mirror of
https://github.com/Mintplex-Labs/anything-llm.git
synced 2024-11-09 00:10:10 +01:00
398 lines
13 KiB
JavaScript
398 lines
13 KiB
JavaScript
const { QdrantClient } = require("@qdrant/js-client-rest");
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const { RecursiveCharacterTextSplitter } = require("langchain/text_splitter");
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const { storeVectorResult, cachedVectorInformation } = require("../../files");
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const { v4: uuidv4 } = require("uuid");
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const { toChunks, getLLMProvider } = require("../../helpers");
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const { chatPrompt } = require("../../chats");
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const QDrant = {
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name: "QDrant",
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connect: async function () {
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if (process.env.VECTOR_DB !== "qdrant")
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throw new Error("QDrant::Invalid ENV settings");
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const client = new QdrantClient({
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url: process.env.QDRANT_ENDPOINT,
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...(process.env.QDRANT_API_KEY
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? { apiKey: process.env.QDRANT_API_KEY }
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: {}),
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});
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const isAlive = (await client.api("cluster")?.clusterStatus())?.ok || false;
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if (!isAlive)
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throw new Error(
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"QDrant::Invalid Heartbeat received - is the instance online?"
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);
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return { client };
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},
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heartbeat: async function () {
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await this.connect();
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return { heartbeat: Number(new Date()) };
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},
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totalIndicies: async function () {
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const { client } = await this.connect();
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const { collections } = await client.getCollections();
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var totalVectors = 0;
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for (const collection of collections) {
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if (!collection || !collection.name) continue;
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totalVectors +=
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(await this.namespace(client, collection.name))?.vectorCount || 0;
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}
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return totalVectors;
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},
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namespaceCount: async function (_namespace = null) {
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const { client } = await this.connect();
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const namespace = await this.namespace(client, _namespace);
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return namespace?.vectorCount || 0;
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},
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similarityResponse: async function (_client, namespace, queryVector) {
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const { client } = await this.connect();
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const result = {
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contextTexts: [],
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sourceDocuments: [],
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};
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const responses = await client.search(namespace, {
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vector: queryVector,
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limit: 4,
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});
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responses.forEach((response) => {
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result.contextTexts.push(response?.payload?.text || "");
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result.sourceDocuments.push({
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...(response?.payload || {}),
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id: response.id,
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});
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});
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return result;
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},
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namespace: async function (client, namespace = null) {
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if (!namespace) throw new Error("No namespace value provided.");
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const collection = await client.getCollection(namespace).catch(() => null);
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if (!collection) return null;
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return {
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name: namespace,
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...collection,
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vectorCount: collection.vectors_count,
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};
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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 { client } = await this.connect();
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return await this.namespaceExists(client, namespace);
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},
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namespaceExists: async function (client, namespace = null) {
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if (!namespace) throw new Error("No namespace value provided.");
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const collection = await client.getCollection(namespace).catch((e) => {
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console.error("QDrant::namespaceExists", e.message);
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return null;
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});
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return !!collection;
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},
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deleteVectorsInNamespace: async function (client, namespace = null) {
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await client.deleteCollection(namespace);
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return true;
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},
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getOrCreateCollection: async function (client, namespace) {
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if (await this.namespaceExists(client, namespace)) {
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return await client.getCollection(namespace);
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}
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await client.createCollection(namespace, {
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vectors: {
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size: 1536, //TODO: Fixed to OpenAI models - when other embeddings exist make variable.
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distance: "Cosine",
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},
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});
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return await client.getCollection(namespace);
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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 { client } = await this.connect();
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const collection = await this.getOrCreateCollection(client, namespace);
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if (!collection)
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throw new Error("Failed to create new QDrant collection!", {
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namespace,
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});
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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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const submission = {
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ids: [],
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vectors: [],
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payloads: [],
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};
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// Before sending to Qdrant 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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chunk.forEach((chunk) => {
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const id = uuidv4();
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const { id: _id, ...payload } = chunk.payload;
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documentVectors.push({ docId, vectorId: id });
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submission.ids.push(id);
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submission.vectors.push(chunk.vector);
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submission.payloads.push(payload);
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});
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const additionResult = await client.upsert(namespace, {
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wait: true,
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batch: { ...submission },
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});
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if (additionResult?.status !== "completed")
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throw new Error("Error embedding into QDrant", additionResult);
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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 `Qdrant.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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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 LLMConnector = getLLMProvider();
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const documentVectors = [];
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const vectors = [];
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const vectorValues = await LLMConnector.embedChunks(textChunks);
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const submission = {
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ids: [],
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vectors: [],
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payloads: [],
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};
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if (!!vectorValues && vectorValues.length > 0) {
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for (const [i, vector] of vectorValues.entries()) {
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const vectorRecord = {
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id: uuidv4(),
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vector: vector,
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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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payload: { ...metadata, text: textChunks[i] },
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};
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submission.ids.push(vectorRecord.id);
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submission.vectors.push(vectorRecord.vector);
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submission.payloads.push(vectorRecord.payload);
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vectors.push(vectorRecord);
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documentVectors.push({ docId, vectorId: vectorRecord.id });
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}
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} else {
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console.error(
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"Could not use OpenAI to embed document chunks! This document will not be recorded."
