535 lines
23 KiB
JavaScript
535 lines
23 KiB
JavaScript
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"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.CouchbaseVectorStore = void 0;
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const vectorstores_1 = require("@langchain/core/vectorstores");
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const couchbase_1 = require("couchbase");
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const documents_1 = require("@langchain/core/documents");
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const uuid_1 = require("uuid");
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/**
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* Class for interacting with the Couchbase database. It extends the
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* VectorStore class and provides methods for adding vectors and
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* documents, and searching for similar vectors.
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* Initiate the class using initialize() method.
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*/
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class CouchbaseVectorStore extends vectorstores_1.VectorStore {
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/**
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* The private constructor used to provide embedding to parent class.
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* Initialize the class using static initialize() method
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* @param embedding - object to generate embedding
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* @param config - the fields required to initialize a vector store
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*/
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constructor(embedding, config) {
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super(embedding, config);
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Object.defineProperty(this, "metadataKey", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: "metadata"
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});
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Object.defineProperty(this, "defaultTextKey", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: "text"
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});
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Object.defineProperty(this, "defaultScopedIndex", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: true
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});
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Object.defineProperty(this, "defaultEmbeddingKey", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: "embedding"
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});
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Object.defineProperty(this, "cluster", {
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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, "_bucket", {
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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, "_scope", {
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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, "_collection", {
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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, "bucketName", {
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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, "scopeName", {
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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, "collectionName", {
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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, "indexName", {
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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, "textKey", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: this.defaultTextKey
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});
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Object.defineProperty(this, "embeddingKey", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: this.defaultEmbeddingKey
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});
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Object.defineProperty(this, "scopedIndex", {
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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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/**
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* Formats couchbase metadata by removing `metadata.` from initials
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* @param fields - all the fields of row
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* @returns - formatted metadata fields
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*/
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Object.defineProperty(this, "formatMetadata", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: (fields) => {
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delete fields[this.textKey];
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const metadataFields = {};
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// eslint-disable-next-line guard-for-in
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for (const key in fields) {
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const newKey = key.replace(`${this.metadataKey}.`, "");
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metadataFields[newKey] = fields[key];
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}
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return metadataFields;
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}
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});
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}
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/**
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* initialize class for interacting with the Couchbase database.
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* It extends the VectorStore class and provides methods
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* for adding vectors and documents, and searching for similar vectors.
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* This also verifies the params
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*
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* @param embeddings - object to generate embedding
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* @param config - the fields required to initialize a vector store
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*/
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static async initialize(embeddings, config) {
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const store = new CouchbaseVectorStore(embeddings, config);
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const { cluster, bucketName, scopeName, collectionName, indexName, textKey, embeddingKey, scopedIndex, } = config;
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store.cluster = cluster;
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store.bucketName = bucketName;
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store.scopeName = scopeName;
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store.collectionName = collectionName;
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store.indexName = indexName;
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if (textKey) {
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store.textKey = textKey;
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}
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else {
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store.textKey = store.defaultTextKey;
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}
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if (embeddingKey) {
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store.embeddingKey = embeddingKey;
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}
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else {
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store.embeddingKey = store.defaultEmbeddingKey;
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}
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if (scopedIndex !== undefined) {
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store.scopedIndex = scopedIndex;
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}
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else {
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store.scopedIndex = store.defaultScopedIndex;
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}
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try {
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store._bucket = store.cluster.bucket(store.bucketName);
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store._scope = store._bucket.scope(store.scopeName);
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store._collection = store._scope.collection(store.collectionName);
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}
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catch (err) {
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throw new Error("Error connecting to couchbase, Please check connection and credentials");
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}
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try {
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if (!(await store.checkBucketExists()) ||
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!(await store.checkIndexExists()) ||
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!(await store.checkScopeAndCollectionExists())) {
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throw new Error("Error while initializing vector store");
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}
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}
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catch (err) {
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throw new Error(`Error while initializing vector store: ${err}`);
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}
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return store;
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}
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/**
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* An asynchrononous method to verify the search indexes.
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* It retrieves all indexes and checks if specified index is present.
