feat(community): BM25 Keyword Search Retriever (#6719)

Co-authored-by: jacoblee93 <jacoblee93@gmail.com>

docs/core_docs/docs/integrations/retrievers/bm25.ipynb

@@ -0,0 +1,101 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# BM25\n",
+    "\n",
+    "BM25, also known as [Okapi BM25](https://en.wikipedia.org/wiki/Okapi_BM25), is a ranking function used in information retrieval systems to estimate the relevance of documents to a given search query.\n",
+    "\n",
+    "You can use it as part of your retrieval pipeline as a to rerank documents as a postprocessing step after retrieving an initial set of documents from another source.\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "The `BM25Retriever` is exported from `@langchain/community`. You'll need to install it like this:\n",
+    "\n",
+    "```{=mdx}\n",
+    "import IntegrationInstallTooltip from \"@mdx_components/integration_install_tooltip.mdx\";\n",
+    "import Npm2Yarn from \"@theme/Npm2Yarn\";\n",
+    "\n",
+    "<IntegrationInstallTooltip></IntegrationInstallTooltip>\n",
+    "\n",
+    "<Npm2Yarn>\n",
+    "  @langchain/community @langchain/core\n",
+    "</Npm2Yarn>\n",
+    "```\n",
+    "\n",
+    "This retriever uses code from [`this implementation`](https://github.com/FurkanToprak/OkapiBM25) of Okapi BM25.\n",
+    "\n",
+    "## Usage\n",
+    "\n",
+    "You can now create a new retriever with previously retrieved documents:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "  { pageContent: 'mitochondria is made of lipids', metadata: {} },\n",
+      "  {\n",
+      "    pageContent: 'mitochondria is the powerhouse of the cell',\n",
+      "    metadata: {}\n",
+      "  },\n",
+      "  { pageContent: 'Buildings are made out of brick', metadata: {} },\n",
+      "  { pageContent: 'Buildings are made out of wood', metadata: {} }\n",
+      "]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import { BM25Retriever } from \"@langchain/community/retrievers/bm25\";\n",
+    "\n",
+    "const retriever = BM25Retriever.fromDocuments([\n",
+    "  { pageContent: \"Buildings are made out of brick\", metadata: {} },\n",
+    "  { pageContent: \"Buildings are made out of wood\", metadata: {} },\n",
+    "  { pageContent: \"Buildings are made out of stone\", metadata: {} },\n",
+    "  { pageContent: \"Cars are made out of metal\", metadata: {} },\n",
+    "  { pageContent: \"Cars are made out of plastic\", metadata: {} },\n",
+    "  { pageContent: \"mitochondria is the powerhouse of the cell\", metadata: {} },\n",
+    "  { pageContent: \"mitochondria is made of lipids\", metadata: {} },\n",
+    "], { k: 4 });\n",
+    "\n",
+    "// Will return the 4 documents reranked by the BM25 algorithm\n",
+    "await retriever.invoke(\"mitochondria\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "TypeScript",
+   "language": "typescript",
+   "name": "tslab"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "mode": "typescript",
+    "name": "javascript",
+    "typescript": true
+   },
+   "file_extension": ".ts",
+   "mimetype": "text/typescript",
+   "name": "typescript",
+   "version": "3.7.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

libs/langchain-community/.gitignore

@@ -626,6 +626,10 @@ retrievers/amazon_knowledge_base.cjs
 retrievers/amazon_knowledge_base.js
 retrievers/amazon_knowledge_base.d.ts
 retrievers/amazon_knowledge_base.d.cts
+retrievers/bm25.cjs
+retrievers/bm25.js
+retrievers/bm25.d.ts
+retrievers/bm25.d.cts
 retrievers/chaindesk.cjs
 retrievers/chaindesk.js
 retrievers/chaindesk.d.ts

libs/langchain-community/langchain.config.js

@@ -196,6 +196,7 @@ export const config = {
     // retrievers
     "retrievers/amazon_kendra": "retrievers/amazon_kendra",
     "retrievers/amazon_knowledge_base": "retrievers/amazon_knowledge_base",
+    "retrievers/bm25": "retrievers/bm25",
     "retrievers/chaindesk": "retrievers/chaindesk",
     "retrievers/databerry": "retrievers/databerry",
     "retrievers/dria": "retrievers/dria",

