const { QdrantClient } = require("@qdrant/js-client-rest"); const { RecursiveCharacterTextSplitter } = require("langchain/text_splitter"); const { storeVectorResult, cachedVectorInformation } = require("../../files"); const { v4: uuidv4 } = require("uuid"); const { toChunks, getLLMProvider } = require("../../helpers"); const QDrant = { name: "QDrant", connect: async function () { if (process.env.VECTOR_DB !== "qdrant") throw new Error("QDrant::Invalid ENV settings"); const client = new QdrantClient({ url: process.env.QDRANT_ENDPOINT, ...(process.env.QDRANT_API_KEY ? { apiKey: process.env.QDRANT_API_KEY } : {}), }); const isAlive = (await client.api("cluster")?.clusterStatus())?.ok || false; if (!isAlive) throw new Error( "QDrant::Invalid Heartbeat received - is the instance online?" ); return { client }; }, heartbeat: async function () { await this.connect(); return { heartbeat: Number(new Date()) }; }, totalVectors: async function () { const { client } = await this.connect(); const { collections } = await client.getCollections(); var totalVectors = 0; for (const collection of collections) { if (!collection || !collection.name) continue; totalVectors += (await this.namespace(client, collection.name))?.vectorCount || 0; } return totalVectors; }, namespaceCount: async function (_namespace = null) { const { client } = await this.connect(); const namespace = await this.namespace(client, _namespace); return namespace?.vectorCount || 0; }, similarityResponse: async function ( _client, namespace, queryVector, similarityThreshold = 0.25 ) { const { client } = await this.connect(); const result = { contextTexts: [], sourceDocuments: [], scores: [], }; const responses = await client.search(namespace, { vector: queryVector, limit: 4, with_payload: true, }); responses.forEach((response) => { if (response.score < similarityThreshold) return; result.contextTexts.push(response?.payload?.text || ""); result.sourceDocuments.push({ ...(response?.payload || {}), id: response.id, }); result.scores.push(response.score); }); return result; }, namespace: async function (client, namespace = null) { if (!namespace) throw new Error("No namespace value provided."); const collection = await client.getCollection(namespace).catch(() => null); if (!collection) return null; return { name: namespace, ...collection, vectorCount: collection.vectors_count, }; }, hasNamespace: async function (namespace = null) { if (!namespace) return false; const { client } = await this.connect(); return await this.namespaceExists(client, namespace); }, namespaceExists: async function (client, namespace = null) { if (!namespace) throw new Error("No namespace value provided."); const collection = await client.getCollection(namespace).catch((e) => { console.error("QDrant::namespaceExists", e.message); return null; }); return !!collection; }, deleteVectorsInNamespace: async function (client, namespace = null) { await client.deleteCollection(namespace); return true; }, getOrCreateCollection: async function (client, namespace) { if (await this.namespaceExists(client, namespace)) { return await client.getCollection(namespace); } await client.createCollection(namespace, { vectors: { size: 1536, //TODO: Fixed to OpenAI models - when other embeddings exist make variable. distance: "Cosine", }, }); return await client.getCollection(namespace); }, addDocumentToNamespace: async function ( namespace, documentData = {}, fullFilePath = null ) { const { DocumentVectors } = require("../../../models/vectors"); try { const { pageContent, docId, ...metadata } = documentData; if (!pageContent || pageContent.length == 0) return false; console.log("Adding new vectorized document into namespace", namespace); const cacheResult = await cachedVectorInformation(fullFilePath); if (cacheResult.exists) { const { client } = await this.connect(); const collection = await this.getOrCreateCollection(client, namespace); if (!collection) throw new Error("Failed to create new QDrant collection!", { namespace, }); const { chunks } = cacheResult; const documentVectors = []; for (const chunk of chunks) { const submission = { ids: [], vectors: [], payloads: [], }; // Before sending to Qdrant and saving the records to our db // we need to assign the id of each chunk that is stored in the cached file. chunk.forEach((chunk) => { const id = uuidv4(); const { id: _id, ...payload } = chunk.payload; documentVectors.push({ docId, vectorId: id }); submission.ids.push(id); submission.vectors.push(chunk.vector); submission.payloads.push(payload); }); const additionResult = await client.upsert(namespace, { wait: true, batch: { ...submission }, }); if (additionResult?.status !