cc1e75b3c6
* add rerankers in retrieving pipeline * update example MVP pipeline * add citation pipeline and function call interface * change return type of QA and AgentPipeline to Document
108 lines
3.4 KiB
Python
108 lines
3.4 KiB
Python
from __future__ import annotations
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import logging
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from typing import Type
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from langchain.chat_models.base import BaseChatModel
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from langchain.schema.messages import BaseMessage, HumanMessage
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from theflow.base import Param
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from ...base import BaseComponent
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from ...base.schema import LLMInterface
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logger = logging.getLogger(__name__)
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class ChatLLM(BaseComponent):
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def flow(self):
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if self.inflow is None:
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raise ValueError("No inflow provided.")
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if not isinstance(self.inflow, BaseComponent):
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raise ValueError(
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f"inflow must be a BaseComponent, found {type(self.inflow)}"
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)
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text = self.inflow.flow().text
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return self.__call__(text)
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class LangchainChatLLM(ChatLLM):
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_lc_class: Type[BaseChatModel]
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def __init__(self, **params):
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if self._lc_class is None:
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raise AttributeError(
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"Should set _lc_class attribute to the LLM class from Langchain "
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"if using LLM from Langchain"
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)
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self._kwargs: dict = {}
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for param in list(params.keys()):
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if param in self._lc_class.__fields__:
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self._kwargs[param] = params.pop(param)
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super().__init__(**params)
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@Param.auto(cache=False)
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def agent(self) -> BaseChatModel:
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return self._lc_class(**self._kwargs)
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def run(
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self, messages: str | BaseMessage | list[BaseMessage], **kwargs
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) -> LLMInterface:
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"""Generate response from messages
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Args:
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messages: history of messages to generate response from
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**kwargs: additional arguments to pass to the langchain chat model
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Returns:
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LLMInterface: generated response
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"""
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input_: list[BaseMessage] = []
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if isinstance(messages, str):
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input_ = [HumanMessage(content=messages)]
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elif isinstance(messages, BaseMessage):
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input_ = [messages]
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else:
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input_ = messages
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pred = self.agent.generate(messages=[input_], **kwargs)
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all_text = [each.text for each in pred.generations[0]]
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all_messages = [each.message for each in pred.generations[0]]
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completion_tokens, total_tokens, prompt_tokens = 0, 0, 0
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try:
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if pred.llm_output is not None:
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completion_tokens = pred.llm_output["token_usage"]["completion_tokens"]
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total_tokens = pred.llm_output["token_usage"]["total_tokens"]
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prompt_tokens = pred.llm_output["token_usage"]["prompt_tokens"]
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except Exception:
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logger.warning(
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f"Cannot get token usage from LLM output for {self._lc_class.__name__}"
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)
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return LLMInterface(
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text=all_text[0] if len(all_text) > 0 else "",
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candidates=all_text,
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completion_tokens=completion_tokens,
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total_tokens=total_tokens,
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prompt_tokens=prompt_tokens,
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messages=all_messages,
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logits=[],
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)
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def __setattr__(self, name, value):
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if name in self._lc_class.__fields__:
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self._kwargs[name] = value
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setattr(self.agent, name, value)
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else:
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super().__setattr__(name, value)
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def __getattr__(self, name):
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if name in self._lc_class.__fields__:
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return getattr(self.agent, name)
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return super().__getattr__(name) # type: ignore
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