mirror of
https://github.com/Mintplex-Labs/anything-llm.git
synced 2024-09-17 18:20:11 +02:00
304 lines
11 KiB
JavaScript
304 lines
11 KiB
JavaScript
const { PineconeClient } = require("@pinecone-database/pinecone");
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const { PineconeStore } = require("langchain/vectorstores/pinecone");
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const { OpenAI } = require("langchain/llms/openai");
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const { ChatOpenAI } = require("langchain/chat_models/openai");
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const { VectorDBQAChain, LLMChain } = require("langchain/chains");
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const { OpenAIEmbeddings } = require("langchain/embeddings/openai");
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const { VectorStoreRetrieverMemory } = require("langchain/memory");
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const { PromptTemplate } = require("langchain/prompts");
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const { RecursiveCharacterTextSplitter } = require("langchain/text_splitter");
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const { storeVectorResult, cachedVectorInformation } = require("../files");
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const { Configuration, OpenAIApi } = require("openai");
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const { v4: uuidv4 } = require("uuid");
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const { toChunks, curateSources } = require("../helpers");
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const Pinecone = {
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name: "Pinecone",
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connect: async function () {
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if (process.env.VECTOR_DB !== "pinecone")
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throw new Error("Pinecone::Invalid ENV settings");
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const client = new PineconeClient();
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await client.init({
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apiKey: process.env.PINECONE_API_KEY,
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environment: process.env.PINECONE_ENVIRONMENT,
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});
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const pineconeIndex = client.Index(process.env.PINECONE_INDEX);
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const { status } = await client.describeIndex({
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indexName: process.env.PINECONE_INDEX,
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});
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if (!status.ready) throw new Error("Pinecode::Index not ready.");
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return { client, pineconeIndex, indexName: process.env.PINECONE_INDEX };
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},
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embedder: function () {
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return new OpenAIEmbeddings({ openAIApiKey: process.env.OPEN_AI_KEY });
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},
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openai: function () {
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const config = new Configuration({ apiKey: process.env.OPEN_AI_KEY });
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const openai = new OpenAIApi(config);
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return openai;
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},
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embedChunk: async function (openai, textChunk) {
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const {
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data: { data },
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} = await openai.createEmbedding({
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model: "text-embedding-ada-002",
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input: textChunk,
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});
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return data.length > 0 && data[0].hasOwnProperty("embedding")
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? data[0].embedding
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: null;
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},
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llm: function () {
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const model = process.env.OPEN_MODEL_PREF || "gpt-3.5-turbo";
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return new OpenAI({
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openAIApiKey: process.env.OPEN_AI_KEY,
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temperature: 0.7,
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modelName: model,
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});
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},
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chatLLM: function () {
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const model = process.env.OPEN_MODEL_PREF || "gpt-3.5-turbo";
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return new ChatOpenAI({
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openAIApiKey: process.env.OPEN_AI_KEY,
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temperature: 0.7,
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modelName: model,
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});
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},
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totalIndicies: async function () {
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const { pineconeIndex } = await this.connect();
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const { namespaces } = await pineconeIndex.describeIndexStats1();
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return Object.values(namespaces).reduce(
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(a, b) => a + (b?.vectorCount || 0),
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0
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);
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},
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namespace: async function (index, namespace = null) {
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if (!namespace) throw new Error("No namespace value provided.");
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const { namespaces } = await index.describeIndexStats1();
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return namespaces.hasOwnProperty(namespace) ? namespaces[namespace] : null;
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},
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hasNamespace: async function (namespace = null) {
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if (!namespace) return false;
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const { pineconeIndex } = await this.connect();
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return await this.namespaceExists(pineconeIndex, namespace);
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},
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namespaceExists: async function (index, namespace = null) {
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if (!namespace) throw new Error("No namespace value provided.");
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const { namespaces } = await index.describeIndexStats1();
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return namespaces.hasOwnProperty(namespace);
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},
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deleteVectorsInNamespace: async function (index, namespace = null) {
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await index.delete1({ namespace, deleteAll: true });
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return true;
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},
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addDocumentToNamespace: async function (
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namespace,
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documentData = {},
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fullFilePath = null
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) {
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const { DocumentVectors } = require("../../models/vectors");
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try {
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const { pageContent, docId, ...metadata } = documentData;
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if (!pageContent || pageContent.length == 0) return false;
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console.log("Adding new vectorized document into namespace", namespace);
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const cacheResult = await cachedVectorInformation(fullFilePath);
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if (cacheResult.exists) {
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const { pineconeIndex } = await this.connect();
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const { chunks } = cacheResult;
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const documentVectors = [];
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for (const chunk of chunks) {
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// Before sending to Pinecone and saving the records to our db
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// we need to assign the id of each chunk that is stored in the cached file.
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const newChunks = chunk.map((chunk) => {
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const id = uuidv4();
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documentVectors.push({ docId, vectorId: id });
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return { ...chunk, id };
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});
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// Push chunks with new ids to pinecone.
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await pineconeIndex.upsert({
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upsertRequest: {
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vectors: [...newChunks],
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namespace,
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},
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});
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}
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await DocumentVectors.bulkInsert(documentVectors);
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return true;
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}
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// If we are here then we are going to embed and store a novel document.
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// We have to do this manually as opposed to using LangChains `PineconeStore.fromDocuments`
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// because we then cannot atomically control our namespace to granularly find/remove documents
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// from vectordb.
