3cceec63ef
* add base Tool * minor update test_tool * update test dependency * update test dependency * Fix namespace conflict * update test * add base Agent Interface, add ReWoo Agent * minor update * update test * fix typo * remove unneeded print * update rewoo agent * add LLMTool * update BaseAgent type * add ReAct agent * add ReAct agent * minor update * minor update * minor update * minor update * update docstring * fix max_iteration --------- Co-authored-by: trducng <trungduc1992@gmail.com>
83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
from typing import List, Type, TypeVar
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from langchain.schema.language_model import BaseLanguageModel
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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 import LLMInterface
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Message = TypeVar("Message", bound=BaseMessage)
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class ChatLLM(BaseComponent):
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...
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class LangchainChatLLM(ChatLLM):
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_lc_class: Type[BaseLanguageModel]
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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.decorate(no_cache=True)
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def agent(self) -> BaseLanguageModel:
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return self._lc_class(**self._kwargs)
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def run_raw(self, text: str, **kwargs) -> LLMInterface:
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message = HumanMessage(content=text)
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return self.run_document([message], **kwargs)
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def run_batch_raw(self, text: List[str], **kwargs) -> List[LLMInterface]:
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inputs = [[HumanMessage(content=each)] for each in text]
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return self.run_batch_document(inputs, **kwargs)
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def run_document(self, text: List[Message], **kwargs) -> LLMInterface:
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pred = self.agent.generate([text], **kwargs) # type: ignore
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return LLMInterface(
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text=[each.text for each in pred.generations[0]],
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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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logits=[],
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)
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def run_batch_document(
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self, text: List[List[Message]], **kwargs
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) -> List[LLMInterface]:
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outputs = []
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for each_text in text:
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outputs.append(self.run_document(each_text, **kwargs))
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return outputs
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def is_document(self, text, **kwargs) -> bool:
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if isinstance(text, str):
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return False
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elif isinstance(text, List) and isinstance(text[0], str):
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return False
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return True
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def is_batch(self, text, **kwargs) -> bool:
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if isinstance(text, str):
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return False
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elif isinstance(text, List):
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if isinstance(text[0], BaseMessage):
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return False
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return True
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def __setattr__(self, name, value):
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if name in self._lc_class.__fields__:
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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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