633 lines
23 KiB
Python
633 lines
23 KiB
Python
"""
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Deprecated LiteLLM wrapper.
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⭐ Use `pip install langchain-litellm` and import
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`from langchain_litellm import ChatLiteLLM` instead.
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"""
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from __future__ import annotations
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import json
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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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Literal,
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Mapping,
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Optional,
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Sequence,
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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._api.deprecation import deprecated
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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 import LanguageModelInput
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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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FunctionMessage,
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FunctionMessageChunk,
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HumanMessage,
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HumanMessageChunk,
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SystemMessage,
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SystemMessageChunk,
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ToolCall,
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ToolCallChunk,
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ToolMessage,
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)
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from langchain_core.messages.ai import UsageMetadata
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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.runnables import Runnable
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from langchain_core.tools import BaseTool
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from langchain_core.utils import get_from_dict_or_env, pre_init
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from langchain_core.utils.function_calling import convert_to_openai_tool
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from pydantic import BaseModel, Field
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logger = logging.getLogger(__name__)
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class ChatLiteLLMException(Exception):
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"""Error with the `LiteLLM I/O` library"""
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def _create_retry_decorator(
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llm: ChatLiteLLM,
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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 PaLM exceptions"""
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import litellm
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errors = [
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litellm.Timeout,
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litellm.APIError,
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litellm.APIConnectionError,
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litellm.RateLimitError,
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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_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
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role = _dict["role"]
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if role == "user":
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return HumanMessage(content=_dict["content"])
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elif role == "assistant":
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# Fix for azure
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# Also OpenAI returns None for tool invocations
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content = _dict.get("content", "") or ""
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additional_kwargs = {}
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if _dict.get("function_call"):
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additional_kwargs["function_call"] = dict(_dict["function_call"])
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if _dict.get("tool_calls"):
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additional_kwargs["tool_calls"] = _dict["tool_calls"]
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return AIMessage(content=content, additional_kwargs=additional_kwargs)
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elif role == "system":
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return SystemMessage(content=_dict["content"])
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elif role == "function":
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return FunctionMessage(content=_dict["content"], name=_dict["name"])
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else:
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return ChatMessage(content=_dict["content"], role=role)
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async def acompletion_with_retry(
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llm: ChatLiteLLM,
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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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# Use OpenAI's async api https://github.com/openai/openai-python#async-api
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return await llm.client.acreate(**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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_dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
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) -> BaseMessageChunk:
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role = _dict.get("role")
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content = _dict.get("content") or ""
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if _dict.get("function_call"):
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additional_kwargs = {"function_call": dict(_dict["function_call"])}
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elif _dict.get("reasoning_content"):
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additional_kwargs = {"reasoning_content": _dict["reasoning_content"]}
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else:
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additional_kwargs = {}
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tool_call_chunks = []
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if raw_tool_calls := _dict.get("tool_calls"):
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additional_kwargs["tool_calls"] = raw_tool_calls
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try:
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tool_call_chunks = [
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ToolCallChunk(
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name=rtc["function"].get("name"),
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args=rtc["function"].get("arguments"),
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id=rtc.get("id"),
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index=rtc["index"],
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)
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for rtc in raw_tool_calls
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]
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except KeyError:
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pass
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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(
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content=content,
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additional_kwargs=additional_kwargs,
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tool_call_chunks=tool_call_chunks,
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)
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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 == "function" or default_class == FunctionMessageChunk:
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return FunctionMessageChunk(content=content, name=_dict["name"])
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elif role or default_class == ChatMessageChunk:
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return ChatMessageChunk(content=content, role=role) # type: ignore[arg-type]
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else:
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return default_class(content=content) # type: ignore[call-arg]
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def _lc_tool_call_to_openai_tool_call(tool_call: ToolCall) -> dict:
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return {
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"type": "function",
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"id": tool_call["id"],
