const { PineconeClient } = require("@pinecone-database/pinecone"); const { RecursiveCharacterTextSplitter } = require("langchain/text_splitter"); const { storeVectorResult, cachedVectorInformation } = require("../../files"); const { v4: uuidv4 } = require("uuid"); const { toChunks, getLLMProvider } = require("../../helpers"); const { chatPrompt } = require("../../chats"); const Pinecone = { name: "Pinecone", connect: async function () { if (process.env.VECTOR_DB !== "pinecone") throw new Error("Pinecone::Invalid ENV settings"); const client = new PineconeClient(); await client.init({ apiKey: process.env.PINECONE_API_KEY, environment: process.env.PINECONE_ENVIRONMENT, }); const pineconeIndex = client.Index(process.env.PINECONE_INDEX); const { status } = await client.describeIndex({ indexName: process.env.PINECONE_INDEX, }); if (!status.ready) throw new Error("Pinecode::Index not ready."); return { client, pineconeIndex, indexName: process.env.PINECONE_INDEX }; }, totalIndicies: async function () { const { pineconeIndex } = await this.connect(); const { namespaces } = await pineconeIndex.describeIndexStats1(); return Object.values(namespaces).reduce( (a, b) => a + (b?.vectorCount || 0), 0 ); }, namespaceCount: async function (_namespace = null) { const { pineconeIndex } = await this.connect(); const namespace = await this.namespace(pineconeIndex, _namespace); return namespace?.vectorCount || 0; }, similarityResponse: async function (index, namespace, queryVector) { const result = { contextTexts: [], sourceDocuments: [], }; const response = await index.query({ queryRequest: { namespace, vector: queryVector, topK: 4, includeMetadata: true, }, }); response.matches.forEach((match) => { result.contextTexts.push(match.metadata.text); result.sourceDocuments.push(match); }); return result; }, namespace: async function (index, namespace = null) { if (!namespace) throw new Error("No namespace value provided."); const { namespaces } = await index.describeIndexStats1(); return namespaces.hasOwnProperty(namespace) ? namespaces[namespace] : null; }, hasNamespace: async function (namespace = null) { if (!namespace) return false; const { pineconeIndex } = await this.connect(); return await this.namespaceExists(pineconeIndex, namespace); }, namespaceExists: async function (index, namespace = null) { if (!namespace) throw new Error("No namespace value provided."); const { namespaces } = await index.describeIndexStats1(); return namespaces.hasOwnProperty(namespace); }, deleteVectorsInNamespace: async function (index, namespace = null) { await index.delete1({ namespace, deleteAll: true }); 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 { pineconeIndex } = await this.connect(); const { chunks } = cacheResult; const documentVectors = []; for (const chunk of chunks) { // Before sending to Pinecone and saving the records to our db // we need to assign the id of each chunk that is stored in the cached file. const newChunks = chunk.map((chunk) => { const id = uuidv4(); documentVectors.push({ docId, vectorId: id }); return { ...chunk, id }; }); // Push chunks with new ids to pinecone. await pineconeIndex.upsert({ upsertRequest: { vectors: [...newChunks], namespace, }, }); } 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 `PineconeStore.fromDocuments` // because we then cannot atomically control our namespace to granularly find/remove documents // from vectordb. // https://github.com/hwchase17/langchainjs/blob/2def486af734c0ca87285a48f1a04c057ab74bdf/langchain/src/vectorstores/pinecone.ts#L167 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); 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] }, }; vectors.push(vectorRecord); documentVectors.push({ docId, vectorId: vectorRecord.id }); } } else { console.error( "Could not use OpenAI to embed document chunks! This document will not be recorded." ); } if (vectors.length > 0) { const chunks = []; const { pineconeIndex } = await this.connect(); console.log("Inserting vectorized chunks into Pinecone."); for (const chunk of toChunks(vectors, 100)) { chunks.push(chunk); await pineconeIndex.upsert({ upsertRequest: { vectors: [...chunk], namespace, }, }); } await storeVectorResult(chunks, fullFilePath); } await DocumentVectors.bulkInsert(documentVectors); return true; } catch (e) { console.error("addDocumentToNamespace", e.message); return false; } }, deleteDocumentFromNamespace: async function (namespace, docId) { const { DocumentVectors } = require("../../../models/vectors"); const { pineconeIndex } = await this.connect(); if (!