== "completed") throw new Error("Error embedding into QDrant", additionResult); } await DocumentVectors.bulkInsert(documentVectors); return true; } // If we are here then we are going to embed and store a novel document. // We have to do this manually as opposed to using LangChains `Qdrant.fromDocuments` // because we then cannot atomically control our namespace to granularly find/remove documents // from vectordb. const textSplitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000, chunkOverlap: 20, }); const textChunks = await textSplitter.splitText(pageContent); console.log("Chunks created from document:", textChunks.length); const LLMConnector = getLLMProvider(); const documentVectors = []; const vectors = []; const vectorValues = await LLMConnector.embedChunks(textChunks); const submission = { ids: [], vectors: [], payloads: [], }; if (!!vectorValues && vectorValues.length > 0) { for (const [i, vector] of vectorValues.entries()) { const vectorRecord = { id: uuidv4(), vector: vector, // [DO NOT REMOVE] // LangChain will be unable to find your text if you embed manually and dont include the `text` key. // https://github.com/hwchase17/langchainjs/blob/2def486af734c0ca87285a48f1a04c057ab74bdf/langchain/src/vectorstores/pinecone.ts#L64 payload: { ...metadata, text: textChunks[i] }, }; submission.ids.push(vectorRecord.id); submission.vectors.push(vectorRecord.vector); submission.payloads.push(vectorRecord.payload); vectors.push(vectorRecord); documentVectors.push({ docId, vectorId: vectorRecord.id }); } } else { throw new Error( "Could not embed document chunks! This document will not be recorded." ); } const { client } = await this.connect(); const collection = await this.getOrCreateCollection(client, namespace); if (!collection) throw new Error("Failed to create new QDrant collection!", { namespace, }); if (vectors.length > 0) { const chunks = []; console.log("Inserting vectorized chunks into QDrant collection."); for (const chunk of toChunks(vectors, 500)) chunks.push(chunk); const additionResult = await client.upsert(namespace, { wait: true, batch: { ids: submission.ids, vectors: submission.vectors, payloads: submission.payloads, }, }); if (additionResult?.status !== "completed") throw new Error("Error embedding into QDrant", additionResult); await storeVectorResult(chunks, fullFilePath); } await DocumentVectors.bulkInsert(documentVectors); return true; } catch (e) { console.error(e); console.error("addDocumentToNamespace", e.message); return false; } }, deleteDocumentFromNamespace: async function (namespace, docId) { const { DocumentVectors } = require("../../../models/vectors"); const { client } = await this.connect(); if (!(await this.namespaceExists(client, namespace))) return; const knownDocuments = await DocumentVectors.where({ docId }); if (knownDocuments.length === 0) return; const vectorIds = knownDocuments.map((doc) => doc.vectorId); await client.delete(namespace, { wait: true, points: vectorIds, }); const indexes = knownDocuments.map((doc) => doc.id); await DocumentVectors.deleteIds(indexes); return true; }, performSimilaritySearch: async function ({ namespace = null, input = "", LLMConnector = null, similarityThreshold = 0.25, }) { if (!namespace || !input || !LLMConnector) throw new Error("Invalid request to performSimilaritySearch."); const { client } = await this.connect(); if (!(await this.namespaceExists(client, namespace))) { return { contextTexts: [], sources: [], message: "Invalid query - no documents found for workspace!", }; } const queryVector = await LLMConnector.embedTextInput(input); const { contextTexts, sourceDocuments } = await this.similarityResponse( client, namespace, queryVector, similarityThreshold ); const sources = sourceDocuments.map((metadata, i) => { return { ...metadata, text: contextTexts[i] }; }); return { contextTexts, sources: this.curateSources(sources), message: false, }; }, "namespace-stats": async function (reqBody = {}) { const { namespace = null } = reqBody; if (!namespace) throw new Error("namespace required"); const { client } = await this.connect(); if (!(await this.namespaceExists(client, namespace))) throw new Error("Namespace by that name does not exist."); const stats = await this.namespace(client, namespace); return stats ? stats : { message: "No stats were able to be fetched from DB for namespace" }; }, "delete-namespace": async function (reqBody = {}) { const { namespace = null } = reqBody; const { client } = await this.connect(); if (!(await this.namespaceExists(client, namespace))) throw new Error("Namespace by that name does not exist."); const details = await this.namespace(client, namespace); await this.deleteVectorsInNamespace(client, namespace); return { message: `Namespace ${namespace} was deleted along with ${details?.vectorCount} vectors.`, }; }, reset: async function () { const { client } = await this.connect(); const response = await client.getCollections(); for (const collection of response.collections) { await client.deleteCollection(collection.name); } return { reset: true }; }, curateSources: function (sources = []) { const documents = []; for (const source of sources) { if (Object.keys(source).length > 0) { const metadata = source.hasOwnProperty("metadata") ? source.metadata : source; documents.push({ ...metadata, }); } } return documents; }, }; module.exports.QDrant = QDrant;