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"function": {
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"name": tool_call["name"],
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"arguments": json.dumps(tool_call["args"]),
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},
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}
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def _convert_message_to_dict(message: BaseMessage) -> dict:
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message_dict: Dict[str, Any] = {"content": message.content}
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if isinstance(message, ChatMessage):
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message_dict["role"] = message.role
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elif isinstance(message, HumanMessage):
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message_dict["role"] = "user"
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elif isinstance(message, AIMessage):
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message_dict["role"] = "assistant"
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if "function_call" in message.additional_kwargs:
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message_dict["function_call"] = message.additional_kwargs["function_call"]
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if message.tool_calls:
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message_dict["tool_calls"] = [
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_lc_tool_call_to_openai_tool_call(tc) for tc in message.tool_calls
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]
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elif "tool_calls" in message.additional_kwargs:
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message_dict["tool_calls"] = message.additional_kwargs["tool_calls"]
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elif isinstance(message, SystemMessage):
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message_dict["role"] = "system"
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elif isinstance(message, FunctionMessage):
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message_dict["role"] = "function"
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message_dict["name"] = message.name
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elif isinstance(message, ToolMessage):
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message_dict["role"] = "tool"
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message_dict["tool_call_id"] = message.tool_call_id
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else:
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raise ValueError(f"Got unknown type {message}")
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if "name" in message.additional_kwargs:
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message_dict["name"] = message.additional_kwargs["name"]
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return message_dict
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_OPENAI_MODELS = [
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"o1-mini",
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"o1-preview",
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"gpt-4o-mini",
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"gpt-4o-mini-2024-07-18",
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"gpt-4o",
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"gpt-4o-2024-08-06",
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"gpt-4o-2024-05-13",
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"gpt-4-turbo",
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"gpt-4-turbo-preview",
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"gpt-4-0125-preview",
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"gpt-4-1106-preview",
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"gpt-3.5-turbo-1106",
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"gpt-3.5-turbo",
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"gpt-3.5-turbo-0301",
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"gpt-3.5-turbo-0613",
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"gpt-3.5-turbo-16k",
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"gpt-3.5-turbo-16k-0613",
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"gpt-4",
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"gpt-4-0314",
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"gpt-4-0613",
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"gpt-4-32k",
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"gpt-4-32k-0314",
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"gpt-4-32k-0613",
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]
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@deprecated(
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since="0.3.24",
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removal="1.0",
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alternative_import="langchain_litellm.ChatLiteLLM",
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)
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class ChatLiteLLM(BaseChatModel):
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"""DEPRECATED – use `langchain_litellm.ChatLiteLLM` instead."""
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client: Any = None #: :meta private:
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model: str = "gpt-3.5-turbo"
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model_name: Optional[str] = None
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"""Model name to use."""
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openai_api_key: Optional[str] = None
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azure_api_key: Optional[str] = None
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anthropic_api_key: Optional[str] = None
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replicate_api_key: Optional[str] = None
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cohere_api_key: Optional[str] = None
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openrouter_api_key: Optional[str] = None
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api_key: Optional[str] = None
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streaming: bool = False
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api_base: Optional[str] = None
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organization: Optional[str] = None
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custom_llm_provider: Optional[str] = None
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request_timeout: Optional[Union[float, Tuple[float, float]]] = None
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temperature: Optional[float] = None
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"""Run inference with this temperature. Must be in the closed
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interval [0.0, 1.0]."""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Holds any model parameters valid for API call not explicitly specified."""
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top_p: Optional[float] = None
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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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top_k: Optional[int] = None
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"""Decode using top-k sampling: consider the set of top_k most probable tokens.
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Must be positive."""
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n: Optional[int] = None
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"""Number of chat completions to generate for each prompt. Note that the API may
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not return the full n completions if duplicates are generated."""
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max_tokens: Optional[int] = None
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max_retries: int = 1
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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 OpenAI API."""
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set_model_value = self.model
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if self.model_name is not None:
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set_model_value = self.model_name
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return {
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"model": set_model_value,
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"force_timeout": self.request_timeout,
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"max_tokens": self.max_tokens,
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"stream": self.streaming,
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"n": self.n,
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"temperature": self.temperature,
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"custom_llm_provider": self.custom_llm_provider,
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**self.model_kwargs,
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}
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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 openai client."""