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*
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* @throws - If the specified index does not exist in the database.
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*
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* @returns - returns promise true if no error is found
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*/
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async checkIndexExists() {
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if (this.scopedIndex) {
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const allIndexes = await this._scope.searchIndexes().getAllIndexes();
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const indexNames = allIndexes.map((index) => index.name);
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if (!indexNames.includes(this.indexName)) {
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throw new Error(`Index ${this.indexName} does not exist. Please create the index before searching.`);
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}
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}
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else {
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const allIndexes = await this.cluster.searchIndexes().getAllIndexes();
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const indexNames = allIndexes.map((index) => index.name);
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if (!indexNames.includes(this.indexName)) {
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throw new Error(`Index ${this.indexName} does not exist. Please create the index before searching.`);
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}
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}
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return true;
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}
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/**
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* An asynchronous method to verify the existence of a bucket.
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* It retrieves the bucket using the bucket manager and checks if the specified bucket is present.
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*
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* @throws - If the specified bucket does not exist in the database.
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*
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* @returns - Returns a promise that resolves to true if no error is found, indicating the bucket exists.
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*/
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async checkBucketExists() {
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const bucketManager = this.cluster.buckets();
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try {
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await bucketManager.getBucket(this.bucketName);
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return true;
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}
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catch (error) {
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throw new Error(`Bucket ${this.bucketName} does not exist. Please create the bucket before searching.`);
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}
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}
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/**
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* An asynchronous method to verify the existence of a scope and a collection within that scope.
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* It retrieves all scopes and collections in the bucket, and checks if the specified scope and collection are present.
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*
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* @throws - If the specified scope does not exist in the bucket, or if the specified collection does not exist in the scope.
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*
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* @returns - Returns a promise that resolves to true if no error is found, indicating the scope and collection exist.
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*/
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async checkScopeAndCollectionExists() {
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const scopeCollectionMap = {};
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// Get a list of all scopes in the bucket
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const scopes = await this._bucket.collections().getAllScopes();
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for (const scope of scopes) {
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scopeCollectionMap[scope.name] = [];
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// Get a list of all the collections in the scope
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for (const collection of scope.collections) {
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scopeCollectionMap[scope.name].push(collection.name);
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}
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}
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// Check if the scope exists
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if (!Object.keys(scopeCollectionMap).includes(this.scopeName)) {
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throw new Error(`Scope ${this.scopeName} not found in Couchbase bucket ${this.bucketName}`);
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}
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// Check if the collection exists in the scope
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if (!scopeCollectionMap[this.scopeName].includes(this.collectionName)) {
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throw new Error(`Collection ${this.collectionName} not found in scope ${this.scopeName} in Couchbase bucket ${this.bucketName}`);
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}
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return true;
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}
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_vectorstoreType() {
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return "couchbase";
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}
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/**
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* Performs a similarity search on the vectors in the Couchbase database and returns the documents and their corresponding scores.
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*
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* @param queryEmbeddings - Embedding vector to look up documents similar to.
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* @param k - Number of documents to return. Defaults to 4.
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* @param filter - Optional search filter that are passed to Couchbase search. Defaults to empty object.
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* - `fields`: Optional list of fields to include in the
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* metadata of results. Note that these need to be stored in the index.
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* If nothing is specified, defaults to all the fields stored in the index.
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* - `searchOptions`: Optional search options that are passed to Couchbase search. Defaults to empty object.
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*
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* @returns - Promise of list of [document, score] that are the most similar to the query vector.
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*
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* @throws If the search operation fails.
