mirror of https://github.com/hwchase17/langchain
add bm25 module (#7779)
- Description: Add a BM25 Retriever that do not need Elastic search - Dependencies: rank_bm25(if it is not installed it will be install by using pip, just like TFIDFRetriever do) - Tag maintainer: @rlancemartin, @eyurtsev - Twitter handle: DayuanJian21687 --------- Co-authored-by: Bagatur <baskaryan@gmail.com>pull/7838/head
Dayuan Jiang
11 months ago
committed by
GitHub
parent
fa0a9e502a
commit
ee40d37098
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ab66dd43",
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"metadata": {},
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"source": [
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"# BM25\n",
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"\n",
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"[BM25](https://en.wikipedia.org/wiki/Okapi_BM25) also known as the Okapi BM25, is a ranking function used in information retrieval systems to estimate the relevance of documents to a given search query.\n",
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"\n",
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"This notebook goes over how to use a retriever that under the hood uses BM25 using [`rank_bm25`](https://github.com/dorianbrown/rank_bm25) package.\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a801b57c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# !pip install rank_bm25"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "393ac030",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/workspaces/langchain/.venv/lib/python3.10/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.10) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"from langchain.retrievers import BM25Retriever"
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]
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},
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{
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"cell_type": "markdown",
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"id": "aaf80e7f",
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"metadata": {},
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"source": [
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"## Create New Retriever with Texts"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "98b1c017",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"retriever = BM25Retriever.from_texts([\"foo\", \"bar\", \"world\", \"hello\", \"foo bar\"])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c016b266",
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"metadata": {},
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"source": [
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"## Create a New Retriever with Documents\n",
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"\n",
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"You can now create a new retriever with the documents you created."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "53af4f00",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.schema import Document\n",
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"\n",
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"retriever = BM25Retriever.from_documents(\n",
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" [\n",
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" Document(page_content=\"foo\"),\n",
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" Document(page_content=\"bar\"),\n",
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" Document(page_content=\"world\"),\n",
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" Document(page_content=\"hello\"),\n",
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" Document(page_content=\"foo bar\"),\n",
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" ]\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "08437fa2",
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"metadata": {},
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"source": [
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"## Use Retriever\n",
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"\n",
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"We can now use the retriever!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "c0455218",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"result = retriever.get_relevant_documents(\"foo\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "7dfa5c29",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Document(page_content='foo', metadata={}),\n",
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" Document(page_content='foo bar', metadata={}),\n",
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" Document(page_content='hello', metadata={}),\n",
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" Document(page_content='world', metadata={})]"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"result"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "997aaa8d",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -0,0 +1,86 @@
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"""
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BM25 Retriever without elastic search
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"""
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from __future__ import annotations
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from typing import Any, Callable, Dict, Iterable, List, Optional
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from langchain.callbacks.manager import (
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AsyncCallbackManagerForRetrieverRun,
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CallbackManagerForRetrieverRun,
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)
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from langchain.schema import BaseRetriever, Document
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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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vectorizer: Any
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docs: List[Document]
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k: int = 4
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preprocess_func: Callable[[str], List[str]] = default_preprocessing_func
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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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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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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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docs = [Document(page_content=t, metadata=m) for t, m in zip(texts, metadatas)]
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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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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,
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bm25_params=bm25_params,
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metadatas=metadatas,
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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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async def _aget_relevant_documents(
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self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
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) -> List[Document]:
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raise NotImplementedError
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@ -0,0 +1,34 @@
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import pytest
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from langchain.retrievers.bm25 import BM25Retriever
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from langchain.schema import Document
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@pytest.mark.requires("rank_bm25")
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def test_from_texts() -> None:
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input_texts = ["I have a pen.", "Do you have a pen?", "I have a bag."]
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bm25_retriever = BM25Retriever.from_texts(texts=input_texts)
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assert len(bm25_retriever.docs) == 3
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assert bm25_retriever.vectorizer.doc_len == [4, 5, 4]
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@pytest.mark.requires("rank_bm25")
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def test_from_texts_with_bm25_params() -> None:
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input_texts = ["I have a pen.", "Do you have a pen?", "I have a bag."]
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bm25_retriever = BM25Retriever.from_texts(
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texts=input_texts, bm25_params={"epsilon": 10}
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)
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# should count only multiple words (have, pan)
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assert bm25_retriever.vectorizer.epsilon == 10
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@pytest.mark.requires("rank_bm25")
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def test_from_documents() -> None:
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input_docs = [
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Document(page_content="I have a pen."),
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Document(page_content="Do you have a pen?"),
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Document(page_content="I have a bag."),
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]
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bm25_retriever = BM25Retriever.from_documents(documents=input_docs)
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assert len(bm25_retriever.docs) == 3
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assert bm25_retriever.vectorizer.doc_len == [4, 5, 4]
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