(await this.namespaceExists(pineconeIndex, namespace))) return; const knownDocuments = await DocumentVectors.where(`docId = '${docId}'`); if (knownDocuments.length === 0) return; const vectorIds = knownDocuments.map((doc) => doc.vectorId); await pineconeIndex.delete1({ ids: vectorIds, namespace, }); const indexes = knownDocuments.map((doc) => doc.id); await DocumentVectors.deleteIds(indexes); return true; }, "namespace-stats": async function (reqBody = {}) { const { namespace = null } = reqBody; if (!namespace) throw new Error("namespace required"); const { pineconeIndex } = await this.connect(); if (!(await this.namespaceExists(pineconeIndex, namespace))) throw new Error("Namespace by that name does not exist."); const stats = await this.namespace(pineconeIndex, namespace); return stats ? stats : { message: "No stats were able to be fetched from DB" }; }, "delete-namespace": async function (reqBody = {}) { const { namespace = null } = reqBody; const { pineconeIndex } = await this.connect(); if (!(await this.namespaceExists(pineconeIndex, namespace))) throw new Error("Namespace by that name does not exist."); const details = await this.namespace(pineconeIndex, namespace); await this.deleteVectorsInNamespace(pineconeIndex, namespace); return { message: `Namespace ${namespace} was deleted along with ${details.vectorCount} vectors.`, }; }, query: async function (reqBody = {}) { const { namespace = null, input, workspace = {} } = reqBody; if (!namespace || !input) throw new Error("Invalid request body"); const { pineconeIndex } = await this.connect(); if (!(await this.namespaceExists(pineconeIndex, namespace))) { return { response: null, sources: [], message: "Invalid query - no documents found for workspace!", }; } const LLMConnector = getLLMProvider(); const queryVector = await LLMConnector.embedTextInput(input); const { contextTexts, sourceDocuments } = await this.similarityResponse( pineconeIndex, namespace, queryVector ); const prompt = { role: "system", content: `${chatPrompt(workspace)} Context: ${contextTexts .map((text, i) => { return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`; }) .join("")}`, }; const memory = [prompt, { role: "user", content: input }]; const responseText = await LLMConnector.getChatCompletion(memory, { temperature: workspace?.openAiTemp ?? 0.7, }); return { response: responseText, sources: this.curateSources(sourceDocuments), message: false, }; }, // This implementation of chat uses the chat history and modifies the system prompt at execution // this is improved over the regular langchain implementation so that chats do not directly modify embeddings // because then multi-user support will have all conversations mutating the base vector collection to which then // the only solution is replicating entire vector databases per user - which will very quickly consume space on VectorDbs chat: async function (reqBody = {}) { const { namespace = null, input, workspace = {}, chatHistory = [], } = reqBody; if (!namespace || !input) throw new Error("Invalid request body"); const { pineconeIndex } = await this.connect(); if (!(await this.namespaceExists(pineconeIndex, namespace))) throw new Error( "Invalid namespace - has it been collected and seeded yet?" ); const LLMConnector = getLLMProvider(); const queryVector = await LLMConnector.embedTextInput(input); const { contextTexts, sourceDocuments } = await this.similarityResponse( pineconeIndex, namespace, queryVector ); const prompt = { role: "system", content: `${chatPrompt(workspace)} Context: ${contextTexts .map((text, i) => { return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`; }) .join("")}`, }; const memory = [prompt, ...chatHistory, { role: "user", content: input }]; const responseText = await LLMConnector.getChatCompletion(memory, { temperature: workspace?.openAiTemp ?? 0.7, }); return { response: responseText, sources: this.curateSources(sourceDocuments), message: false, }; }, 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.Pinecone = Pinecone;