Source code for langchain_community.chat_models.litellm

"""Wrapper around LiteLLM's model I/O library."""
from __future__ import annotations

import logging
from typing import (
    Any,
    AsyncIterator,
    Callable,
    Dict,
    Iterator,
    List,
    Mapping,
    Optional,
    Tuple,
    Type,
    Union,
)

from langchain_core.callbacks import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain_core.language_models.chat_models import (
    BaseChatModel,
    agenerate_from_stream,
    generate_from_stream,
)
from langchain_core.language_models.llms import create_base_retry_decorator
from langchain_core.messages import (
    AIMessage,
    AIMessageChunk,
    BaseMessage,
    BaseMessageChunk,
    ChatMessage,
    ChatMessageChunk,
    FunctionMessage,
    FunctionMessageChunk,
    HumanMessage,
    HumanMessageChunk,
    SystemMessage,
    SystemMessageChunk,
)
from langchain_core.outputs import (
    ChatGeneration,
    ChatGenerationChunk,
    ChatResult,
)
from langchain_core.pydantic_v1 import Field, root_validator
from langchain_core.utils import get_from_dict_or_env

logger = logging.getLogger(__name__)


[docs]class ChatLiteLLMException(Exception): """Error with the `LiteLLM I/O` library"""
def _create_retry_decorator( llm: ChatLiteLLM, run_manager: Optional[ Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun] ] = None, ) -> Callable[[Any], Any]: """Returns a tenacity retry decorator, preconfigured to handle PaLM exceptions""" import litellm errors = [ litellm.Timeout, litellm.APIError, litellm.APIConnectionError, litellm.RateLimitError, ] return create_base_retry_decorator( error_types=errors, max_retries=llm.max_retries, run_manager=run_manager ) def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage: role = _dict["role"] if role == "user": return HumanMessage(content=_dict["content"]) elif role == "assistant": # Fix for azure # Also OpenAI returns None for tool invocations content = _dict.get("content", "") or "" if _dict.get("function_call"): additional_kwargs = {"function_call": dict(_dict["function_call"])} else: additional_kwargs = {} return AIMessage(content=content, additional_kwargs=additional_kwargs) elif role == "system": return SystemMessage(content=_dict["content"]) elif role == "function": return FunctionMessage(content=_dict["content"], name=_dict["name"]) else: return ChatMessage(content=_dict["content"], role=role)
[docs]async def acompletion_with_retry( llm: ChatLiteLLM, 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: # Use OpenAI's async api https://github.com/openai/openai-python#async-api return await llm.client.acreate(**kwargs) return await _completion_with_retry(**kwargs)
def _convert_delta_to_message_chunk( _dict: Mapping[str, Any], default_class: Type[BaseMessageChunk] ) -> BaseMessageChunk: role = _dict.get("role") content = _dict.get("content") or "" if _dict.get("function_call"): additional_kwargs = {"function_call": dict(_dict["function_call"])} else: additional_kwargs = {} if role == "user" or default_class == HumanMessageChunk: return HumanMessageChunk(content=content) elif role == "assistant" or default_class == AIMessageChunk: return AIMessageChunk(content=content, additional_kwargs=additional_kwargs) elif role == "system" or default_class == SystemMessageChunk: return SystemMessageChunk(content=content) elif role == "function" or default_class == FunctionMessageChunk: return FunctionMessageChunk(content=content, name=_dict["name"]) elif role or default_class == ChatMessageChunk: return ChatMessageChunk(content=content, role=role) # type: ignore[arg-type] else: return default_class(content=content) # type: ignore[call-arg] def _convert_message_to_dict(message: BaseMessage) -> dict: if isinstance(message, ChatMessage): message_dict = {"role": message.role, "content": message.content} elif isinstance(message, HumanMessage): message_dict = {"role": "user", "content": message.content} elif isinstance(message, AIMessage): message_dict = {"role": "assistant", "content": message.content} if "function_call" in message.additional_kwargs: message_dict["function_call"] = message.additional_kwargs["function_call"] elif isinstance(message, SystemMessage): message_dict = {"role": "system", "content": message.content} elif isinstance(message, FunctionMessage): message_dict = { "role": "function", "content": message.content, "name": message.name, } else: raise ValueError(f"Got unknown type {message}") if "name" in message.additional_kwargs: message_dict["name"] = message.additional_kwargs["name"] return message_dict