libs/langchain-community/package.json

@@ -2118,6 +2118,15 @@
       "import": "./retrievers/amazon_knowledge_base.js",
       "require": "./retrievers/amazon_knowledge_base.cjs"
     },
+    "./retrievers/bm25": {
+      "types": {
+        "import": "./retrievers/bm25.d.ts",
+        "require": "./retrievers/bm25.d.cts",
+        "default": "./retrievers/bm25.d.ts"
+      },
+      "import": "./retrievers/bm25.js",
+      "require": "./retrievers/bm25.cjs"
+    },
     "./retrievers/chaindesk": {
       "types": {
         "import": "./retrievers/chaindesk.d.ts",
@@ -3713,6 +3722,10 @@
     "retrievers/amazon_knowledge_base.js",
     "retrievers/amazon_knowledge_base.d.ts",
     "retrievers/amazon_knowledge_base.d.cts",
+    "retrievers/bm25.cjs",
+    "retrievers/bm25.js",
+    "retrievers/bm25.d.ts",
+    "retrievers/bm25.d.cts",
     "retrievers/chaindesk.cjs",
     "retrievers/chaindesk.js",
     "retrievers/chaindesk.d.ts",

libs/langchain-community/src/load/import_map.ts

@@ -54,6 +54,7 @@ export * as chat_models__moonshot from "../chat_models/moonshot.js";
 export * as chat_models__ollama from "../chat_models/ollama.js";
 export * as chat_models__togetherai from "../chat_models/togetherai.js";
 export * as chat_models__yandex from "../chat_models/yandex.js";
+export * as retrievers__bm25 from "../retrievers/bm25.js";
 export * as retrievers__chaindesk from "../retrievers/chaindesk.js";
 export * as retrievers__databerry from "../retrievers/databerry.js";
 export * as retrievers__remote from "../retrievers/remote/index.js";

libs/langchain-community/src/retrievers/bm25.ts

@@ -0,0 +1,58 @@
+import { BaseRetriever, BaseRetrieverInput } from "@langchain/core/retrievers";
+import { Document } from "@langchain/core/documents";
+
+import { BM25 } from "../utils/@furkantoprak/bm25/BM25.js";
+
+export type BM25RetrieverOptions = {
+  docs: Document[];
+  k: number;
+} & BaseRetrieverInput;
+
+/**
+ * A retriever that uses the BM25 algorithm to rank documents based on their
+ * similarity to a query. It uses the "okapibm25" package for BM25 scoring.
+ * The k parameter determines the number of documents to return for each query.
+ */
+export class BM25Retriever extends BaseRetriever {
+  static lc_name() {
+    return "BM25Retriever";
+  }
+
+  lc_namespace = ["langchain", "retrievers", "bm25_retriever"];
+
+  static fromDocuments(
+    documents: Document[],
+    options: Omit<BM25RetrieverOptions, "docs">
+  ) {
+    return new this({ ...options, docs: documents });
+  }
+
+  docs: Document[];
+
+  k: number;
+
+  constructor(options: BM25RetrieverOptions) {
+    super(options);
+    this.docs = options.docs;
+    this.k = options.k;
+  }
+
+  private preprocessFunc(text: string): string[] {
+    return text.toLowerCase().split(/\s+/);
+  }
+
+  async _getRelevantDocuments(query: string) {
+    const processedQuery = this.preprocessFunc(query);
+    const documents = this.docs.map((doc) => doc.pageContent);
+    const scores = BM25(documents, processedQuery) as number[];
+
+    const scoredDocs = this.docs.map((doc, index) => ({
+      document: doc,
+      score: scores[index],
+    }));
+
+    scoredDocs.sort((a, b) => b.score - a.score);
+
+    return scoredDocs.slice(0, this.k).map((item) => item.document);
+  }
+}

libs/langchain-community/src/retrievers/tests/bm25.test.ts

@@ -0,0 +1,27 @@
+import { expect, test } from "@jest/globals";
+import { Document } from "@langchain/core/documents";
+import { BM25Retriever } from "../bm25.js";
+
+test("BM25Retriever", async () => {
+  const docs = [
+    new Document({
+      pageContent: "The quick brown fox jumps over the lazy dog",
+    }),
+    new Document({
+      pageContent: "A lazy dog sleeps all day",
+    }),
+    new Document({
+      pageContent: "The brown fox is quick and clever",
+    }),
+  ];
+
+  const retriever = BM25Retriever.fromDocuments(docs, {
+    k: 2,
+  });
+  const results = await retriever.invoke("the fox and the dog");
+
+  expect(results).toHaveLength(2);
+  expect(results[0].pageContent).toBe(
+    "The quick brown fox jumps over the lazy dog"
+  );
+});

