119 lines
4.1 KiB
TypeScript
119 lines
4.1 KiB
TypeScript
import { VectorStore } from 'langchain/vectorstores/base'
|
|
import { INode, INodeData, INodeParams, INodeOutputsValue } from '../../../src/Interface'
|
|
import { handleEscapeCharacters } from '../../../src'
|
|
import { ScoreThresholdRetriever } from 'langchain/retrievers/score_threshold'
|
|
|
|
class SimilarityThresholdRetriever_Retrievers implements INode {
|
|
label: string
|
|
name: string
|
|
version: number
|
|
description: string
|
|
type: string
|
|
icon: string
|
|
category: string
|
|
baseClasses: string[]
|
|
inputs: INodeParams[]
|
|
outputs: INodeOutputsValue[]
|
|
|
|
constructor() {
|
|
this.label = 'Similarity Score Threshold Retriever'
|
|
this.name = 'similarityThresholdRetriever'
|
|
this.version = 2.0
|
|
this.type = 'SimilarityThresholdRetriever'
|
|
this.icon = 'similaritythreshold.svg'
|
|
this.category = 'Retrievers'
|
|
this.description = 'Return results based on the minimum similarity percentage'
|
|
this.baseClasses = [this.type, 'BaseRetriever']
|
|
this.inputs = [
|
|
{
|
|
label: 'Vector Store',
|
|
name: 'vectorStore',
|
|
type: 'VectorStore'
|
|
},
|
|
{
|
|
label: 'Query',
|
|
name: 'query',
|
|
type: 'string',
|
|
description: 'Query to retrieve documents from retriever. If not specified, user question will be used',
|
|
optional: true,
|
|
acceptVariable: true
|
|
},
|
|
{
|
|
label: 'Minimum Similarity Score (%)',
|
|
name: 'minSimilarityScore',
|
|
description: 'Finds results with at least this similarity score',
|
|
type: 'number',
|
|
default: 80,
|
|
step: 1
|
|
},
|
|
{
|
|
label: 'Max K',
|
|
name: 'maxK',
|
|
description: `The maximum number of results to fetch`,
|
|
type: 'number',
|
|
default: 20,
|
|
step: 1,
|
|
additionalParams: true
|
|
},
|
|
{
|
|
label: 'K Increment',
|
|
name: 'kIncrement',
|
|
description: `How much to increase K by each time. It'll fetch N results, then N + kIncrement, then N + kIncrement * 2, etc.`,
|
|
type: 'number',
|
|
default: 2,
|
|
step: 1,
|
|
additionalParams: true
|
|
}
|
|
]
|
|
this.outputs = [
|
|
{
|
|
label: 'Similarity Threshold Retriever',
|
|
name: 'retriever',
|
|
baseClasses: this.baseClasses
|
|
},
|
|
{
|
|
label: 'Document',
|
|
name: 'document',
|
|
baseClasses: ['Document']
|
|
},
|
|
{
|
|
label: 'Text',
|
|
name: 'text',
|
|
baseClasses: ['string', 'json']
|
|
}
|
|
]
|
|
}
|
|
|
|
async init(nodeData: INodeData, input: string): Promise<any> {
|
|
const vectorStore = nodeData.inputs?.vectorStore as VectorStore
|
|
const minSimilarityScore = nodeData.inputs?.minSimilarityScore as number
|
|
const query = nodeData.inputs?.query as string
|
|
const maxK = nodeData.inputs?.maxK as string
|
|
const kIncrement = nodeData.inputs?.kIncrement as string
|
|
|
|
const output = nodeData.outputs?.output as string
|
|
|
|
const retriever = ScoreThresholdRetriever.fromVectorStore(vectorStore, {
|
|
minSimilarityScore: minSimilarityScore ? minSimilarityScore / 100 : 0.9,
|
|
maxK: maxK ? parseInt(maxK, 10) : 100,
|
|
kIncrement: kIncrement ? parseInt(kIncrement, 10) : 2
|
|
})
|
|
|
|
if (output === 'retriever') return retriever
|
|
else if (output === 'document') return await retriever.getRelevantDocuments(query ? query : input)
|
|
else if (output === 'text') {
|
|
let finaltext = ''
|
|
|
|
const docs = await retriever.getRelevantDocuments(query ? query : input)
|
|
|
|
for (const doc of docs) finaltext += `${doc.pageContent}\n`
|
|
|
|
return handleEscapeCharacters(finaltext, false)
|
|
}
|
|
|
|
return retriever
|
|
}
|
|
}
|
|
|
|
module.exports = { nodeClass: SimilarityThresholdRetriever_Retrievers }
|