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set_model_value = self.model
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if self.model_name is not None:
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set_model_value = self.model_name
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self.client.api_base = self.api_base
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self.client.api_key = self.api_key
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for named_api_key in [
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"openai_api_key",
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"azure_api_key",
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"anthropic_api_key",
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"replicate_api_key",
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"cohere_api_key",
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"openrouter_api_key",
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]:
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if api_key_value := getattr(self, named_api_key):
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setattr(
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self.client,
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named_api_key.replace("_api_key", "_key"),
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api_key_value,
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)
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self.client.organization = self.organization
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creds: Dict[str, Any] = {
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"model": set_model_value,
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"force_timeout": self.request_timeout,
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"api_base": self.api_base,
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}
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return {**self._default_params, **creds}
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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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return self.client.completion(**kwargs)
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return _completion_with_retry(**kwargs)
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@pre_init
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate api key, python package exists, temperature, top_p, and top_k."""
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try:
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import litellm
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except ImportError:
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raise ChatLiteLLMException(
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"Could not import litellm python package. "
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"Please install it with `pip install litellm`"
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)
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values["openai_api_key"] = get_from_dict_or_env(
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values, "openai_api_key", "OPENAI_API_KEY", default=""
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)
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values["azure_api_key"] = get_from_dict_or_env(
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values, "azure_api_key", "AZURE_API_KEY", default=""
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)
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values["anthropic_api_key"] = get_from_dict_or_env(
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values, "anthropic_api_key", "ANTHROPIC_API_KEY", default=""
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)
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values["replicate_api_key"] = get_from_dict_or_env(
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values, "replicate_api_key", "REPLICATE_API_KEY", default=""
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)
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values["openrouter_api_key"] = get_from_dict_or_env(
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values, "openrouter_api_key", "OPENROUTER_API_KEY", default=""
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)
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values["cohere_api_key"] = get_from_dict_or_env(
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values, "cohere_api_key", "COHERE_API_KEY", default=""
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)
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values["huggingface_api_key"] = get_from_dict_or_env(
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values, "huggingface_api_key", "HUGGINGFACE_API_KEY", default=""
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)
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values["together_ai_api_key"] = get_from_dict_or_env(
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values, "together_ai_api_key", "TOGETHERAI_API_KEY", default=""
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)
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values["client"] = litellm
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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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if values["top_k"] is not None and values["top_k"] <= 0:
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raise ValueError("top_k must be positive")
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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 self.streaming
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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(self, response: Mapping[str, Any]) -> ChatResult:
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generations = []
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token_usage = response.get("usage", {})
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for res in response["choices"]:
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message = _convert_dict_to_message(res["message"])
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if isinstance(message, AIMessage):
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message.response_metadata = {
