agsamantha/node_modules/@langchain/community/dist/vectorstores/tigris.js

import * as uuid from "uuid";
import { VectorStore } from "@langchain/core/vectorstores";
import { Document } from "@langchain/core/documents";
/**
 * Class for managing and operating vector search applications with
 * Tigris, an open-source Serverless NoSQL Database and Search Platform.
 */
export class TigrisVectorStore extends VectorStore {
    _vectorstoreType() {
        return "tigris";
    }
    constructor(embeddings, args) {
        super(embeddings, args);
        // eslint-disable-next-line @typescript-eslint/no-explicit-any
        Object.defineProperty(this, "index", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        this.embeddings = embeddings;
        this.index = args.index;
    }
    /**
     * Method to add an array of documents to the Tigris database.
     * @param documents An array of Document instances to be added to the Tigris database.
     * @param options Optional parameter that can either be an array of string IDs or an object with a property 'ids' that is an array of string IDs.
     * @returns A Promise that resolves when the documents have been added to the Tigris database.
     */
    async addDocuments(documents, options) {
        const texts = documents.map(({ pageContent }) => pageContent);
        await this.addVectors(await this.embeddings.embedDocuments(texts), documents, options);
    }
    /**
     * Method to add vectors to the Tigris database.
     * @param vectors An array of vectors to be added to the Tigris database.
     * @param documents An array of Document instances corresponding to the vectors.
     * @param options Optional parameter that can either be an array of string IDs or an object with a property 'ids' that is an array of string IDs.
     * @returns A Promise that resolves when the vectors have been added to the Tigris database.
     */
    async addVectors(vectors, documents, options) {
        if (vectors.length === 0) {
            return;
        }
        if (vectors.length !== documents.length) {
            throw new Error(`Vectors and metadatas must have the same length`);
        }
        const ids = Array.isArray(options) ? options : options?.ids;
        const documentIds = ids == null ? documents.map(() => uuid.v4()) : ids;
        await this.index?.addDocumentsWithVectors({
            ids: documentIds,
            embeddings: vectors,
            documents: documents.map(({ metadata, pageContent }) => ({
                content: pageContent,
                metadata,
            })),
        });
    }
    /**
     * Method to perform a similarity search in the Tigris database and return
     * the k most similar vectors along with their similarity scores.
     * @param query The query vector.
     * @param k The number of most similar vectors to return.
     * @param filter Optional filter object to apply during the search.
     * @returns A Promise that resolves to an array of tuples, each containing a Document and its similarity score.
     */
    async similaritySearchVectorWithScore(query, k, filter) {
        const result = await this.index?.similaritySearchVectorWithScore({
            query,
            k,
            filter,
        });
        if (!result) {
            return [];
        }
        // eslint-disable-next-line @typescript-eslint/no-explicit-any
        return result.map(([document, score]) => [
            new Document({
                pageContent: document.content,
                metadata: document.metadata,
            }),
            score,
        ]);
    }
    /**
     * Static method to create a new instance of TigrisVectorStore from an
     * array of texts.
     * @param texts An array of texts to be converted into Document instances and added to the Tigris database.
     * @param metadatas Either an array of metadata objects or a single metadata object to be associated with the texts.
     * @param embeddings An instance of Embeddings to be used for embedding the texts.
     * @param dbConfig An instance of TigrisLibArgs to be used for configuring the Tigris database.
     * @returns A Promise that resolves to a new instance of TigrisVectorStore.
     */
    static async fromTexts(texts, metadatas, embeddings, dbConfig) {
        const docs = [];
        for (let i = 0; i < texts.length; i += 1) {
            const metadata = Array.isArray(metadatas) ? metadatas[i] : metadatas;
            const newDoc = new Document({
                pageContent: texts[i],
                metadata,
            });
            docs.push(newDoc);
        }
        return TigrisVectorStore.fromDocuments(docs, embeddings, dbConfig);
    }
    /**
     * Static method to create a new instance of TigrisVectorStore from an
     * array of Document instances.
     * @param docs An array of Document instances to be added to the Tigris database.
     * @param embeddings An instance of Embeddings to be used for embedding the documents.
     * @param dbConfig An instance of TigrisLibArgs to be used for configuring the Tigris database.
     * @returns A Promise that resolves to a new instance of TigrisVectorStore.
     */
    static async fromDocuments(docs, embeddings, dbConfig) {
        const instance = new this(embeddings, dbConfig);
        await instance.addDocuments(docs);
        return instance;
    }
    /**
     * Static method to create a new instance of TigrisVectorStore from an
     * existing index.
     * @param embeddings An instance of Embeddings to be used for embedding the documents.
     * @param dbConfig An instance of TigrisLibArgs to be used for configuring the Tigris database.
     * @returns A Promise that resolves to a new instance of TigrisVectorStore.
     */
    static async fromExistingIndex(embeddings, dbConfig) {
        const instance = new this(embeddings, dbConfig);
        return instance;
    }
}