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def _create_retry_decorator(
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llm: ChatMistralAI,
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run_manager: Optional[
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Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
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] = None,
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) -> Callable[[Any], Any]:
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"""Returns a tenacity retry decorator, preconfigured to handle exceptions"""
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errors = [
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MistralException,
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MistralAPIException,
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MistralConnectionException,
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]
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return create_base_retry_decorator(
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error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
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)
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def _convert_mistral_chat_message_to_message(
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_message: MistralChatMessage,
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) -> BaseMessage:
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role = _message.role
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if role == "user":
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return HumanMessage(content=_message.content)
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elif role == "assistant":
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return AIMessage(content=_message.content)
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elif role == "system":
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return SystemMessage(content=_message.content)
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else:
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return ChatMessage(content=_message.content, role=role)
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async def acompletion_with_retry(
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llm: ChatMistralAI,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Any:
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"""Use tenacity to retry the async completion call."""
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retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
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@retry_decorator
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async def _completion_with_retry(**kwargs: Any) -> Any:
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client = MistralAsyncClient(
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api_key=llm.mistral_api_key,
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endpoint=llm.endpoint,
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max_retries=llm.max_retries,
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timeout=llm.timeout,
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max_concurrent_requests=llm.max_concurrent_requests,
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)
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stream = kwargs.pop("stream", False)
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if stream:
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return client.chat_stream(**kwargs)
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else:
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return await client.chat(**kwargs)
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return await _completion_with_retry(**kwargs)
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def _convert_delta_to_message_chunk(
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_obj: MistralDeltaMessage, default_class: Type[BaseMessageChunk]
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) -> BaseMessageChunk:
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role = getattr(_obj, "role")
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content = getattr(_obj, "content", "")
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if role == "user" or default_class == HumanMessageChunk:
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return HumanMessageChunk(content=content)
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elif role == "assistant" or default_class == AIMessageChunk:
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return AIMessageChunk(content=content)
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elif role == "system" or default_class == SystemMessageChunk:
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return SystemMessageChunk(content=content)
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elif role or default_class == ChatMessageChunk:
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return ChatMessageChunk(content=content, role=role)
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else:
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return default_class(content=content)
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def _convert_message_to_mistral_chat_message(
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message: BaseMessage,
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) -> MistralChatMessage:
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if isinstance(message, ChatMessage):
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mistral_message = MistralChatMessage(role=message.role, content=message.content)
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elif isinstance(message, HumanMessage):
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mistral_message = MistralChatMessage(role="user", content=message.content)
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elif isinstance(message, AIMessage):
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mistral_message = MistralChatMessage(role="assistant", content=message.content)
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elif isinstance(message, SystemMessage):
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mistral_message = MistralChatMessage(role="system", content=message.content)
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else:
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raise ValueError(f"Got unknown type {message}")
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return mistral_message
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class ChatMistralAI(BaseChatModel):
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"""A chat model that uses the MistralAI API."""
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client: Any #: :meta private:
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mistral_api_key: Optional[str] = None
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endpoint: str = DEFAULT_MISTRAL_ENDPOINT
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max_retries: int = 5
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timeout: int = 120
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max_concurrent_requests: int = 64
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model: str = "mistral-small"
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temperature: float = 0.7
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max_tokens: Optional[int] = None
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top_p: float = 1
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"""Decode using nucleus sampling: consider the smallest set of tokens whose
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probability sum is at least top_p. Must be in the closed interval [0.0, 1.0]."""
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random_seed: Optional[int] = None
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safe_mode: bool = False
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling the API."""
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defaults = {
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"model": self.model,
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"temperature": self.temperature,
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"max_tokens": self.max_tokens,
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"top_p": self.top_p,
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"random_seed": self.random_seed,
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"safe_mode": self.safe_mode,
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}
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filtered = {k: v for k, v in defaults.items() if v is not None}
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return filtered
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@property
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def _client_params(self) -> Dict[str, Any]:
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"""Get the parameters used for the client."""
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return self._default_params
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def completion_with_retry(
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self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any
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) -> Any:
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"""Use tenacity to retry the completion call."""
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retry_decorator = _create_retry_decorator(self, run_manager=run_manager)
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@retry_decorator
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def _completion_with_retry(**kwargs: Any) -> Any:
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stream = kwargs.pop("stream", False)
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if stream:
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return self.client.chat_stream(**kwargs)
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else:
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return self.client.chat(**kwargs)
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return _completion_with_retry(**kwargs)
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate api key, python package exists, temperature, and top_p."""