libs/langchain-community/src/utils/@furkantoprak/bm25/BM25.ts

@@ -0,0 +1,100 @@
+/**
+ * Adapted from
+ * https://github.com/FurkanToprak/OkapiBM25
+ *
+ * Inlined due to CJS import issues.
+ */
+
+/** Gets word count. */
+export const getWordCount = (corpus: string) => {
+  return ((corpus || "").match(/\w+/g) || []).length;
+};
+
+/** Number of occurences of a word in a string. */
+export const getTermFrequency = (term: string, corpus: string) => {
+  return ((corpus || "").match(new RegExp(term, "g")) || []).length;
+};
+
+/** Inverse document frequency. */
+export const getIDF = (term: string, documents: string[]) => {
+  // Number of relevant documents.
+  const relevantDocuments = documents.filter((document: string) =>
+    document.includes(term)
+  ).length;
+  return Math.log(
+    (documents.length - relevantDocuments + 0.5) / (relevantDocuments + 0.5) + 1
+  );
+};
+
+/** Represents a document; useful when sorting results.
+ */
+export interface BMDocument {
+  /** The document is originally scoreed. */
+  document: string;
+  /** The score that the document recieves. */
+  score: number;
+}
+
+/** Constants that are free parameters used in BM25, specifically when generating inverse document frequency. */
+export interface BMConstants {
+  /** Free parameter. Is 0.75 by default.  */
+  b?: number;
+  /** Free parameter. Is 1.2 by default. Generally in range [1.2, 2.0] */
+  k1?: number;
+}
+
+/** If returns positive, the sorting results in secondEl coming before firstEl, else, firstEl comes before secondEL  */
+export type BMSorter = (firstEl: BMDocument, secondEl: BMDocument) => number;
+
+/** Implementation of Okapi BM25 algorithm.
+ *  @param documents: Collection of documents.
+ *  @param keywords: query terms.
+ *  @param constants: Contains free parameters k1 and b. b=0.75 and k1=1.2 by default.
+ *  @param sort: A function that allows you to sort queries by a given rule. If not provided, returns results corresponding to the original order.
+ * If this option is provided, the return type will not be an array of scores but an array of documents with their scores.
+ */
+export function BM25(
+  documents: string[],
+  keywords: string[],
+  constants?: BMConstants,
+  sorter?: BMSorter
+): number[] | BMDocument[] {
+  const b = constants && constants.b ? constants.b : 0.75;
+  const k1 = constants && constants.k1 ? constants.k1 : 1.2;
+  const documentLengths = documents.map((document: string) =>
+    getWordCount(document)
+  );
+  const averageDocumentLength =
+    documentLengths.reduce((a, b) => a + b, 0) / documents.length;
+  const idfByKeyword = keywords.reduce((obj, keyword) => {
+    obj.set(keyword, getIDF(keyword, documents));
+    return obj;
+  }, new Map<string, number>());
+
+  const scores = documents.map((document: string, index: number) => {
+    const score = keywords
+      .map((keyword: string) => {
+        const inverseDocumentFrequency = idfByKeyword.get(keyword);
+        if (inverseDocumentFrequency === undefined) {
+          throw new Error("Missing keyword.");
+        }
+        const termFrequency = getTermFrequency(keyword, document);
+        const documentLength = documentLengths[index];
+        return (
+          (inverseDocumentFrequency * (termFrequency * (k1 + 1))) /
+          (termFrequency +
+            k1 * (1 - b + (b * documentLength) / averageDocumentLength))
+        );
+      })
+      .reduce((a: number, b: number) => a + b, 0);
+    if (sorter) {
+      return { score, document } as BMDocument;
+    }
+    return score;
+  });
+  // sort the results
+  if (sorter) {
+    return (scores as BMDocument[]).sort(sorter);
+  }
+  return scores as number[];
+}

libs/langchain-community/src/utils/@furkantoprak/bm25/LICENSE.md

@@ -0,0 +1,21 @@
+# MIT License
+
+## Copyright (c) 2020 Furkan Toprak
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.