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// https://github.com/hwchase17/langchainjs/blob/2def486af734c0ca87285a48f1a04c057ab74bdf/langchain/src/vectorstores/pinecone.ts#L167
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const textSplitter = new RecursiveCharacterTextSplitter({
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chunkSize: 1000,
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chunkOverlap: 20,
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});
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const textChunks = await textSplitter.splitText(pageContent);
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console.log("Chunks created from document:", textChunks.length);
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const documentVectors = [];
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const vectors = [];
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const openai = this.openai();
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for (const textChunk of textChunks) {
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const vectorValues = await this.embedChunk(openai, textChunk);
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if (!!vectorValues) {
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const vectorRecord = {
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id: uuidv4(),
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values: vectorValues,
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// [DO NOT REMOVE]
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// LangChain will be unable to find your text if you embed manually and dont include the `text` key.
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// https://github.com/hwchase17/langchainjs/blob/2def486af734c0ca87285a48f1a04c057ab74bdf/langchain/src/vectorstores/pinecone.ts#L64
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metadata: { ...metadata, text: textChunk },
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};
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vectors.push(vectorRecord);
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documentVectors.push({ docId, vectorId: vectorRecord.id });
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} else {
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console.error(
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"Could not use OpenAI to embed document chunk! This document will not be recorded."
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);
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}
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}
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if (vectors.length > 0) {
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const chunks = [];
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const { pineconeIndex } = await this.connect();
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console.log("Inserting vectorized chunks into Pinecone.");
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for (const chunk of toChunks(vectors, 100)) {
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chunks.push(chunk);
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await pineconeIndex.upsert({
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upsertRequest: {
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vectors: [...chunk],
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namespace,
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},
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});
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}
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await storeVectorResult(chunks, fullFilePath);
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}
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await DocumentVectors.bulkInsert(documentVectors);
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return true;
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} catch (e) {
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console.error("addDocumentToNamespace", e.message);
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return false;
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}
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},
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deleteDocumentFromNamespace: async function (namespace, docId) {
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const { DocumentVectors } = require("../../models/vectors");
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const { pineconeIndex } = await this.connect();
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if (!(await this.namespaceExists(pineconeIndex, namespace))) return;
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const knownDocuments = await DocumentVectors.where(`docId = '${docId}'`);
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if (knownDocuments.length === 0) return;
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const vectorIds = knownDocuments.map((doc) => doc.vectorId);
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await pineconeIndex.delete1({
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ids: vectorIds,
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namespace,
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});
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const indexes = knownDocuments.map((doc) => doc.id);
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await DocumentVectors.deleteIds(indexes);
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return true;
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},
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"namespace-stats": async function (reqBody = {}) {
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const { namespace = null } = reqBody;
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if (!namespace) throw new Error("namespace required");
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const { pineconeIndex } = await this.connect();
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if (!(await this.namespaceExists(pineconeIndex, namespace)))
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throw new Error("Namespace by that name does not exist.");
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const stats = await this.namespace(pineconeIndex, namespace);
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return stats
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? stats
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: { message: "No stats were able to be fetched from DB" };
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},
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"delete-namespace": async function (reqBody = {}) {
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const { namespace = null } = reqBody;
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const { pineconeIndex } = await this.connect();
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if (!(await this.namespaceExists(pineconeIndex, namespace)))
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throw new Error("Namespace by that name does not exist.");
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const details = await this.namespace(pineconeIndex, namespace);
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await this.deleteVectorsInNamespace(pineconeIndex, namespace);
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return {
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message: `Namespace ${namespace} was deleted along with ${details.vectorCount} vectors.`,
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};
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},
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query: async function (reqBody = {}) {
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const { namespace = null, input } = reqBody;
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if (!namespace || !input) throw new Error("Invalid request body");
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const { pineconeIndex } = await this.connect();
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if (!(await this.namespaceExists(pineconeIndex, namespace))) {
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return {
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response: null,
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sources: [],
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message: "Invalid query - no documents found for workspace!",
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};
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}
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const vectorStore = await PineconeStore.fromExistingIndex(this.embedder(), {
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pineconeIndex,
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namespace,
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});
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const model = this.llm();
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const chain = VectorDBQAChain.fromLLM(model, vectorStore, {
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k: 5,
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returnSourceDocuments: true,
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});
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const response = await chain.call({ query: input });
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return {
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response: response.text,
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sources: curateSources(response.sourceDocuments),
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message: false,
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};
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},
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// This implementation of chat also expands the memory of the chat itself
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// and adds more tokens to the PineconeDB instance namespace
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chat: async function (reqBody = {}) {
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const { namespace = null, input } = reqBody;
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if (!namespace || !input) throw new Error("Invalid request body");
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const { pineconeIndex } = await this.connect();
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if (!(await this.namespaceExists(pineconeIndex, namespace)))
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throw new Error(
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"Invalid namespace - has it been collected and seeded yet?"
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);
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const vectorStore = await PineconeStore.fromExistingIndex(this.embedder(), {
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pineconeIndex,
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namespace,
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});
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const memory = new VectorStoreRetrieverMemory({
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vectorStoreRetriever: vectorStore.asRetriever(1),
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memoryKey: "history",
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});
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const model = this.llm();
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const prompt =
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PromptTemplate.fromTemplate(`The following is a friendly conversation between a human and an AI. The AI is very casual and talkative and responds with a friendly tone. If the AI does not know the answer to a question, it truthfully says it does not know.
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Relevant pieces of previous conversation:
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{history}
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Current conversation:
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Human: {input}
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AI:`);
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const chain = new LLMChain({ llm: model, prompt, memory });
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const response = await chain.call({ input });
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return { response: response.text, sources: [], message: false };
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},
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};
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module.exports.Pinecone = Pinecone;
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