import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface' import { ZepVectorStore, IZepConfig } from 'langchain/vectorstores/zep' import { Embeddings } from 'langchain/embeddings/base' import { Document } from 'langchain/document' import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils' import { IDocument, ZepClient } from '@getzep/zep-js' class Zep_Existing_VectorStores implements INode { label: string name: string version: number description: string type: string icon: string category: string badge: string baseClasses: string[] inputs: INodeParams[] credential: INodeParams outputs: INodeOutputsValue[] constructor() { this.label = 'Zep Load Existing Index' this.name = 'zepExistingIndex' this.version = 1.0 this.type = 'Zep' this.icon = 'zep.svg' this.category = 'Vector Stores' this.description = 'Load existing index from Zep (i.e: Document has been upserted)' this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever'] this.badge = 'DEPRECATING' this.credential = { label: 'Connect Credential', name: 'credential', type: 'credential', optional: true, description: 'Configure JWT authentication on your Zep instance (Optional)', credentialNames: ['zepMemoryApi'] } this.inputs = [ { label: 'Embeddings', name: 'embeddings', type: 'Embeddings' }, { label: 'Base URL', name: 'baseURL', type: 'string', default: 'http://127.0.0.1:8000' }, { label: 'Zep Collection', name: 'zepCollection', type: 'string', placeholder: 'my-first-collection' }, { label: 'Zep Metadata Filter', name: 'zepMetadataFilter', type: 'json', optional: true, additionalParams: true }, { label: 'Embedding Dimension', name: 'dimension', type: 'number', default: 1536, additionalParams: true }, { label: 'Top K', name: 'topK', description: 'Number of top results to fetch. Default to 4', placeholder: '4', type: 'number', additionalParams: true, optional: true } ] this.outputs = [ { label: 'Zep Retriever', name: 'retriever', baseClasses: this.baseClasses }, { label: 'Zep Vector Store', name: 'vectorStore', baseClasses: [this.type, ...getBaseClasses(ZepVectorStore)] } ] } async init(nodeData: INodeData, _: string, options: ICommonObject): Promise { const baseURL = nodeData.inputs?.baseURL as string const zepCollection = nodeData.inputs?.zepCollection as string const zepMetadataFilter = nodeData.inputs?.zepMetadataFilter const dimension = nodeData.inputs?.dimension as number const embeddings = nodeData.inputs?.embeddings as Embeddings const output = nodeData.outputs?.output as string const topK = nodeData.inputs?.topK as string const k = topK ? parseFloat(topK) : 4 const credentialData = await getCredentialData(nodeData.credential ?? '', options) const apiKey = getCredentialParam('apiKey', credentialData, nodeData) const zepConfig: IZepConfig & Partial = { apiUrl: baseURL, collectionName: zepCollection, embeddingDimensions: dimension, isAutoEmbedded: false } if (apiKey) zepConfig.apiKey = apiKey if (zepMetadataFilter) { const metadatafilter = typeof zepMetadataFilter === 'object' ? zepMetadataFilter : JSON.parse(zepMetadataFilter) zepConfig.filter = metadatafilter } const vectorStore = await ZepExistingVS.fromExistingIndex(embeddings, zepConfig) if (output === 'retriever') { const retriever = vectorStore.asRetriever(k) return retriever } else if (output === 'vectorStore') { ;(vectorStore as any).k = k return vectorStore } return vectorStore } } interface ZepFilter { filter: Record } function zepDocsToDocumentsAndScore(results: IDocument[]): [Document, number][] { return results.map((d) => [ new Document({ pageContent: d.content, metadata: d.metadata }), d.score ? d.score : 0 ]) } function assignMetadata(value: string | Record | object | undefined): Record | undefined { if (typeof value === 'object' && value !== null) { return value as Record } if (value !== undefined) { console.warn('Metadata filters must be an object, Record, or undefined.') } return undefined } class ZepExistingVS extends ZepVectorStore { filter?: Record args?: IZepConfig & Partial constructor(embeddings: Embeddings, args: IZepConfig & Partial) { super(embeddings, args) this.filter = args.filter this.args = args } async initalizeCollection(args: IZepConfig & Partial) { this.client = await ZepClient.init(args.apiUrl, args.apiKey) try { this.collection = await this.client.document.getCollection(args.collectionName) } catch (err) { if (err instanceof Error) { if (err.name === 'NotFoundError') { await this.createNewCollection(args) } else { throw err } } } } async createNewCollection(args: IZepConfig & Partial) { if (!args.embeddingDimensions) { throw new Error( `Collection ${args.collectionName} not found. You can create a new Collection by providing embeddingDimensions.` ) } this.collection = await this.client.document.addCollection({ name: args.collectionName, description: args.description, metadata: args.metadata, embeddingDimensions: args.embeddingDimensions, isAutoEmbedded: false }) } async similaritySearchVectorWithScore( query: number[], k: number, filter?: Record | undefined ): Promise<[Document, number][]> { if (filter && this.filter) { throw new Error('cannot provide both `filter` and `this.filter`') } const _filters = filter ?? this.filter const ANDFilters = [] for (const filterKey in _filters) { let filterVal = _filters[filterKey] if (typeof filterVal === 'string') filterVal = `"${filterVal}"` ANDFilters.push({ jsonpath: `$[*] ? (@.${filterKey} == ${filterVal})` }) } const newfilter = { where: { and: ANDFilters } } await this.initalizeCollection(this.args!).catch((err) => { console.error('Error initializing collection:', err) throw err }) const results = await this.collection.search( { embedding: new Float32Array(query), metadata: assignMetadata(newfilter) }, k ) return zepDocsToDocumentsAndScore(results) } static async fromExistingIndex(embeddings: Embeddings, dbConfig: IZepConfig & Partial): Promise { const instance = new this(embeddings, dbConfig) return instance } } module.exports = { nodeClass: Zep_Existing_VectorStores }