Source code for langchain_community.retrievers.tfidf

from __future__ import annotations

import pickle
from pathlib import Path
from typing import Any, Dict, Iterable, List, Optional

from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever


[docs]class TFIDFRetriever(BaseRetriever): """`TF-IDF` retriever. Largely based on https://github.com/asvskartheek/Text-Retrieval/blob/master/TF-IDF%20Search%20Engine%20(SKLEARN).ipynb """ vectorizer: Any """TF-IDF vectorizer.""" docs: List[Document] """Documents.""" tfidf_array: Any """TF-IDF array.""" k: int = 4 """Number of documents to return.""" class Config: """Configuration for this pydantic object.""" arbitrary_types_allowed = True
[docs] @classmethod def from_texts( cls, texts: Iterable[str], metadatas: Optional[Iterable[dict]] = None, tfidf_params: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> TFIDFRetriever: try: from sklearn.feature_extraction.text import TfidfVectorizer except ImportError: raise ImportError( "Could not import scikit-learn, please install with `pip install " "scikit-learn`." ) tfidf_params = tfidf_params or {} vectorizer = TfidfVectorizer(**tfidf_params) tfidf_array = vectorizer.fit_transform(texts) metadatas = metadatas or ({} for _ in texts) docs = [Document(page_content=t, metadata=m) for t, m in zip(texts, metadatas)] return cls(vectorizer=vectorizer, docs=docs, tfidf_array=tfidf_array, **kwargs)
[docs] @classmethod def from_documents( cls, documents: Iterable[Document], *, tfidf_params: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> TFIDFRetriever: texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents)) return cls.from_texts( texts=texts, tfidf_params=tfidf_params, metadatas=metadatas, **kwargs )
def _get_relevant_documents( self, query: str, *, run_manager: CallbackManagerForRetrieverRun ) -> List[Document]: from sklearn.metrics.pairwise import cosine_similarity query_vec = self.vectorizer.transform( [query] ) # Ip -- (n_docs,x), Op -- (n_docs,n_Feats) results = cosine_similarity(self.tfidf_array, query_vec).reshape( (-1,) ) # Op -- (n_docs,1) -- Cosine Sim with each doc return_docs = [self.docs[i] for i in results.argsort()[-self.k :][::-1]] return return_docs
[docs] def save_local( self, folder_path: str, file_name: str = "tfidf_vectorizer", ) -> None: try: import joblib except ImportError: raise ImportError( "Could not import joblib, please install with `pip install joblib`." ) path = Path(folder_path) path.mkdir(exist_ok=True, parents=True) # Save vectorizer with joblib dump. joblib.dump(self.vectorizer, path / f"{file_name}.joblib") # Save docs and tfidf array as pickle. with open(path / f"{file_name}.pkl", "wb") as f: pickle.dump((self.docs, self.tfidf_array), f)
[docs] @classmethod def load_local( cls, folder_path: str, *, allow_dangerous_deserialization: bool = False, file_name: str = "tfidf_vectorizer", ) -> TFIDFRetriever: """Load the retriever from local storage. Args: folder_path: Folder path to load from. allow_dangerous_deserialization: Whether to allow dangerous deserialization. Defaults to False. The deserialization relies on .joblib and .pkl files, which can be modified to deliver a malicious payload that results in execution of arbitrary code on your machine. You will need to set this to `True` to use deserialization. If you do this, make sure you trust the source of the file. file_name: File name to load from. Defaults to "tfidf_vectorizer". Returns: TFIDFRetriever: Loaded retriever. """ try: import joblib except ImportError: raise ImportError( "Could not import joblib, please install with `pip install joblib`." ) if not allow_dangerous_deserialization: raise ValueError( "The de-serialization of this retriever is based on .joblib and " ".pkl files." "Such files can be modified to deliver a malicious payload that " "results in execution of arbitrary code on your machine." "You will need to set `allow_dangerous_deserialization` to `True` to " "load this retriever. If you do this, make sure you trust the source " "of the file, and you are responsible for validating the file " "came from a trusted source." ) path = Path(folder_path) # Load vectorizer with joblib load. vectorizer = joblib.load(path / f"{file_name}.joblib") # Load docs and tfidf array as pickle. with open(path / f"{file_name}.pkl", "rb") as f: docs, tfidf_array = pickle.load(f) return cls(vectorizer=vectorizer, docs=docs, tfidf_array=tfidf_array)