122 lines
4.1 KiB
TypeScript
122 lines
4.1 KiB
TypeScript
import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
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import { Embeddings } from 'langchain/embeddings/base'
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import { Document } from 'langchain/document'
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import { getBaseClasses } from '../../../src/utils'
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import { FaissStore } from 'langchain/vectorstores/faiss'
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import { flatten } from 'lodash'
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class FaissUpsert_VectorStores 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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badge: 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 = 'Faiss Upsert Document'
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this.name = 'faissUpsert'
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this.version = 1.0
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this.type = 'Faiss'
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this.icon = 'faiss.svg'
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this.category = 'Vector Stores'
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this.description = 'Upsert documents to Faiss'
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this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
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this.badge = 'DEPRECATING'
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this.inputs = [
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{
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label: 'Document',
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name: 'document',
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type: 'Document',
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list: true
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},
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{
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label: 'Embeddings',
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name: 'embeddings',
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type: 'Embeddings'
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},
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{
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label: 'Base Path to store',
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name: 'basePath',
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description: 'Path to store faiss.index file',
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placeholder: `C:\\Users\\User\\Desktop`,
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type: 'string'
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},
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{
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label: 'Top K',
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name: 'topK',
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description: 'Number of top results to fetch. Default to 4',
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placeholder: '4',
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type: 'number',
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additionalParams: true,
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optional: true
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}
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]
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this.outputs = [
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{
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label: 'Faiss 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: 'Faiss Vector Store',
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name: 'vectorStore',
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baseClasses: [this.type, ...getBaseClasses(FaissStore)]
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}
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]
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}
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async init(nodeData: INodeData): Promise<any> {
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const docs = nodeData.inputs?.document as Document[]
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const embeddings = nodeData.inputs?.embeddings as Embeddings
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const output = nodeData.outputs?.output as string
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const basePath = nodeData.inputs?.basePath as string
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const topK = nodeData.inputs?.topK as string
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const k = topK ? parseFloat(topK) : 4
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const flattenDocs = docs && docs.length ? flatten(docs) : []
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const finalDocs = []
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for (let i = 0; i < flattenDocs.length; i += 1) {
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if (flattenDocs[i] && flattenDocs[i].pageContent) {
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finalDocs.push(new Document(flattenDocs[i]))
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}
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}
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const vectorStore = await FaissStore.fromDocuments(finalDocs, embeddings)
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await vectorStore.save(basePath)
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// Avoid illegal invocation error
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vectorStore.similaritySearchVectorWithScore = async (query: number[], k: number) => {
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const index = vectorStore.index
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if (k > index.ntotal()) {
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const total = index.ntotal()
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console.warn(`k (${k}) is greater than the number of elements in the index (${total}), setting k to ${total}`)
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k = total
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}
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const result = index.search(query, k)
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return result.labels.map((id, index) => {
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const uuid = vectorStore._mapping[id]
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return [vectorStore.docstore.search(uuid), result.distances[index]] as [Document, number]
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})
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}
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if (output === 'retriever') {
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const retriever = vectorStore.asRetriever(k)
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return retriever
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} else if (output === 'vectorStore') {
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;(vectorStore as any).k = k
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return vectorStore
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}
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return vectorStore
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}
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}
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module.exports = { nodeClass: FaissUpsert_VectorStores }
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