Compression Retriever: Reciprocal Rank Fusion

packages/components/nodes/retrievers/RRFRetriever/RRFRetriever.ts

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+import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { BaseLanguageModel } from 'langchain/base_language'
+import { ContextualCompressionRetriever } from 'langchain/retrievers/contextual_compression'
+import { BaseRetriever } from 'langchain/schema/retriever'
+import { ReciprocalRankFusion } from './ReciprocalRankFusion'
+import { VectorStoreRetriever } from 'langchain/vectorstores/base'
+
+class RRFRetriever_Retrievers implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+    badge: string
+
+    constructor() {
+        this.label = 'Reciprocal Rank Fusion Retriever'
+        this.name = 'RRFRetriever'
+        this.version = 2.0
+        this.type = 'RRFRetriever'
+        this.badge = 'NEW'
+        this.icon = 'compressionRetriever.svg'
+        this.category = 'Retrievers'
+        this.description = 'Reciprocal Rank Fusion to re-rank search results by multiple query generation.'
+        this.baseClasses = [this.type, 'BaseRetriever']
+        this.inputs = [
+            {
+                label: 'Base Retriever',
+                name: 'baseRetriever',
+                type: 'VectorStoreRetriever'
+            },
+            {
+                label: 'Language Model',
+                name: 'model',
+                type: 'BaseLanguageModel'
+            },
+            {
+                label: 'Query Count',
+                name: 'queryCount',
+                description: 'Number of synthetic queries to generate. Default to 4',
+                placeholder: '4',
+                type: 'number',
+                default: 4,
+                additionalParams: true,
+                optional: true
+            },
+            {
+                label: 'Top K',
+                name: 'topK',
+                description: 'Number of top results to fetch. Default to the TopK of the Base Retriever',
+                placeholder: '0',
+                type: 'number',
+                default: 0,
+                additionalParams: true,
+                optional: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const llm = nodeData.inputs?.model as BaseLanguageModel
+        const baseRetriever = nodeData.inputs?.baseRetriever as BaseRetriever
+        const queryCount = nodeData.inputs?.queryCount as string
+        const q = queryCount ? parseFloat(queryCount) : 4
+        const topK = nodeData.inputs?.topK as string
+        let k = topK ? parseFloat(topK) : 4
+
+        if (k <= 0) {
+            k = (baseRetriever as VectorStoreRetriever).k
+        }
+
+        const ragFusion = new ReciprocalRankFusion(llm, baseRetriever as VectorStoreRetriever, q, k)
+        return new ContextualCompressionRetriever({
+            baseCompressor: ragFusion,
+            baseRetriever: baseRetriever
+        })
+    }
+}
+
+module.exports = { nodeClass: RRFRetriever_Retrievers }

packages/components/nodes/retrievers/RRFRetriever/ReciprocalRankFusion.ts

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+import { BaseDocumentCompressor } from 'langchain/retrievers/document_compressors'
+import { Document } from 'langchain/document'
+import { Callbacks } from 'langchain/callbacks'
+import { BaseLanguageModel } from 'langchain/base_language'
+import { ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate } from 'langchain/prompts'
+import { LLMChain } from 'langchain/chains'
+import { VectorStoreRetriever } from 'langchain/vectorstores/base'
+
+export class ReciprocalRankFusion extends BaseDocumentCompressor {
+    private readonly llm: BaseLanguageModel
+    private readonly queryCount: number
+    private readonly topK: number
+    private baseRetriever: VectorStoreRetriever
+    constructor(llm: BaseLanguageModel, baseRetriever: VectorStoreRetriever, queryCount: number, topK: number) {
+        super()
+        this.queryCount = queryCount
+        this.llm = llm
+        this.baseRetriever = baseRetriever
+        this.topK = topK
+    }
+    async compressDocuments(
+        documents: Document<Record<string, any>>[],
+        query: string,
+        _?: Callbacks | undefined
+    ): Promise<Document<Record<string, any>>[]> {
+        // avoid empty api call
+        if (documents.length === 0) {
+            return []
+        }
+        const chatPrompt = ChatPromptTemplate.fromMessages([
+            SystemMessagePromptTemplate.fromTemplate(
+                'You are a helpful assistant that generates multiple search queries based on a single input query.'
+            ),
+            HumanMessagePromptTemplate.fromTemplate(
+                'Generate multiple search queries related to: {input}. Provide these alternative questions separated by newlines, do not add any numbers.'
+            ),
+            HumanMessagePromptTemplate.fromTemplate('OUTPUT (' + this.queryCount + ' queries):')
+        ])
+        const llmChain = new LLMChain({
+            llm: this.llm,
+            prompt: chatPrompt
+        })
+        const multipleQueries = await llmChain.call({ input: query })
+        const queries = []
+        queries.push(query)
+        multipleQueries.text.split('\n').map((q: string) => {
+            queries.push(q)
+        })
+        console.log(JSON.stringify(queries))
+        const docList: Document<Record<string, any>>[][] = []
+        for (let i = 0; i < queries.length; i++) {
+            const resultOne = await this.baseRetriever.vectorStore.similaritySearch(queries[i], 5)
+            const docs: any[] = []
+            resultOne.forEach((doc) => {
+                docs.push(doc)
+            })
+            docList.push(docs)
+        }
+
+        return this.reciprocalRankFunction(docList, 60)
+    }
+
+    reciprocalRankFunction(docList: Document<Record<string, any>>[][], k: number): Document<Record<string, any>>[] {
+        docList.forEach((docs: Document<Record<string, any>>[]) => {
+            docs.forEach((doc: any, index: number) => {
+                let rank = index + 1
+                if (doc.metadata.relevancy_score) {
+                    doc.metadata.relevancy_score += 1 / (rank + k)
+                } else {
+                    doc.metadata.relevancy_score = 1 / (rank + k)
+                }
+            })
+        })
+        const scoreArray: any[] = []
+        docList.forEach((docs: Document<Record<string, any>>[]) => {
+            docs.forEach((doc: any) => {
+                scoreArray.push(doc.metadata.relevancy_score)
+            })
+        })
+        scoreArray.sort((a, b) => b - a)
+        const rerankedDocuments: Document<Record<string, any>>[] = []
+        const seenScores: any[] = []
+        scoreArray.forEach((score) => {
+            docList.forEach((docs) => {
+                docs.forEach((doc: any) => {
+                    if (doc.metadata.relevancy_score === score && seenScores.indexOf(score) === -1) {
+                        rerankedDocuments.push(doc)
+                        seenScores.push(doc.metadata.relevancy_score)
+                    }
+                })
+            })
+        })
+        return rerankedDocuments.splice(0, this.topK)
+    }
+}

packages/components/nodes/retrievers/RRFRetriever/compressionRetriever.svg

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+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-chart-bar" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
+   <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+   <path d="M3 12m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v6a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
+   <path d="M9 8m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v10a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
+   <path d="M15 4m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v14a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
+   <path d="M4 20l14 0" />
+</svg>