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https://github.com/hwchase17/langchain.git
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d34bf78f3b
Currently this retriever discards document ids --------- Co-authored-by: asi-cider <88270351+asi-cider@users.noreply.github.com> Co-authored-by: Erick Friis <erick@langchain.dev>
117 lines
4.0 KiB
Python
117 lines
4.0 KiB
Python
from __future__ import annotations
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from typing import Any, Callable, Dict, Iterable, List, Optional
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from langchain_core.callbacks import CallbackManagerForRetrieverRun
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from langchain_core.documents import Document
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from langchain_core.retrievers import BaseRetriever
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from pydantic import ConfigDict, Field
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def default_preprocessing_func(text: str) -> List[str]:
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return text.split()
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class BM25Retriever(BaseRetriever):
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"""`BM25` retriever without Elasticsearch."""
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vectorizer: Any = None
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""" BM25 vectorizer."""
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docs: List[Document] = Field(repr=False)
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""" List of documents."""
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k: int = 4
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""" Number of documents to return."""
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preprocess_func: Callable[[str], List[str]] = default_preprocessing_func
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""" Preprocessing function to use on the text before BM25 vectorization."""
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model_config = ConfigDict(
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arbitrary_types_allowed=True,
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)
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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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ids: Optional[Iterable[str]] = None,
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bm25_params: Optional[Dict[str, Any]] = None,
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preprocess_func: Callable[[str], List[str]] = default_preprocessing_func,
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**kwargs: Any,
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) -> BM25Retriever:
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"""
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Create a BM25Retriever from a list of texts.
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Args:
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texts: A list of texts to vectorize.
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metadatas: A list of metadata dicts to associate with each text.
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ids: A list of ids to associate with each text.
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bm25_params: Parameters to pass to the BM25 vectorizer.
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preprocess_func: A function to preprocess each text before vectorization.
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**kwargs: Any other arguments to pass to the retriever.
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Returns:
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A BM25Retriever instance.
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"""
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try:
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from rank_bm25 import BM25Okapi
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except ImportError:
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raise ImportError(
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"Could not import rank_bm25, please install with `pip install "
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"rank_bm25`."
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)
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texts_processed = [preprocess_func(t) for t in texts]
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bm25_params = bm25_params or {}
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vectorizer = BM25Okapi(texts_processed, **bm25_params)
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metadatas = metadatas or ({} for _ in texts)
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if ids:
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docs = [
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Document(page_content=t, metadata=m, id=i)
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for t, m, i in zip(texts, metadatas, ids)
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]
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else:
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docs = [
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Document(page_content=t, metadata=m) for t, m in zip(texts, metadatas)
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]
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return cls(
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vectorizer=vectorizer, docs=docs, preprocess_func=preprocess_func, **kwargs
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)
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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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bm25_params: Optional[Dict[str, Any]] = None,
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preprocess_func: Callable[[str], List[str]] = default_preprocessing_func,
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**kwargs: Any,
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) -> BM25Retriever:
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"""
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Create a BM25Retriever from a list of Documents.
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Args:
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documents: A list of Documents to vectorize.
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bm25_params: Parameters to pass to the BM25 vectorizer.
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preprocess_func: A function to preprocess each text before vectorization.
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**kwargs: Any other arguments to pass to the retriever.
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Returns:
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A BM25Retriever instance.
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"""
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texts, metadatas, ids = zip(
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*((d.page_content, d.metadata, d.id) for d in documents)
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)
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return cls.from_texts(
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texts=texts,
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bm25_params=bm25_params,
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metadatas=metadatas,
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ids=ids,
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preprocess_func=preprocess_func,
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**kwargs,
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)
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def _get_relevant_documents(
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self, query: str, *, run_manager: CallbackManagerForRetrieverRun
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) -> List[Document]:
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processed_query = self.preprocess_func(query)
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return_docs = self.vectorizer.get_top_n(processed_query, self.docs, n=self.k)
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return return_docs
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