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langchain[minor]: add EnsembleRetriever (#5556)
* langchain[patch]: add support to merge retrievers * Format * Parallelize, lint, format, small fixes * Add entrypoint * Fix import * Add docs and fix build artifacts --------- Co-authored-by: jeasonnow <guyf@seeyon.com> Co-authored-by: jacoblee93 <jacoblee93@gmail.com>
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| Original file line number | Diff line number | Diff line change |
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| # How to combine results from multiple retrievers | ||
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| :::info Prerequisites | ||
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| This guide assumes familiarity with the following concepts: | ||
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| - [Documents](/docs/concepts#document) | ||
| - [Retrievers](/docs/concepts#retrievers) | ||
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| ::: | ||
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| The [EnsembleRetriever](https://api.js.langchain.com/classes/langchain_retrievers_ensemble.EnsembleRetriever.html) supports ensembling of results from multiple retrievers. It is initialized with a list of [BaseRetriever](https://api.js.langchain.com/classes/langchain_core_retrievers.BaseRetriever.html) objects. EnsembleRetrievers rerank the results of the constituent retrievers based on the [Reciprocal Rank Fusion](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf) algorithm. | ||
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| By leveraging the strengths of different algorithms, the `EnsembleRetriever` can achieve better performance than any single algorithm. | ||
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| One useful pattern is to combine a keyword matching retriever with a dense retriever (like embedding similarity), because their strengths are complementary. This can be considered a form of "hybrid search". The sparse retriever is good at finding relevant documents based on keywords, while the dense retriever is good at finding relevant documents based on semantic similarity. | ||
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| Below we demonstrate ensembling of a [simple custom retriever](/docs/how_to/custom_retriever/) that simply returns documents that directly contain the input query with a retriever derived from a [demo, in-memory, vector store](https://api.js.langchain.com/classes/langchain_vectorstores_memory.MemoryVectorStore.html). | ||
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| import CodeBlock from "@theme/CodeBlock"; | ||
| import Example from "@examples/retrievers/ensemble_retriever.ts"; | ||
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| <CodeBlock language="typescript">{Example}</CodeBlock> | ||
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| ## Next steps | ||
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| You've now learned how to combine results from multiple retrievers. | ||
| Next, check out some other retrieval how-to guides, such as how to [improve results using multiple embeddings per document](/docs/how_to/multi_vector) | ||
| or how to [create your own custom retriever](/docs/how_to/custom_retriever). |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,67 @@ | ||
| import { EnsembleRetriever } from "langchain/retrievers/ensemble"; | ||
| import { MemoryVectorStore } from "langchain/vectorstores/memory"; | ||
| import { OpenAIEmbeddings } from "@langchain/openai"; | ||
| import { BaseRetriever, BaseRetrieverInput } from "@langchain/core/retrievers"; | ||
| import { Document } from "@langchain/core/documents"; | ||
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| class SimpleCustomRetriever extends BaseRetriever { | ||
| lc_namespace = []; | ||
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| documents: Document[]; | ||
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| constructor(fields: { documents: Document[] } & BaseRetrieverInput) { | ||
| super(fields); | ||
| this.documents = fields.documents; | ||
| } | ||
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| async _getRelevantDocuments(query: string): Promise<Document[]> { | ||
| return this.documents.filter((document) => | ||
| document.pageContent.includes(query) | ||
| ); | ||
| } | ||
| } | ||
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| const docs1 = [ | ||
| new Document({ pageContent: "I like apples", metadata: { source: 1 } }), | ||
| new Document({ pageContent: "I like oranges", metadata: { source: 1 } }), | ||
| new Document({ | ||
| pageContent: "apples and oranges are fruits", | ||
| metadata: { source: 1 }, | ||
| }), | ||
| ]; | ||
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| const keywordRetriever = new SimpleCustomRetriever({ documents: docs1 }); | ||
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| const docs2 = [ | ||
| new Document({ pageContent: "You like apples", metadata: { source: 2 } }), | ||
| new Document({ pageContent: "You like oranges", metadata: { source: 2 } }), | ||
| ]; | ||
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| const vectorstore = await MemoryVectorStore.fromDocuments( | ||
| docs2, | ||
| new OpenAIEmbeddings() | ||
| ); | ||
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| const vectorstoreRetriever = vectorstore.asRetriever(); | ||
