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Merge pull request #1012 from FlowiseAI/feature/SimilarityThresholdRetriever
Feature/Similarity Threshold Retriever
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import { VectorStore } from 'langchain/vectorstores/base'
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import { INode, INodeData, INodeParams, INodeOutputsValue } from '../../../src/Interface'
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import { handleEscapeCharacters } from '../../../src'
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import { ScoreThresholdRetriever } from 'langchain/retrievers/score_threshold'
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class SimilarityThresholdRetriever_Retrievers implements INode {
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label: string
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name: string
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version: number
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description: string
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type: string
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icon: string
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category: string
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baseClasses: string[]
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inputs: INodeParams[]
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outputs: INodeOutputsValue[]
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constructor() {
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this.label = 'Similarity Score Threshold Retriever'
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this.name = 'similarityThresholdRetriever'
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this.version = 1.0
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this.type = 'SimilarityThresholdRetriever'
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this.icon = 'similaritythreshold.svg'
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this.category = 'Retrievers'
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this.description = 'Return results based on the minimum similarity percentage'
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this.baseClasses = [this.type, 'BaseRetriever']
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this.inputs = [
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{
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label: 'Vector Store',
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name: 'vectorStore',
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type: 'VectorStore'
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},
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{
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label: 'Minimum Similarity Score (%)',
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name: 'minSimilarityScore',
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description: 'Finds results with at least this similarity score',
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type: 'number',
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default: 80,
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step: 1
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},
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{
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label: 'Max K',
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name: 'maxK',
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description: `The maximum number of results to fetch`,
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type: 'number',
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default: 20,
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step: 1
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},
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{
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label: 'K Increment',
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name: 'kIncrement',
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description: `How much to increase K by each time. It'll fetch N results, then N + kIncrement, then N + kIncrement * 2, etc.`,
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type: 'number',
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default: 2,
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step: 1
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}
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]
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this.outputs = [
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{
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label: 'Similarity Threshold Retriever',
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name: 'retriever',
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baseClasses: this.baseClasses
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},
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{
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label: 'Document',
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name: 'document',
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baseClasses: ['Document']
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},
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{
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label: 'Text',
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name: 'text',
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baseClasses: ['string', 'json']
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}
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]
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}
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async init(nodeData: INodeData, input: string): Promise<any> {
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const vectorStore = nodeData.inputs?.vectorStore as VectorStore
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const minSimilarityScore = nodeData.inputs?.minSimilarityScore as number
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const maxK = nodeData.inputs?.maxK as string
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const kIncrement = nodeData.inputs?.kIncrement as string
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const output = nodeData.outputs?.output as string
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const retriever = ScoreThresholdRetriever.fromVectorStore(vectorStore, {
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minSimilarityScore: minSimilarityScore ? minSimilarityScore / 100 : 0.9,
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maxK: maxK ? parseInt(maxK, 10) : 100,
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kIncrement: kIncrement ? parseInt(kIncrement, 10) : 2
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})
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if (output === 'retriever') return retriever
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else if (output === 'document') return await retriever.getRelevantDocuments(input)
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else if (output === 'text') {
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let finaltext = ''
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const docs = await retriever.getRelevantDocuments(input)
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for (const doc of docs) finaltext += `${doc.pageContent}\n`
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return handleEscapeCharacters(finaltext, false)
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}
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return retriever
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}
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}
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module.exports = { nodeClass: SimilarityThresholdRetriever_Retrievers }
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+5
@@ -0,0 +1,5 @@
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<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-chart-line" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
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<path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
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<path d="M4 19l16 0"></path>
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<path d="M4 15l4 -6l4 2l4 -5l4 4"></path>
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</svg>
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