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*/
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async similaritySearchVectorWithScore(queryEmbeddings, k = 4, filter = {}) {
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let { fields } = filter;
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const { searchOptions } = filter;
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if (!fields) {
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fields = ["*"];
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}
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if (!(fields.length === 1 && fields[0] === "*") &&
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!fields.includes(this.textKey)) {
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fields.push(this.textKey);
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}
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const searchRequest = new couchbase_1.SearchRequest(couchbase_1.VectorSearch.fromVectorQuery(new couchbase_1.VectorQuery(this.embeddingKey, queryEmbeddings).numCandidates(k)));
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let searchIterator;
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const docsWithScore = [];
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try {
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if (this.scopedIndex) {
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searchIterator = this._scope.search(this.indexName, searchRequest, {
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limit: k,
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fields,
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raw: searchOptions,
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});
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}
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else {
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searchIterator = this.cluster.search(this.indexName, searchRequest, {
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limit: k,
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fields,
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raw: searchOptions,
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});
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}
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const searchRows = (await searchIterator).rows;
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for (const row of searchRows) {
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const text = row.fields[this.textKey];
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const metadataFields = this.formatMetadata(row.fields);
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const searchScore = row.score;
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const doc = new documents_1.Document({
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pageContent: text,
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metadata: metadataFields,
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});
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docsWithScore.push([doc, searchScore]);
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}
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}
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catch (err) {
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console.log("error received");
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throw new Error(`Search failed with error: ${err}`);
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}
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return docsWithScore;
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}
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/**
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* Return documents that are most similar to the vector embedding.
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*
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* @param queryEmbeddings - Embedding to look up documents similar to.
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* @param k - The number of similar documents to return. Defaults to 4.
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* @param filter - Optional search filter that are passed to Couchbase search. Defaults to empty object.
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* - `fields`: Optional list of fields to include in the
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* metadata of results. Note that these need to be stored in the index.
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* If nothing is specified, defaults to all the fields stored in the index.
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* - `searchOptions`: Optional search options that are passed to Couchbase search. Defaults to empty object.
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*
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* @returns - A promise that resolves to an array of documents that match the similarity search.
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*/
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async similaritySearchByVector(queryEmbeddings, k = 4, filter = {}) {
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const docsWithScore = await this.similaritySearchVectorWithScore(queryEmbeddings, k, filter);
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const docs = [];
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for (const doc of docsWithScore) {
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docs.push(doc[0]);
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}
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return docs;
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}
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/**
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* Return documents that are most similar to the query.
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*
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* @param query - Query to look up for similar documents
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* @param k - The number of similar documents to return. Defaults to 4.
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* @param filter - Optional search filter that are passed to Couchbase search. Defaults to empty object.
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* - `fields`: Optional list of fields to include in the
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* metadata of results. Note that these need to be stored in the index.
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* If nothing is specified, defaults to all the fields stored in the index.
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* - `searchOptions`: Optional search options that are passed to Couchbase search. Defaults to empty object.
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*
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* @returns - Promise of list of documents that are most similar to the query.
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*/
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async similaritySearch(query, k = 4, filter = {}) {
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const queryEmbeddings = await this.embeddings.embedQuery(query);
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const docsWithScore = await this.similaritySearchVectorWithScore(queryEmbeddings, k, filter);
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const docs = [];
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for (const doc of docsWithScore) {
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docs.push(doc[0]);
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}
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return docs;
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}
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/**
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* Return documents that are most similar to the query with their scores.
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*
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* @param query - Query to look up for similar documents
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* @param k - The number of similar documents to return. Defaults to 4.
|
||
|
|
* @param filter - Optional search filter that are passed to Couchbase search. Defaults to empty object.
|
||
|
|
* - `fields`: Optional list of fields to include in the
|
||
|
|
* metadata of results. Note that these need to be stored in the index.
|
||
|
|
* If nothing is specified, defaults to all the fields stored in the index.
|
||
|
|
* - `searchOptions`: Optional search options that are passed to Couchbase search. Defaults to empty object.
|
||
|
|
*
|
||
|
|
* @returns - Promise of list of documents that are most similar to the query.
