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BM25, also known as Okapi BM25, is a ranking function used in information retrieval systems to estimate the relevance of documents to a given search query. 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.

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Setup

The BM25Retriever is exported from @langchain/community. You’ll need to install it like this: 
npm install @langchain/community @langchain/core
This retriever uses code from this implementation of Okapi BM25.

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Usage

You can now create a new retriever with previously retrieved documents: 
import { BM25Retriever } from "@langchain/community/retrievers/bm25";

const retriever = BM25Retriever.fromDocuments([
  { pageContent: "Buildings are made out of brick", metadata: {} },
  { pageContent: "Buildings are made out of wood", metadata: {} },
  { pageContent: "Buildings are made out of stone", metadata: {} },
  { pageContent: "Cars are made out of metal", metadata: {} },
  { pageContent: "Cars are made out of plastic", metadata: {} },
  { pageContent: "mitochondria is the powerhouse of the cell", metadata: {} },
  { pageContent: "mitochondria is made of lipids", metadata: {} },
], { k: 4 });

// Will return the 4 documents reranked by the BM25 algorithm
await retriever.invoke("mitochondria");

[
  { pageContent: 'mitochondria is made of lipids', metadata: {} },
  {
    pageContent: 'mitochondria is the powerhouse of the cell',
    metadata: {}
  },
  { pageContent: 'Buildings are made out of brick', metadata: {} },
  { pageContent: 'Buildings are made out of wood', metadata: {} }
]
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