71 lines
2.0 KiB
Python
71 lines
2.0 KiB
Python
from typing import List, Type
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from langchain.schema.language_model import BaseLanguageModel
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from theflow.base import Param
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from ...components import BaseComponent
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from ..base import LLMInterface
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class LLM(BaseComponent):
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pass
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class LangchainLLM(LLM):
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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()
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def agent(self):
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return self._lc_class(**self._kwargs)
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def run_raw(self, text: str) -> LLMInterface:
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pred = self.agent.generate([text])
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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_raw(self, text: List[str]) -> List[LLMInterface]:
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outputs = []
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for each_text in text:
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outputs.append(self.run_raw(each_text))
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return outputs
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def run_document(self, text: str) -> LLMInterface:
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return self.run_raw(text)
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def run_batch_document(self, text: List[str]) -> List[LLMInterface]:
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return self.run_batch_raw(text)
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def is_document(self, text) -> bool:
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return False
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def is_batch(self, text) -> bool:
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return False if isinstance(text, str) else 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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class LLMChat(BaseComponent):
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pass
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