|
||
|
|
*/
|
||
|
|
async similaritySearchWithScore(query, k = 4, filter = {}) {
|
||
|
|
const queryEmbeddings = await this.embeddings.embedQuery(query);
|
||
|
|
const docsWithScore = await this.similaritySearchVectorWithScore(queryEmbeddings, k, filter);
|
||
|
|
return docsWithScore;
|
||
|
|
}
|
||
|
|
/**
|
||
|
|
* upsert documents asynchronously into a couchbase collection
|
||
|
|
* @param documentsToInsert Documents to be inserted into couchbase collection with embeddings, original text and metadata
|
||
|
|
* @returns DocIds of the inserted documents
|
||
|
|
*/
|
||
|
|
async upsertDocuments(documentsToInsert) {
|
||
|
|
// Create promises for each document to be upserted
|
||
|
|
const upsertDocumentsPromises = documentsToInsert.map((document) => {
|
||
|
|
const currentDocumentKey = Object.keys(document)[0];
|
||
|
|
return this._collection
|
||
|
|
.upsert(currentDocumentKey, document[currentDocumentKey])
|
||
|
|
.then(() => currentDocumentKey)
|
||
|
|
.catch((e) => {
|
||
|
|
console.error("error received while upserting document", e);
|
||
|
|
throw new Error(`Upsert failed with error: ${e}`);
|
||
|
|
});
|
||
|
|
});
|
||
|
|
try {
|
||
|
|
// Upsert all documents asynchronously
|
||
|
|
const docIds = await Promise.all(upsertDocumentsPromises);
|
||
|
|
const successfulDocIds = [];
|
||
|
|
for (const id of docIds) {
|
||
|
|
if (id) {
|
||
|
|
successfulDocIds.push(id);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
return successfulDocIds;
|
||
|
|
}
|
||
|
|
catch (e) {
|
||
|
|
console.error("An error occurred with Promise.all at upserting all documents", e);
|
||
|
|
throw e;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
/**
|
||
|
|
* Add vectors and corresponding documents to a couchbase collection
|
||
|
|
* If the document IDs are passed, the existing documents (if any) will be
|
||
|
|
* overwritten with the new ones.
|
||
|
|
* @param vectors - The vectors to be added to the collection.
|
||
|
|
* @param documents - The corresponding documents to be added to the collection.
|
||
|
|
* @param options - Optional parameters for adding vectors.
|
||
|
|
* This may include the IDs and metadata of the documents to be added. Defaults to an empty object.
|
||
|
|
*
|
||
|
|
* @returns - A promise that resolves to an array of document IDs that were added to the collection.
|
||
|
|
*/
|
||
|
|
async addVectors(vectors, documents, options = {}) {
|
||
|
|
// Get document ids. if ids are not available then use UUIDs for each document
|
||
|
|
let ids = options ? options.ids : undefined;
|
||
|
|
if (ids === undefined) {
|
||
|
|
ids = Array.from({ length: documents.length }, () => (0, uuid_1.v4)());
|
||
|
|
}
|
||
|
|
// Get metadata for each document. if metadata is not available, use empty object for each document
|
||
|
|
let metadata = options ? options.metadata : undefined;
|
||
|
|
if (metadata === undefined) {
|
||
|
|
metadata = Array.from({ length: documents.length }, () => ({}));
|
||
|
|
}
|
||
|
|
const documentsToInsert = ids.map((id, index) => ({
|
||
|
|
[id]: {
|
||
|
|
[this.textKey]: documents[index].pageContent,
|
||
|
|
[this.embeddingKey]: vectors[index],
|
||
|
|
[this.metadataKey]: metadata[index],
|
||
|
|
},
|
||
|
|
}));
|
||
|
|
let docIds = [];
|
||
|
|
try {
|
||
|
|
docIds = await this.upsertDocuments(documentsToInsert);
|
||
|
|
}
|
||
|
|
catch (err) {
|
||
|
|
console.error("Error while adding vectors", err);
|
||
|
|
throw err;
|
||
|
|
}
|
||
|
|
return docIds;
|
||
|
|
}
|
||
|
|
/**
|
||
|
|
* Run texts through the embeddings and persist in vectorstore.
|
||
|
|
* If the document IDs are passed, the existing documents (if any) will be
|
||
|
|
* overwritten with the new ones.
|
||
|
|
* @param documents - The corresponding documents to be added to the collection.
|
||
|
|
* @param options - Optional parameters for adding documents.
|
||
|
|
* This may include the IDs and metadata of the documents to be added. Defaults to an empty object.
|
||
|
|
*
|
||
|
|
* @returns - A promise that resolves to an array of document IDs that were added to the collection.