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mistralai_spec = importlib.util.find_spec("mistralai")
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if mistralai_spec is None:
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raise MistralException(
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"Could not find mistralai python package. "
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"Please install it with `pip install mistralai`"
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)
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values["mistral_api_key"] = get_from_dict_or_env(
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values, "mistral_api_key", "MISTRAL_API_KEY", default=""
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)
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values["client"] = MistralClient(
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api_key=values["mistral_api_key"],
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endpoint=values["endpoint"],
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max_retries=values["max_retries"],
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timeout=values["timeout"],
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)
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if values["temperature"] is not None and not 0 <= values["temperature"] <= 1:
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raise ValueError("temperature must be in the range [0.0, 1.0]")
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if values["top_p"] is not None and not 0 <= values["top_p"] <= 1:
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raise ValueError("top_p must be in the range [0.0, 1.0]")
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return values
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def _generate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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stream: Optional[bool] = None,
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**kwargs: Any,
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) -> ChatResult:
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should_stream = stream if stream is not None else False
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if should_stream:
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stream_iter = self._stream(
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messages, stop=stop, run_manager=run_manager, **kwargs
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)
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return generate_from_stream(stream_iter)
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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {**params, **kwargs}
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response = self.completion_with_retry(
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messages=message_dicts, run_manager=run_manager, **params
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)
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return self._create_chat_result(response)
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def _create_chat_result(
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self, response: MistralChatCompletionResponse
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) -> ChatResult:
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generations = []
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for res in response.choices:
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finish_reason = getattr(res, "finish_reason")
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if finish_reason:
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finish_reason = finish_reason.value
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gen = ChatGeneration(
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message=_convert_mistral_chat_message_to_message(res.message),
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generation_info={"finish_reason": finish_reason},
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)
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generations.append(gen)
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token_usage = getattr(response, "usage")
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token_usage = vars(token_usage) if token_usage else {}
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llm_output = {"token_usage": token_usage, "model": self.model}
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return ChatResult(generations=generations, llm_output=llm_output)
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def _create_message_dicts(
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self, messages: List[BaseMessage], stop: Optional[List[str]]
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) -> Tuple[List[MistralChatMessage], Dict[str, Any]]:
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params = self._client_params
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if stop is not None:
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if "stop" in params:
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raise ValueError("`stop` found in both the input and default params.")
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params["stop"] = stop
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message_dicts = [_convert_message_to_mistral_chat_message(m) for m in messages]
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return message_dicts, params
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def _stream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Iterator[ChatGenerationChunk]:
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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {**params, **kwargs, "stream": True}
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default_chunk_class = AIMessageChunk
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for chunk in self.completion_with_retry(
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messages=message_dicts, run_manager=run_manager, **params
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):
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if len(chunk.choices) == 0:
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continue
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delta = chunk.choices[0].delta
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if not delta.content:
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continue
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chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
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default_chunk_class = chunk.__class__
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yield ChatGenerationChunk(message=chunk)
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if run_manager:
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run_manager.on_llm_new_token(chunk.content)
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async def _astream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> AsyncIterator[ChatGenerationChunk]:
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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {**params, **kwargs, "stream": True}
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default_chunk_class = AIMessageChunk
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async for chunk in await acompletion_with_retry(
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self, messages=message_dicts, run_manager=run_manager, **params
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):
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if len(chunk.choices) == 0:
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continue
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delta = chunk.choices[0].delta
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if not delta.content:
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continue
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chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
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default_chunk_class = chunk.__class__
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yield ChatGenerationChunk(message=chunk)
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if run_manager:
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await run_manager.on_llm_new_token(chunk.content)
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async def _agenerate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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stream: Optional[bool] = None,
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**kwargs: Any,
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) -> ChatResult:
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should_stream = stream if stream is not None else False
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if should_stream:
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stream_iter = self._astream(
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messages=messages, stop=stop, run_manager=run_manager, **kwargs
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)
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return await agenerate_from_stream(stream_iter)
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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {**params, **kwargs}
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response = await acompletion_with_retry(
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self, messages=message_dicts, run_manager=run_manager, **params
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)
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return self._create_chat_result(response)
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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"""Get the identifying parameters."""
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return self._default_params
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@property
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def _llm_type(self) -> str:
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"""Return type of chat model."""
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return "mistralai-chat"
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@property
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def lc_secrets(self) -> Dict[str, str]:
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return {"mistral_api_key": "MISTRAL_API_KEY"}
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@classmethod
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def is_lc_serializable(cls) -> bool:
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"""Return whether this model can be serialized by Langchain."""
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return True
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@classmethod
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def get_lc_namespace(cls) -> List[str]:
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"""Get the namespace of the langchain object."""
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return ["langchain", "chat_models", "mistralai"]
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