286 lines
11 KiB
JavaScript
286 lines
11 KiB
JavaScript
"use strict";
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var __createBinding = (this && this.__createBinding) || (Object.create ? (function(o, m, k, k2) {
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if (k2 === undefined) k2 = k;
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var desc = Object.getOwnPropertyDescriptor(m, k);
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if (!desc || ("get" in desc ? !m.__esModule : desc.writable || desc.configurable)) {
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desc = { enumerable: true, get: function() { return m[k]; } };
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}
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Object.defineProperty(o, k2, desc);
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}) : (function(o, m, k, k2) {
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if (k2 === undefined) k2 = k;
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o[k2] = m[k];
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}));
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var __setModuleDefault = (this && this.__setModuleDefault) || (Object.create ? (function(o, v) {
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Object.defineProperty(o, "default", { enumerable: true, value: v });
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}) : function(o, v) {
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o["default"] = v;
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});
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var __importStar = (this && this.__importStar) || function (mod) {
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if (mod && mod.__esModule) return mod;
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var result = {};
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if (mod != null) for (var k in mod) if (k !== "default" && Object.prototype.hasOwnProperty.call(mod, k)) __createBinding(result, mod, k);
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__setModuleDefault(result, mod);
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return result;
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};
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.ClickHouseStore = void 0;
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const uuid = __importStar(require("uuid"));
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const client_1 = require("@clickhouse/client");
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const mysql2_1 = require("mysql2");
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const vectorstores_1 = require("@langchain/core/vectorstores");
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const documents_1 = require("@langchain/core/documents");
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/**
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* Class for interacting with the ClickHouse database. It extends the
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* VectorStore class and provides methods for adding vectors and
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* documents, searching for similar vectors, and creating instances from
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* texts or documents.
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*/
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class ClickHouseStore extends vectorstores_1.VectorStore {
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_vectorstoreType() {
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return "clickhouse";
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}
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constructor(embeddings, args) {
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super(embeddings, args);
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Object.defineProperty(this, "client", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "indexType", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "indexParam", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "indexQueryParams", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "columnMap", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "database", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "table", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "isInitialized", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: false
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});
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this.indexType = args.indexType || "annoy";
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this.indexParam = args.indexParam || { L2Distance: 100 };
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this.indexQueryParams = args.indexQueryParams || {};
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this.columnMap = args.columnMap || {
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id: "id",
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document: "document",
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embedding: "embedding",
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metadata: "metadata",
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uuid: "uuid",
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};
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this.database = args.database || "default";
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this.table = args.table || "vector_table";
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this.client = (0, client_1.createClient)({
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host: `${args.protocol ?? "https://"}${args.host}:${args.port}`,
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username: args.username,
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password: args.password,
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session_id: uuid.v4(),
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});
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}
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/**
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* Method to add vectors to the ClickHouse database.
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* @param vectors The vectors to add.
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* @param documents The documents associated with the vectors.
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* @returns Promise that resolves when the vectors have been added.
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*/
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async addVectors(vectors, documents) {
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if (vectors.length === 0) {
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return;
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}
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if (!this.isInitialized) {
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await this.initialize(vectors[0].length);
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}
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const queryStr = this.buildInsertQuery(vectors, documents);
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await this.client.exec({ query: queryStr });
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}
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/**
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* Method to add documents to the ClickHouse database.
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* @param documents The documents to add.
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* @returns Promise that resolves when the documents have been added.
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*/
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async addDocuments(documents) {
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return this.addVectors(await this.embeddings.embedDocuments(documents.map((d) => d.pageContent)), documents);
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}
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/**
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* Method to search for vectors that are similar to a given query vector.
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* @param query The query vector.
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* @param k The number of similar vectors to return.
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* @param filter Optional filter for the search results.
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* @returns Promise that resolves with an array of tuples, each containing a Document and a score.
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*/
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async similaritySearchVectorWithScore(query, k, filter) {
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if (!this.isInitialized) {
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await this.initialize(query.length);
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}
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const queryStr = this.buildSearchQuery(query, k, filter);
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const queryResultSet = await this.client.query({ query: queryStr });
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const queryResult = await queryResultSet.json();
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const result = queryResult.data.map((item) => [
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new documents_1.Document({ pageContent: item.document, metadata: item.metadata }),
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item.dist,
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]);
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return result;
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}
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/**
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* Static method to create an instance of ClickHouseStore from texts.
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* @param texts The texts to use.
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* @param metadatas The metadata associated with the texts.
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* @param embeddings The embeddings to use.
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* @param args The arguments for the ClickHouseStore.
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* @returns Promise that resolves with a new instance of ClickHouseStore.
