62 lines
1.7 KiB
Python
62 lines
1.7 KiB
Python
from __future__ import annotations
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from abc import abstractmethod
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from typing import Type
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from langchain.schema.embeddings import Embeddings as LCEmbeddings
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from theflow import Param
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from ..base import BaseComponent, Document
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class BaseEmbeddings(BaseComponent):
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@abstractmethod
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def run(
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self, text: str | list[str] | Document | list[Document]
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) -> list[list[float]]:
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...
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class LangchainEmbeddings(BaseEmbeddings):
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_lc_class: Type[LCEmbeddings]
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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__: # type: ignore
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self._kwargs[param] = params.pop(param)
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super().__init__(**params)
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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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else:
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super().__setattr__(name, value)
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@Param.auto(cache=False)
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def agent(self):
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return self._lc_class(**self._kwargs)
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def run(self, text) -> list[list[float]]:
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input_: list[str] = []
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if not isinstance(text, list):
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text = [text]
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for item in text:
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if isinstance(item, str):
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input_.append(item)
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elif isinstance(item, Document):
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input_.append(item.text)
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else:
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raise ValueError(
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f"Invalid input type {type(item)}, should be str or Document"
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)
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return self.agent.embed_documents(input_)
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