[docs]class ChatLiteLLM(BaseChatModel): """Chat model that uses the LiteLLM API.""" client: Any #: :meta private: model: str = "gpt-3.5-turbo" model_name: Optional[str] = None """Model name to use.""" openai_api_key: Optional[str] = None azure_api_key: Optional[str] = None anthropic_api_key: Optional[str] = None replicate_api_key: Optional[str] = None cohere_api_key: Optional[str] = None openrouter_api_key: Optional[str] = None streaming: bool = False api_base: Optional[str] = None organization: Optional[str] = None custom_llm_provider: Optional[str] = None request_timeout: Optional[Union[float, Tuple[float, float]]] = None temperature: Optional[float] = 1 model_kwargs: Dict[str, Any] = Field(default_factory=dict) """Run inference with this temperature. Must by in the closed interval [0.0, 1.0].""" top_p: Optional[float] = None """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].""" top_k: Optional[int] = None """Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.""" n: int = 1 """Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.""" max_tokens: int = 256 max_retries: int = 6 @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling OpenAI API.""" set_model_value = self.model if self.model_name is not None: set_model_value = self.model_name return { "model": set_model_value, "force_timeout": self.request_timeout, "max_tokens": self.max_tokens, "stream": self.streaming, "n": self.n, "temperature": self.temperature, "custom_llm_provider": self.custom_llm_provider, **self.model_kwargs, } @property def _client_params(self) -> Dict[str, Any]: """Get the parameters used for the openai client.""" set_model_value = self.model if self.model_name is not None: set_model_value = self.model_name self.client.api_base = self.api_base self.client.organization = self.organization creds: Dict[str, Any] = { "model": set_model_value, "force_timeout": self.request_timeout, "api_base": self.api_base, } return {**self._default_params, **creds}
[docs] 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: return self.client.completion(**kwargs) return _completion_with_retry(**kwargs)
@root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate api key, python package exists, temperature, top_p, and top_k.""" try: import litellm except ImportError: raise ChatLiteLLMException( "Could not import litellm python package. " "Please install it with `pip install litellm`" ) values["openai_api_key"] = get_from_dict_or_env( values, "openai_api_key", "OPENAI_API_KEY", default="" ) values["azure_api_key"] = get_from_dict_or_env( values, "azure_api_key", "AZURE_API_KEY", default="" ) values["anthropic_api_key"] = get_from_dict_or_env( values, "anthropic_api_key", "ANTHROPIC_API_KEY", default="" ) values["replicate_api_key"] = get_from_dict_or_env( values, "replicate_api_key", "REPLICATE_API_KEY", default="" ) values["openrouter_api_key"] = get_from_dict_or_env( values, "openrouter_api_key", "OPENROUTER_API_KEY", default="" ) values["cohere_api_key"] = get_from_dict_or_env( values, "cohere_api_key", "COHERE_API_KEY", default="" ) values["huggingface_api_key"] = get_from_dict_or_env( values, "huggingface_api_key", "HUGGINGFACE_API_KEY", default="" ) values["together_ai_api_key"] = get_from_dict_or_env( values, "together_ai_api_key", "TOGETHERAI_API_KEY", default="" ) values["client"] = litellm 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]") if values["top_k"] is not None and values["top_k"] <= 0: raise ValueError("top_k must be positive") 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: Mapping[str, Any]) -> ChatResult: generations = [] for res in response["choices"]: message = _convert_dict_to_message(res["message"]) gen = ChatGeneration( message=message, generation_info=dict(finish_reason=res.get("finish_reason")), ) generations.append(gen) token_usage = response.get("usage", {}) set_model_value = self.model if self.model_name is not None: set_model_value = self.model_name llm_output = {"token_usage": token_usage, "model": set_model_value} return ChatResult(generations=generations, llm_output=llm_output) def _create_message_dicts( self, messages: List[BaseMessage], stop: Optional[List[str]] ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]: params = self._client_params if stop is not None: if "stop" in params: raise ValueError("`stop` found in both the input and default params.") params["stop"] = stop message_dicts = [_convert_message_to_dict(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 = 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"] chunk = _convert_delta_to_message_chunk(delta, default_chunk_class) default_chunk_class = chunk.__class__ cg_chunk = ChatGenerationChunk(message=chunk) if run_manager: run_manager.on_llm_new_token(chunk.content, chunk=cg_chunk) yield cg_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 = 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"] chunk = _convert_delta_to_message_chunk(delta, default_chunk_class) default_chunk_class = chunk.__class__ cg_chunk = ChatGenerationChunk(message=chunk) if run_manager: await run_manager.on_llm_new_token(chunk.content, chunk=cg_chunk) yield cg_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 self.streaming 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) @property def _identifying_params(self) -> Dict[str, Any]: """Get the identifying parameters.""" set_model_value = self.model if self.model_name is not None: set_model_value = self.model_name return { "model": set_model_value, "temperature": self.temperature, "top_p": self.top_p, "top_k": self.top_k, "n": self.n, } @property def _llm_type(self) -> str: return "litellm-chat"