const { ChromaClient } = require("chromadb"); const { RecursiveCharacterTextSplitter } = require("langchain/text_splitter"); const { storeVectorResult, cachedVectorInformation } = require("../../files"); const { v4: uuidv4 } = require("uuid"); const { toChunks, getLLMProvider, getEmbeddingEngineSelection, } = require("../../helpers"); const Chroma = { name: "Chroma", connect: async function () { if (process.env.VECTOR_DB !== "chroma") throw new Error("Chroma::Invalid ENV settings"); const client = new ChromaClient({ path: process.env.CHROMA_ENDPOINT, // if not set will fallback to localhost:8000 ...(!!process.env.CHROMA_API_HEADER && !!process.env.CHROMA_API_KEY ? { fetchOptions: { headers: parseAuthHeader( process.env.CHROMA_API_HEADER || "X-Api-Key", process.env.CHROMA_API_KEY ), }, } : {}), }); const isAlive = await client.heartbeat(); if (!isAlive) throw new Error( "ChromaDB::Invalid Heartbeat received - is the instance online?" ); return { client }; }, heartbeat: async function () { const { client } = await this.connect(); return { heartbeat: await client.heartbeat() }; }, totalVectors: async function () { const { client } = await this.connect(); const collections = await client.listCollections(); var totalVectors = 0; for (const collectionObj of collections) { const collection = await client .getCollection({ name: collectionObj.name }) .catch(() => null); if (!collection) continue; totalVectors += await collection.count(); } return totalVectors; }, distanceToSimilarity: function (distance = null) { if (distance === null || typeof distance !== "number") return 0.0; if (distance >= 1.0) return 1; if (distance <= 0) return 0; return 1 - distance; }, 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 collection = await client.getCollection({ name: namespace }); const result = { contextTexts: [], sourceDocuments: [], scores: [], }; const response = await collection.query({ queryEmbeddings: queryVector, nResults: 4, }); response.ids[0].forEach((_, i) => { if ( this.distanceToSimilarity(response.distances[0][i]) < similarityThreshold ) return; result.contextTexts.push(response.documents[0][i]); result.sourceDocuments.push(response.metadatas[0][i]); result.scores.push(this.distanceToSimilarity(response.distances[0][i])); }); return result; }, namespace: async function (client, namespace = null) { if (!namespace) throw new Error("No namespace value provided."); const collection = await client .getCollection({ name: namespace }) .catch(() => null); if (!collection) return null; return { ...collection, vectorCount: await collection.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({ name: namespace }) .catch((e) => { console.error("ChromaDB::namespaceExists", e.message); return null; }); return !!collection; }, deleteVectorsInNamespace: async function (client, namespace = null) { await client.deleteCollection({ name: namespace }); return true; }, 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 client.getOrCreateCollection({ name: namespace, metadata: { "hnsw:space": "cosine" }, }); const { chunks } = cacheResult; const documentVectors = []; for (const chunk of chunks) { const submission = { ids: [], embeddings: [], metadatas: [], documents: [], }; // Before sending to Chroma 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, ...metadata } = chunk.metadata; documentVectors.push({ docId, vectorId: id }); submission.ids.push(id); submission.embeddings.push(chunk.values); submission.metadatas.push(metadata); submission.documents.push(metadata.text); }); const additionResult = await collection.add(submission); if (!additionResult) throw new Error("Error embedding into ChromaDB", 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 `Chroma.fromDocuments` // because we then cannot atomically control our namespace to granularly find/remove documents // from vectordb. const textSplitter = new RecursiveCharacterTextSplitter({ chunkSize: getEmbeddingEngineSelection()?.embeddingMaxChunkLength || 1_000, 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: [], embeddings: [], metadatas: [], documents: [], }; if (!!vectorValues && vectorValues.length > 0) { for (const [i, vector] of vectorValues.entries()) { const vectorRecord = { id: uuidv4(), values: 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 metadata: { ...metadata, text: textChunks[i] }, }; submission.ids.push(vectorRecord.id); submission.embeddings.push(vectorRecord.values); submission.metadatas.push(metadata); submission.documents.push(textChunks[i]); 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 client.getOrCreateCollection({ name: namespace, metadata: { "hnsw:space": "cosine" }, }); if (vectors.length > 0) { const chunks = []; console.log("Inserting vectorized chunks into Chroma collection."); for (const chunk of toChunks(vectors, 500)) chunks.push(chunk); const additionResult = await collection.add(submission); if (!additionResult) throw new Error("Error embedding into ChromaDB", 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 collection = await client.getCollection({ name: namespace, }); const knownDocuments = await DocumentVectors.where({ docId }); if (knownDocuments.length === 0) return; const vectorIds = knownDocuments.map((doc) => doc.vectorId); await collection.delete({ ids: 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: { ...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(); await client.reset(); return { reset: true }; }, curateSources: function (sources = []) { const documents = []; for (const source of sources) { const { metadata = {} } = source; if (Object.keys(metadata).length > 0) { documents.push({ ...metadata, ...(source.hasOwnProperty("pageContent") ? { text: source.pageContent } : {}), }); } } return documents; }, }; module.exports.Chroma = Chroma;