forked from Archives/langchain
77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
"""TF-IDF Retriever.
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Largely based on
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https://github.com/asvskartheek/Text-Retrieval/blob/master/TF-IDF%20Search%20Engine%20(SKLEARN).ipynb"""
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from __future__ import annotations
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from typing import Any, Dict, Iterable, List, Optional
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from pydantic import BaseModel
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from langchain.schema import BaseRetriever, Document
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class TFIDFRetriever(BaseRetriever, BaseModel):
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vectorizer: Any
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docs: List[Document]
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tfidf_array: Any
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k: int = 4
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class Config:
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"""Configuration for this pydantic object."""
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arbitrary_types_allowed = True
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@classmethod
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def from_texts(
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cls,
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texts: Iterable[str],
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metadatas: Optional[Iterable[dict]] = None,
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tfidf_params: Optional[Dict[str, Any]] = None,
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**kwargs: Any,
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) -> TFIDFRetriever:
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try:
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from sklearn.feature_extraction.text import TfidfVectorizer
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except ImportError:
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raise ImportError(
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"Could not import scikit-learn, please install with `pip install "
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"scikit-learn`."
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)
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tfidf_params = tfidf_params or {}
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vectorizer = TfidfVectorizer(**tfidf_params)
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tfidf_array = vectorizer.fit_transform(texts)
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metadatas = metadatas or ({} for _ in texts)
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docs = [Document(page_content=t, metadata=m) for t, m in zip(texts, metadatas)]
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return cls(vectorizer=vectorizer, docs=docs, tfidf_array=tfidf_array, **kwargs)
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@classmethod
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def from_documents(
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cls,
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documents: Iterable[Document],
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*,
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tfidf_params: Optional[Dict[str, Any]] = None,
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**kwargs: Any,
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) -> TFIDFRetriever:
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texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents))
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return cls.from_texts(
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texts=texts, tfidf_params=tfidf_params, metadatas=metadatas, **kwargs
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)
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def get_relevant_documents(self, query: str) -> List[Document]:
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from sklearn.metrics.pairwise import cosine_similarity
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query_vec = self.vectorizer.transform(
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[query]
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) # Ip -- (n_docs,x), Op -- (n_docs,n_Feats)
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results = cosine_similarity(self.tfidf_array, query_vec).reshape(
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(-1,)
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) # Op -- (n_docs,1) -- Cosine Sim with each doc
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return_docs = []
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for i in results.argsort()[-self.k :][::-1]:
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return_docs.append(self.docs[i])
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return return_docs
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async def aget_relevant_documents(self, query: str) -> List[Document]:
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raise NotImplementedError
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