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);
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}
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const { client } = await this.connect();
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const collection = await this.getOrCreateCollection(client, namespace);
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if (!collection)
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throw new Error("Failed to create new QDrant collection!", {
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namespace,
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});
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if (vectors.length > 0) {
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const chunks = [];
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console.log("Inserting vectorized chunks into QDrant collection.");
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for (const chunk of toChunks(vectors, 500)) chunks.push(chunk);
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const additionResult = await client.upsert(namespace, {
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wait: true,
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batch: {
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ids: submission.ids,
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vectors: submission.vectors,
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payloads: submission.payloads,
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},
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});
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if (additionResult?.status !== "completed")
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throw new Error("Error embedding into QDrant", additionResult);
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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 { client } = await this.connect();
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if (!(await this.namespaceExists(client, 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 client.delete(namespace, {
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wait: true,
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points: vectorIds,
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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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query: async function (reqBody = {}) {
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const { namespace = null, input, workspace = {} } = reqBody;
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if (!namespace || !input) throw new Error("Invalid request body");
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const { client } = await this.connect();
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if (!(await this.namespaceExists(client, 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 LLMConnector = getLLMProvider();
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const queryVector = await LLMConnector.embedTextInput(input);
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const { contextTexts, sourceDocuments } = await this.similarityResponse(
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client,
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namespace,
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queryVector
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);
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const prompt = {
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role: "system",
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content: `${chatPrompt(workspace)}
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Context:
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${contextTexts
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.map((text, i) => {
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return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`;
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})
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.join("")}`,
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};
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const memory = [prompt, { role: "user", content: input }];
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const responseText = await LLMConnector.getChatCompletion(memory, {
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temperature: workspace?.openAiTemp ?? 0.7,
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});
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return {
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response: responseText,
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sources: this.curateSources(sourceDocuments),
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message: false,
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};
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},
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// This implementation of chat uses the chat history and modifies the system prompt at execution
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// this is improved over the regular langchain implementation so that chats do not directly modify embeddings
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// because then multi-user support will have all conversations mutating the base vector collection to which then
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// the only solution is replicating entire vector databases per user - which will very quickly consume space on VectorDbs
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chat: async function (reqBody = {}) {
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const {
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namespace = null,
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input,
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workspace = {},
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chatHistory = [],
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} = reqBody;
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if (!namespace || !input) throw new Error("Invalid request body");
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const { client } = await this.connect();
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if (!(await this.namespaceExists(client, 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 LLMConnector = getLLMProvider();
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const queryVector = await LLMConnector.embedTextInput(input);
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const { contextTexts, sourceDocuments } = await this.similarityResponse(
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client,
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namespace,
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queryVector
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);
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const prompt = {
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role: "system",
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content: `${chatPrompt(workspace)}
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Context:
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${contextTexts
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.map((text, i) => {
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return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`;
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})
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.join("")}`,
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};
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const memory = [prompt, ...chatHistory, { role: "user", content: input }];
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const responseText = await LLMConnector.getChatCompletion(memory, {
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temperature: workspace?.openAiTemp ?? 0.7,
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});
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return {
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response: responseText,
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sources: this.curateSources(sourceDocuments),
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message: false,
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};
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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 { client } = await this.connect();
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if (!(await this.namespaceExists(client, namespace)))
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throw new Error("Namespace by that name does not exist.");
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const stats = await this.namespace(client, 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 for namespace" };
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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 { client } = await this.connect();
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if (!(await this.namespaceExists(client, namespace)))
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throw new Error("Namespace by that name does not exist.");
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const details = await this.namespace(client, namespace);
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await this.deleteVectorsInNamespace(client, 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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reset: async function () {
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const { client } = await this.connect();
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const response = await client.getCollections();
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for (const collection of response.collections) {
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await client.deleteCollection(collection.name);
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}
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return { reset: true };
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},
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curateSources: function (sources = []) {
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const documents = [];
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for (const source of sources) {
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if (Object.keys(source).length > 0) {
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documents.push({
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...source,
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});
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}
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}
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return documents;
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},
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};
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module.exports.QDrant = QDrant;
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