@langchain/community/dist/vectorstores/closevector/common.cjs

Third-party integrations for LangChain.js

"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.CloseVector = void 0;
const documents_1 = require("@langchain/core/documents");
const vectorstores_1 = require("@langchain/core/vectorstores");
/**
 * package closevector is largely based on hnswlib.ts in the current folder with the following exceptions:
 * 1. It uses a modified version of hnswlib-node to ensure the generated index can be loaded by closevector_web.ts.
 * 2. It adds features to upload and download the index to/from the CDN provided by CloseVector.
 *
 * For more information, check out https://closevector-docs.getmegaportal.com/
 */
/**
 * Class that implements a vector store using Hierarchical Navigable Small
 * World (HNSW) graphs. It extends the SaveableVectorStore class and
 * provides methods for adding documents and vectors, performing
 * similarity searches, and saving and loading the vector store.
 */
class CloseVector extends vectorstores_1.SaveableVectorStore {
    _vectorstoreType() {
        return "closevector";
    }
    constructor(embeddings, args, credentials) {
        super(embeddings, args);
        Object.defineProperty(this, "_instance", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        // credentials will not be saved to disk
        Object.defineProperty(this, "credentials", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        this.credentials = credentials;
    }
    get instance() {
        if (!this._instance) {
            throw new Error("Vector store not initialised yet. Try calling `addTexts` first.");
        }
        return this._instance;
    }
    set instance(instance) {
        this._instance = instance;
    }
    /**
     * Method to add documents to the vector store. It first converts the
     * documents to vectors using the embeddings, then adds the vectors to the
     * vector store.
     * @param documents The documents to be added to the vector store.
     * @returns A Promise that resolves when the documents have been added.
     */
    async addDocuments(documents) {
        await this.instance.addDocuments(documents);
    }
    /**
     * Method to save the vector store to a directory. It saves the HNSW
     * index, the arguments, and the document store to the directory.
     * @param directory The directory to which to save the vector store. In CloseVector, we use IndexedDB to mock the file system. Therefore, this parameter is can be treated as a key to the contents stored.
     * @returns A Promise that resolves when the vector store has been saved.
     */
    async save(directory) {
        await this.instance.save(directory);
    }
    /**
     * Method to add vectors to the vector store. It first initializes the
     * index if it hasn't been initialized yet, then adds the vectors to the
     * index and the documents to the document store.
     * @param vectors The vectors to be added to the vector store.
     * @param documents The documents corresponding to the vectors.
     * @returns A Promise that resolves when the vectors and documents have been added.
     */
    async addVectors(vectors, documents) {
        await this.instance.addVectors(vectors, documents);
    }
    /**
     * Method to perform a similarity search in the vector store using a query
     * vector. It returns the k most similar documents along with their
     * similarity scores. An optional filter function can be provided to
     * filter the documents.
     * @param query The query vector.
     * @param k The number of most similar documents to return.
     * @param filter An optional filter function to filter the documents.
     * @returns A Promise that resolves to an array of tuples, where each tuple contains a document and its similarity score.
     */
    async similaritySearchVectorWithScore(query, k, filter) {
        const resp = await this.instance.similaritySearchVectorWithScore(query, k, filter
            ? (x) => filter?.({
                pageContent: x.pageContent,
                metadata: x.metadata || {},
            }) || false
            : undefined);
        const mapped = resp.map((x) => [
            new documents_1.Document({
                pageContent: x[0].pageContent,
                metadata: x[0].metadata || {},
            }),
            1 - x[1],
        ]);
        return mapped;
    }
    /**
     * Method to delete the vector store from a directory. It deletes the
     * hnswlib.index file, the docstore.json file, and the args.json file from
     * the directory.
     * @param params An object with a directory property that specifies the directory from which to delete the vector store.
     * @returns A Promise that resolves when the vector store has been deleted.
     */
    async delete(params) {
        return await this.instance.delete(params);
    }
    static textsToDocuments(texts, metadatas) {
        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 docs;
    }
}
exports.CloseVector = CloseVector;