Source code for langchain_community.retrievers.bm25

from __future__ import annotations

from typing import Any, Callable, Dict, Iterable, List, Optional

from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from pydantic import ConfigDict, Field



[docs]
def default_preprocessing_func(text: str) -> List[str]:
    return text.split()




[docs]
class BM25Retriever(BaseRetriever):
    """`BM25` retriever without Elasticsearch."""

    vectorizer: Any = None
    """ BM25 vectorizer."""
    docs: List[Document] = Field(repr=False)
    """ List of documents."""
    k: int = 4
    """ Number of documents to return."""
    preprocess_func: Callable[[str], List[str]] = default_preprocessing_func
    """ Preprocessing function to use on the text before BM25 vectorization."""

    model_config = ConfigDict(
        arbitrary_types_allowed=True,
    )


[docs]
    @classmethod
    def from_texts(
        cls,
        texts: Iterable[str],
        metadatas: Optional[Iterable[dict]] = None,
        ids: Optional[Iterable[str]] = None,
        bm25_params: Optional[Dict[str, Any]] = None,
        preprocess_func: Callable[[str], List[str]] = default_preprocessing_func,
        **kwargs: Any,
    ) -> BM25Retriever:
        """
        Create a BM25Retriever from a list of texts.
        Args:
            texts: A list of texts to vectorize.
            metadatas: A list of metadata dicts to associate with each text.
            ids: A list of ids to associate with each text.
            bm25_params: Parameters to pass to the BM25 vectorizer.
            preprocess_func: A function to preprocess each text before vectorization.
            **kwargs: Any other arguments to pass to the retriever.

        Returns:
            A BM25Retriever instance.
        """
        try:
            from rank_bm25 import BM25Okapi
        except ImportError:
            raise ImportError(
                "Could not import rank_bm25, please install with `pip install "
                "rank_bm25`."
            )

        texts_processed = [preprocess_func(t) for t in texts]
        bm25_params = bm25_params or {}
        vectorizer = BM25Okapi(texts_processed, **bm25_params)
        metadatas = metadatas or ({} for _ in texts)
        if ids:
            docs = [
                Document(page_content=t, metadata=m, id=i)
                for t, m, i in zip(texts, metadatas, ids)
            ]
        else:
            docs = [
                Document(page_content=t, metadata=m) for t, m in zip(texts, metadatas)
            ]
        return cls(
            vectorizer=vectorizer, docs=docs, preprocess_func=preprocess_func, **kwargs
        )



[docs]
    @classmethod
    def from_documents(
        cls,
        documents: Iterable[Document],
        *,
        bm25_params: Optional[Dict[str, Any]] = None,
        preprocess_func: Callable[[str], List[str]] = default_preprocessing_func,
        **kwargs: Any,
    ) -> BM25Retriever:
        """
        Create a BM25Retriever from a list of Documents.
        Args:
            documents: A list of Documents to vectorize.
            bm25_params: Parameters to pass to the BM25 vectorizer.
            preprocess_func: A function to preprocess each text before vectorization.
            **kwargs: Any other arguments to pass to the retriever.

        Returns:
            A BM25Retriever instance.
        """
        texts, metadatas, ids = zip(
            *((d.page_content, d.metadata, d.id) for d in documents)
        )
        return cls.from_texts(
            texts=texts,
            bm25_params=bm25_params,
            metadatas=metadatas,
            ids=ids,
            preprocess_func=preprocess_func,
            **kwargs,
        )


    def _get_relevant_documents(
        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
    ) -> List[Document]:
        processed_query = self.preprocess_func(query)
        return_docs = self.vectorizer.get_top_n(processed_query, self.docs, n=self.k)
        return return_docs