|
||
|
|
*/
|
||
|
|
async addDocuments(documents, options = {}) {
|
||
|
|
const texts = documents.map(({ pageContent }) => pageContent);
|
||
|
|
const metadatas = documents.map((doc) => doc.metadata);
|
||
|
|
if (!options.metadata) {
|
||
|
|
options.metadata = metadatas;
|
||
|
|
}
|
||
|
|
return this.addVectors(await this.embeddings.embedDocuments(texts), documents, options);
|
||
|
|
}
|
||
|
|
/**
|
||
|
|
* Create a new CouchbaseVectorStore from a set of documents.
|
||
|
|
* This function will initialize a new store, add the documents to it, and then return the store.
|
||
|
|
* @param documents - The documents to be added to the new store.
|
||
|
|
* @param embeddings - The embeddings to be used for the documents.
|
||
|
|
* @param config - The configuration for the new CouchbaseVectorStore. This includes the options for adding vectors.
|
||
|
|
*
|
||
|
|
* @returns - A promise that resolves to the new CouchbaseVectorStore that contains the added documents.
|
||
|
|
*/
|
||
|
|
static async fromDocuments(documents, embeddings, config) {
|
||
|
|
const store = await this.initialize(embeddings, config);
|
||
|
|
await store.addDocuments(documents, config.addVectorOptions);
|
||
|
|
return store;
|
||
|
|
}
|
||
|
|
/**
|
||
|
|
* Create a new CouchbaseVectorStore from a set of texts.
|
||
|
|
* This function will convert each text and its corresponding metadata into a Document,
|
||
|
|
* initialize a new store, add the documents to it, and then return the store.
|
||
|
|
* @param texts - The texts to be converted into Documents and added to the new store.
|
||
|
|
* @param metadatas - The metadata for each text. If an array is passed, each text will have its corresponding metadata.
|
||
|
|
* If not, all texts will have the same metadata.
|
||
|
|
* @param embeddings - The embeddings to be used for the documents.
|
||
|
|
* @param config - The configuration for the new CouchbaseVectorStore. This includes the options for adding vectors.
|
||
|
|
*
|
||
|
|
* @returns - A promise that resolves to the new CouchbaseVectorStore that contains the added documents.
|
||
|
|
*/
|
||
|
|
static async fromTexts(texts, metadatas, embeddings, config) {
|
||
|
|
const docs = [];
|
||
|
|
for (let i = 0; i < texts.length; i += 1) {
|
||
|
|
const metadata = Array.isArray(metadatas) ? metadatas[i] : metadatas;
|
||
|
|
const newDoc = new documents_1.Document({
|
||
|
|
pageContent: texts[i],
|
||
|
|
metadata,
|
||
|
|
});
|
||
|
|
docs.push(newDoc);
|
||
|
|
}
|
||
|
|
return await this.fromDocuments(docs, embeddings, config);
|
||
|
|
}
|
||
|
|
/**
|
||
|
|
* Delete documents asynchronously from the collection.
|
||
|
|
* This function will attempt to remove each document in the provided list of IDs from the collection.
|
||
|
|
* If an error occurs during the deletion of a document, an error will be thrown with the ID of the document and the error message.
|
||
|
|
* @param ids - An array of document IDs to be deleted from the collection.
|
||
|
|
*
|
||
|
|
* @returns - A promise that resolves when all documents have been attempted to be deleted. If a document could not be deleted, an error is thrown.
|
||
|
|
*/
|
||
|
|
async delete(ids) {
|
||
|
|
const deleteDocumentsPromises = ids.map((id) => this._collection.remove(id).catch((err) => {
|
||
|
|
throw new Error(`Error while deleting document - Document Id: ${id}, Error: ${err}`);
|
||
|
|
}));
|
||
|
|
try {
|
||
|
|
await Promise.all(deleteDocumentsPromises);
|
||
|
|
}
|
||
|
|
catch (err) {
|
||
|
|
throw new Error(`Error while deleting documents, Error: ${err}`);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
exports.CouchbaseVectorStore = CouchbaseVectorStore;
|