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| const retriever = new EnsembleRetriever({ | ||
| retrievers: [vectorstoreRetriever, keywordRetriever], | ||
| weights: [0.5, 0.5], | ||
| }); | ||
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| const query = "apples"; | ||
| const retrievedDocs = await retriever.invoke(query); | ||
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| console.log(retrievedDocs); | ||
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| /* | ||
| [ | ||
| Document { pageContent: 'You like apples', metadata: { source: 2 } }, | ||
| Document { pageContent: 'I like apples', metadata: { source: 1 } }, | ||
| Document { pageContent: 'You like oranges', metadata: { source: 2 } }, | ||
| Document { | ||
| pageContent: 'apples and oranges are fruits', | ||
| metadata: { source: 1 } | ||
| } | ||
| ] | ||
| */ |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,119 @@ | ||
| import { BaseRetriever, BaseRetrieverInput } from "@langchain/core/retrievers"; | ||
| import { Document, DocumentInterface } from "@langchain/core/documents"; | ||
| import { CallbackManagerForRetrieverRun } from "@langchain/core/callbacks/manager"; | ||
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| export interface EnsembleRetrieverInput extends BaseRetrieverInput { | ||
| /** A list of retrievers to ensemble. */ | ||
| retrievers: BaseRetriever[]; | ||
| /** | ||
| * A list of weights corresponding to the retrievers. Defaults to equal | ||
| * weighting for all retrievers. | ||
| */ | ||
| weights?: number[]; | ||
| /** | ||
| * A constant added to the rank, controlling the balance between the importance | ||
| * of high-ranked items and the consideration given to lower-ranked items. | ||
| * Default is 60. | ||
| */ | ||
| c?: number; | ||
| } | ||
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| /** | ||
| * Ensemble retriever that aggregates and orders the results of | ||
| * multiple retrievers by using weighted Reciprocal Rank Fusion. | ||
| */ | ||
| export class EnsembleRetriever extends BaseRetriever { | ||
| static lc_name() { | ||
| return "EnsembleRetriever"; | ||
| } | ||
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| lc_namespace = ["langchain", "retrievers", "ensemble_retriever"]; | ||
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| retrievers: BaseRetriever[]; | ||
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| weights: number[]; | ||
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| c = 60; | ||
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| constructor(args: EnsembleRetrieverInput) { | ||
| super(args); | ||
| this.retrievers = args.retrievers; | ||
| this.weights = | ||
| args.weights || | ||
| new Array(args.retrievers.length).fill(1 / args.retrievers.length); | ||
| this.c = args.c || 60; | ||
| } | ||
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| async _getRelevantDocuments( | ||
| query: string, | ||
| runManager?: CallbackManagerForRetrieverRun | ||
| ) { | ||
| return this._rankFusion(query, runManager); | ||
| } | ||
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| async _rankFusion( | ||
| query: string, | ||
| runManager?: CallbackManagerForRetrieverRun | ||
| ) { | ||
| const retrieverDocs = await Promise.all( | ||
| this.retrievers.map((retriever, i) => | ||
| retriever.invoke(query, { | ||
| callbacks: runManager?.getChild(`retriever_${i + 1}`), | ||
| }) | ||
| ) | ||
| ); | ||
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| const fusedDocs = await this._weightedReciprocalRank(retrieverDocs); | ||
| return fusedDocs; | ||
| } | ||
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| async _weightedReciprocalRank(docList: DocumentInterface[][]) { | ||
| if (docList.length !== this.weights.length) { | ||
| throw new Error( | ||
| "Number of retrieved document lists must be equal to the number of weights." | ||
| ); | ||
| } | ||
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| const rrfScoreDict = docList.reduce( | ||
| (rffScore: Record<string, number>, retrieverDoc, idx) => { | ||
| let rank = 1; | ||
| const weight = this.weights[idx]; | ||
| while (rank <= retrieverDoc.length) { | ||
| const { pageContent } = retrieverDoc[rank - 1]; | ||
| if (!rffScore[pageContent]) { | ||
| // eslint-disable-next-line no-param-reassign | ||
| rffScore[pageContent] = 0; | ||
| } | ||
| // eslint-disable-next-line no-param-reassign | ||
| rffScore[pageContent] += weight / (rank + this.c); | ||
| rank += 1; | ||
| } | ||
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| return rffScore; | ||
| }, | ||
| {} | ||
| ); | ||
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| const uniqueDocs = this._uniqueUnion(docList.flat()); | ||
| const sortedDocs = Array.from(uniqueDocs).sort( | ||
| (a, b) => rrfScoreDict[b.pageContent] - rrfScoreDict[a.pageContent] | ||
| ); | ||
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| return sortedDocs; | ||
| } | ||
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| private _uniqueUnion(documents: Document[]): Document[] { | ||
| const documentSet = new Set(); | ||
| const result = []; | ||
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| for (const doc of documents) { | ||
| const key = doc.pageContent; | ||
| if (!documentSet.has(key)) { | ||
| documentSet.add(key); | ||
| result.push(doc); | ||
| } | ||
| } | ||
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| return result; | ||
| } | ||
| } |
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