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*/
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static async fromTexts(texts, metadatas, embeddings, args) {
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const docs = [];
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for (let i = 0; i < texts.length; i += 1) {
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const metadata = Array.isArray(metadatas) ? metadatas[i] : metadatas;
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const newDoc = new documents_1.Document({
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pageContent: texts[i],
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metadata,
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});
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docs.push(newDoc);
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}
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return ClickHouseStore.fromDocuments(docs, embeddings, args);
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}
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/**
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* Static method to create an instance of ClickHouseStore from documents.
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* @param docs The documents to use.
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* @param embeddings The embeddings to use.
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* @param args The arguments for the ClickHouseStore.
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* @returns Promise that resolves with a new instance of ClickHouseStore.
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*/
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static async fromDocuments(docs, embeddings, args) {
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const instance = new this(embeddings, args);
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await instance.addDocuments(docs);
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return instance;
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}
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/**
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* Static method to create an instance of ClickHouseStore from an existing
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* index.
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* @param embeddings The embeddings to use.
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* @param args The arguments for the ClickHouseStore.
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* @returns Promise that resolves with a new instance of ClickHouseStore.
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*/
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static async fromExistingIndex(embeddings, args) {
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const instance = new this(embeddings, args);
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await instance.initialize();
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return instance;
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}
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/**
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* Method to initialize the ClickHouse database.
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* @param dimension Optional dimension of the vectors.
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* @returns Promise that resolves when the database has been initialized.
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*/
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async initialize(dimension) {
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const dim = dimension ?? (await this.embeddings.embedQuery("test")).length;
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const indexParamStr = this.indexParam
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? Object.entries(this.indexParam)
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.map(([key, value]) => `'${key}', ${value}`)
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.join(", ")
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: "";
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const query = `
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CREATE TABLE IF NOT EXISTS ${this.database}.${this.table}(
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${this.columnMap.id} Nullable(String),
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${this.columnMap.document} Nullable(String),
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${this.columnMap.embedding} Array(Float32),
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${this.columnMap.metadata} JSON,
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${this.columnMap.uuid} UUID DEFAULT generateUUIDv4(),
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CONSTRAINT cons_vec_len CHECK length(${this.columnMap.embedding}) = ${dim},
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INDEX vec_idx ${this.columnMap.embedding} TYPE ${this.indexType}(${indexParamStr}) GRANULARITY 1000
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) ENGINE = MergeTree ORDER BY ${this.columnMap.uuid} SETTINGS index_granularity = 8192;`;
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await this.client.exec({
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query,
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clickhouse_settings: {
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allow_experimental_object_type: 1,
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allow_experimental_annoy_index: 1,
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},
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});
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this.isInitialized = true;
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}
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/**
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* Method to build an SQL query for inserting vectors and documents into
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* the ClickHouse database.
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* @param vectors The vectors to insert.
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* @param documents The documents to insert.
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* @returns The SQL query string.
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*/
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buildInsertQuery(vectors, documents) {
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const columnsStr = Object.values(Object.fromEntries(Object.entries(this.columnMap).filter(([key]) => key !== this.columnMap.uuid))).join(", ");
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const placeholders = vectors.map(() => "(?, ?, ?, ?)").join(", ");
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const values = [];
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for (let i = 0; i < vectors.length; i += 1) {
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const vector = vectors[i];
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const document = documents[i];
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values.push(uuid.v4(), this.escapeString(document.pageContent), JSON.stringify(vector), JSON.stringify(document.metadata));
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}
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const insertQueryStr = `
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INSERT INTO TABLE ${this.database}.${this.table}(${columnsStr})
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VALUES ${placeholders}
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`;
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const insertQuery = (0, mysql2_1.format)(insertQueryStr, values);
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return insertQuery;
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}
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escapeString(str) {
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return str.replace(/\\/g, "\\\\").replace(/'/g, "\\'");
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}
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/**
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* Method to build an SQL query for searching for similar vectors in the
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* ClickHouse database.
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* @param query The query vector.
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* @param k The number of similar vectors to return.
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* @param filter Optional filter for the search results.
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* @returns The SQL query string.
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*/
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buildSearchQuery(query, k, filter) {
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const order = "ASC";
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const whereStr = filter ? `PREWHERE ${filter.whereStr}` : "";
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const placeholders = query.map(() => "?").join(", ");
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const settingStrings = [];
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if (this.indexQueryParams) {
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for (const [key, value] of Object.entries(this.indexQueryParams)) {
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settingStrings.push(`SETTING ${key}=${value}`);
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}
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}
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const searchQueryStr = `
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SELECT ${this.columnMap.document} AS document, ${this.columnMap.metadata} AS metadata, dist
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FROM ${this.database}.${this.table}
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${whereStr}
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ORDER BY L2Distance(${this.columnMap.embedding}, [${placeholders}]) AS dist ${order}
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LIMIT ${k} ${settingStrings.join(" ")}
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`;
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// Format the query with actual values
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const searchQuery = (0, mysql2_1.format)(searchQueryStr, query);
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return searchQuery;
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}
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}
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exports.ClickHouseStore = ClickHouseStore;
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