import { DataSourceOptions } from 'typeorm' import { Pool } from 'pg' import { Embeddings } from '@langchain/core/embeddings' import { Document } from '@langchain/core/documents' import { TypeORMVectorStore, TypeORMVectorStoreDocument } from '@langchain/community/vectorstores/typeorm' import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface' import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils' class Postgres_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 = 'Postgres Load Existing Index' this.name = 'postgresExistingIndex' this.version = 2.0 this.type = 'Postgres' this.icon = 'postgres.svg' this.category = 'Vector Stores' this.description = 'Load existing index from Postgres using pgvector (i.e: Document has been upserted)' this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever'] this.badge = 'DEPRECATING' this.credential = { label: 'Connect Credential', name: 'credential', type: 'credential', credentialNames: ['PostgresApi'] } this.inputs = [ { label: 'Embeddings', name: 'embeddings', type: 'Embeddings' }, { label: 'Host', name: 'host', type: 'string' }, { label: 'Database', name: 'database', type: 'string' }, { label: 'SSL Connection', name: 'sslConnection', type: 'boolean', default: false, optional: false }, { label: 'Port', name: 'port', type: 'number', placeholder: '6432', optional: true }, { label: 'Table Name', name: 'tableName', type: 'string', placeholder: 'documents', additionalParams: true, optional: true }, { label: 'Additional Configuration', name: 'additionalConfig', type: 'json', additionalParams: true, optional: 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: 'Postgres Retriever', name: 'retriever', baseClasses: this.baseClasses }, { label: 'Postgres Vector Store', name: 'vectorStore', baseClasses: [this.type, ...getBaseClasses(TypeORMVectorStore)] } ] } async init(nodeData: INodeData, _: string, options: ICommonObject): Promise { const credentialData = await getCredentialData(nodeData.credential ?? '', options) const user = getCredentialParam('user', credentialData, nodeData) const password = getCredentialParam('password', credentialData, nodeData) const _tableName = nodeData.inputs?.tableName as string const tableName = _tableName ? _tableName : 'documents' const embeddings = nodeData.inputs?.embeddings as Embeddings const additionalConfig = nodeData.inputs?.additionalConfig as string const output = nodeData.outputs?.output as string const topK = nodeData.inputs?.topK as string const k = topK ? parseFloat(topK) : 4 const sslConnection = nodeData.inputs?.sslConnection as boolean let additionalConfiguration = {} if (additionalConfig) { try { additionalConfiguration = typeof additionalConfig === 'object' ? additionalConfig : JSON.parse(additionalConfig) } catch (exception) { throw new Error('Invalid JSON in the Additional Configuration: ' + exception) } } const postgresConnectionOptions = { ...additionalConfiguration, type: 'postgres', host: nodeData.inputs?.host as string, port: nodeData.inputs?.port as number, username: user, password: password, database: nodeData.inputs?.database as string, ssl: sslConnection } const args = { postgresConnectionOptions: postgresConnectionOptions as DataSourceOptions, tableName: tableName } const vectorStore = await TypeORMVectorStore.fromDataSource(embeddings, args) // Rewrite the method to use pg pool connection instead of the default connection /* Otherwise a connection error is displayed when the chain tries to execute the function [chain/start] [1:chain:ConversationalRetrievalQAChain] Entering Chain run with input: { "question": "what the document is about", "chat_history": [] } [retriever/start] [1:chain:ConversationalRetrievalQAChain > 2:retriever:VectorStoreRetriever] Entering Retriever run with input: { "query": "what the document is about" } [ERROR]: uncaughtException: Illegal invocation TypeError: Illegal invocation at Socket.ref (node:net:1524:18) at Connection.ref (.../node_modules/pg/lib/connection.js:183:17) at Client.ref (.../node_modules/pg/lib/client.js:591:21) at BoundPool._pulseQueue (/node_modules/pg-pool/index.js:148:28) at .../node_modules/pg-pool/index.js:184:37 at process.processTicksAndRejections (node:internal/process/task_queues:77:11) */ vectorStore.similaritySearchVectorWithScore = async (query: number[], k: number, filter?: any) => { const embeddingString = `[${query.join(',')}]` const _filter = filter ?? '{}' const queryString = ` SELECT *, embedding <=> $1 as "_distance" FROM ${tableName} WHERE metadata @> $2 ORDER BY "_distance" ASC LIMIT $3;` const poolOptions = { host: postgresConnectionOptions.host, port: postgresConnectionOptions.port, user: postgresConnectionOptions.username, password: postgresConnectionOptions.password, database: postgresConnectionOptions.database } const pool = new Pool(poolOptions) const conn = await pool.connect() const documents = await conn.query(queryString, [embeddingString, _filter, k]) conn.release() const results = [] as [TypeORMVectorStoreDocument, number][] for (const doc of documents.rows) { if (doc._distance != null && doc.pageContent != null) { const document = new Document(doc) as TypeORMVectorStoreDocument document.id = doc.id results.push([document, doc._distance]) } } return results } if (output === 'retriever') { const retriever = vectorStore.asRetriever(k) return retriever } else if (output === 'vectorStore') { ;(vectorStore as any).k = k return vectorStore } return vectorStore } } module.exports = { nodeClass: Postgres_Existing_VectorStores }