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"model_name": self.model_name or self.model
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}
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message.usage_metadata = _create_usage_metadata(token_usage)
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gen = ChatGeneration(
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message=message,
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generation_info=dict(finish_reason=res.get("finish_reason")),
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)
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generations.append(gen)
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set_model_value = self.model
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if self.model_name is not None:
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set_model_value = self.model_name
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llm_output = {"token_usage": token_usage, "model": set_model_value}
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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[Dict[str, Any]], 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_dict(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: Type[BaseMessageChunk] = AIMessageChunk
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added_model_name = False
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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 not isinstance(chunk, dict):
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chunk = chunk.model_dump()
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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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usage = chunk.get("usage", {})
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chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
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if isinstance(chunk, AIMessageChunk):
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if not added_model_name:
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chunk.response_metadata = {
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"model_name": self.model_name or self.model
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||
}
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added_model_name = True
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chunk.usage_metadata = _create_usage_metadata(usage)
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default_chunk_class = chunk.__class__
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cg_chunk = ChatGenerationChunk(message=chunk)
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if run_manager:
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run_manager.on_llm_new_token(str(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: Type[BaseMessageChunk] = AIMessageChunk
|
||
added_model_name = False
|
||
async for chunk in await acompletion_with_retry(
|
||
self, messages=message_dicts, run_manager=run_manager, **params
|
||
):
|
||
if not isinstance(chunk, dict):
|
||
chunk = chunk.model_dump()
|
||
if len(chunk["choices"]) == 0:
|
||
continue
|
||
delta = chunk["choices"][0]["delta"]
|
||
usage = chunk.get("usage", {})
|
||
chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
|
||
if isinstance(chunk, AIMessageChunk):
|
||
if not added_model_name:
|
||
chunk.response_metadata = {
|
||
"model_name": self.model_name or self.model
|
||
}
|
||
added_model_name = True
|
||
chunk.usage_metadata = _create_usage_metadata(usage)
|
||
default_chunk_class = chunk.__class__
|
||
cg_chunk = ChatGenerationChunk(message=chunk)
|
||
if run_manager:
|
||
await run_manager.on_llm_new_token(str(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)
|
||
|
||
def bind_tools(
|
||
self,
|
||
tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
|
||
tool_choice: Optional[
|
||
Union[dict, str, Literal["auto", "none", "required", "any"], bool]
|
||
] = None,
|
||
**kwargs: Any,
|
||
) -> Runnable[LanguageModelInput, AIMessage]:
|
||
"""Bind tool-like objects to this chat model.
|
||
|
||
LiteLLM expects tools argument in OpenAI format.
|
||
|
||
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. Options are:
|
||
- str of the form ``"<<tool_name>>"``: calls <<tool_name>> tool.
|
||
- ``"auto"``:
|
||
automatically selects a tool (including no tool).
|
||
- ``"none"``:
|
||
does not call a tool.
|
||
- ``"any"`` or ``"required"`` or ``True``:
|
||
forces least one tool to be called.
|
||
- dict of the form:
|
||
``{"type": "function", "function": {"name": <<tool_name>>}}``
|
||
- ``False`` or ``None``: no effect
|
||
**kwargs: Any additional parameters to pass to the
|
||
:class:`~langchain.runnable.Runnable` constructor.
|
||
"""
|
||
|
||
formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
|
||
|
||
# In case of openai if tool_choice is `any` or if bool has been provided we
|
||
# change it to `required` as that is suppored by openai.
|
||
if (
|
||
(self.model is not None and "azure" in self.model)
|
||
or (self.model_name is not None and "azure" in self.model_name)
|
||
or (self.model is not None and self.model in _OPENAI_MODELS)
|
||
or (self.model_name is not None and self.model_name in _OPENAI_MODELS)
|
||
) and (tool_choice == "any" or isinstance(tool_choice, bool)):
|
||
tool_choice = "required"
|
||
# If tool_choice is bool apart from openai we make it `any`
|
||
elif isinstance(tool_choice, bool):
|
||
tool_choice = "any"
|
||
elif isinstance(tool_choice, dict):
|
||
tool_names = [
|
||
formatted_tool["function"]["name"] for formatted_tool in formatted_tools
|
||
]
|
||
if not any(
|
||
tool_name == tool_choice["function"]["name"] for tool_name in tool_names
|
||
):
|
||
raise ValueError(
|
||
f"Tool choice {tool_choice} was specified, but the only "
|
||
f"provided tools were {tool_names}."
|
||
)
|
||
return super().bind(tools=formatted_tools, tool_choice=tool_choice, **kwargs)
|
||
|
||
@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"
|
||
|
||
|
||
def _create_usage_metadata(token_usage: Mapping[str, Any]) -> UsageMetadata:
|
||
input_tokens = token_usage.get("prompt_tokens", 0)
|
||
output_tokens = token_usage.get("completion_tokens", 0)
|
||
return UsageMetadata(
|
||
input_tokens=input_tokens,
|
||
output_tokens=output_tokens,
|
||
total_tokens=input_tokens + output_tokens,
|
||
)
|