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https://github.com/hwchase17/langchain.git
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ada740b5b9
See https://docs.astral.sh/ruff/rules/blanket-type-ignore/ --------- Co-authored-by: Chester Curme <chester.curme@gmail.com>
218 lines
8.2 KiB
Python
218 lines
8.2 KiB
Python
"""LiteLLM Router as LangChain Model."""
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from typing import Any, AsyncIterator, Iterator, List, Mapping, Optional
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from langchain_core.callbacks.manager 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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agenerate_from_stream,
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generate_from_stream,
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)
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from langchain_core.messages import AIMessageChunk, BaseMessage
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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from langchain_community.chat_models.litellm import (
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ChatLiteLLM,
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_convert_delta_to_message_chunk,
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_convert_dict_to_message,
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)
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token_usage_key_name = "token_usage" # nosec # incorrectly flagged as password
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model_extra_key_name = "model_extra" # nosec # incorrectly flagged as password
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def get_llm_output(usage: Any, **params: Any) -> dict:
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"""Get llm output from usage and params."""
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llm_output = {token_usage_key_name: usage}
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# copy over metadata (metadata came from router completion call)
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metadata = params["metadata"]
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for key in metadata:
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if key not in llm_output:
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# if token usage in metadata, prefer metadata's copy of it
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llm_output[key] = metadata[key]
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return llm_output
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class ChatLiteLLMRouter(ChatLiteLLM):
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"""LiteLLM Router as LangChain Model."""
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router: Any
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def __init__(self, *, router: Any, **kwargs: Any) -> None:
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"""Construct Chat LiteLLM Router."""
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super().__init__(router=router, **kwargs) # type: ignore[call-arg]
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self.router = router
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@property
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def _llm_type(self) -> str:
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return "LiteLLMRouter"
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def _prepare_params_for_router(self, params: Any) -> None:
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# allow the router to set api_base based on its model choice
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api_base_key_name = "api_base"
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if api_base_key_name in params and params[api_base_key_name] is None:
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del params[api_base_key_name]
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# add metadata so router can fill it below
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params.setdefault("metadata", {})
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def set_default_model(self, model_name: str) -> None:
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"""Set the default model to use for completion calls.
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Sets `self.model` to `model_name` if it is in the litellm router's
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(`self.router`) model list. This provides the default model to use
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for completion calls if no `model` kwarg is provided.
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"""
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model_list = self.router.model_list
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if not model_list:
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raise ValueError("model_list is None or empty.")
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for entry in model_list:
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if entry["model_name"] == model_name:
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self.model = model_name
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return
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raise ValueError(f"Model {model_name} not found in model_list.")
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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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self._prepare_params_for_router(params)
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response = self.router.completion(
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messages=message_dicts,
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**params,
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)
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return self._create_chat_result(response, **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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default_chunk_class = AIMessageChunk
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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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self._prepare_params_for_router(params)
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for chunk in self.router.completion(messages=message_dicts, **params):
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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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chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
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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(chunk.content, chunk=cg_chunk, **params)
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yield cg_chunk
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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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default_chunk_class = AIMessageChunk
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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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self._prepare_params_for_router(params)
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async for chunk in await self.router.acompletion(
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messages=message_dicts, **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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chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
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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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await run_manager.on_llm_new_token(
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chunk.content, chunk=cg_chunk, **params
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)
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yield cg_chunk
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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 self.streaming
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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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self._prepare_params_for_router(params)
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response = await self.router.acompletion(
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messages=message_dicts,
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**params,
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)
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return self._create_chat_result(response, **params)
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# from
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# https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/chat_models/openai.py
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# but modified to handle LiteLLM Usage class
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def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
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overall_token_usage: dict = {}
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system_fingerprint = None
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for output in llm_outputs:
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if output is None:
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# Happens in streaming
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continue
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token_usage = output["token_usage"]
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if token_usage is not None:
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# get dict from LiteLLM Usage class
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for k, v in token_usage.model_dump().items():
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if k in overall_token_usage and overall_token_usage[k] is not None:
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overall_token_usage[k] += v
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else:
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overall_token_usage[k] = v
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if system_fingerprint is None:
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system_fingerprint = output.get("system_fingerprint")
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combined = {"token_usage": overall_token_usage, "model_name": self.model}
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if system_fingerprint:
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combined["system_fingerprint"] = system_fingerprint
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return combined
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def _create_chat_result(
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self, response: Mapping[str, Any], **params: Any
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) -> ChatResult:
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from litellm.utils import Usage
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generations = []
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for res in response["choices"]:
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message = _convert_dict_to_message(res["message"])
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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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token_usage = response.get("usage", Usage(prompt_tokens=0, total_tokens=0))
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llm_output = get_llm_output(token_usage, **params)
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return ChatResult(generations=generations, llm_output=llm_output)
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