agsamantha/node_modules/@langchain/community/dist/vectorstores/voy.cjs

"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.VoyVectorStore = void 0;
const vectorstores_1 = require("@langchain/core/vectorstores");
const documents_1 = require("@langchain/core/documents");
/**
 * Class that extends `VectorStore`. It allows to perform similarity search using
 * Voi similarity search engine. The class requires passing Voy Client as an input parameter.
 */
class VoyVectorStore extends vectorstores_1.VectorStore {
    _vectorstoreType() {
        return "voi";
    }
    constructor(client, embeddings) {
        super(embeddings, {});
        Object.defineProperty(this, "client", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "numDimensions", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: null
        });
        Object.defineProperty(this, "docstore", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: []
        });
        this.client = client;
        this.embeddings = embeddings;
    }
    /**
     * Adds documents to the Voy database. The documents are embedded using embeddings provided while instantiating the class.
     * @param documents An array of `Document` instances associated with the vectors.
     */
    async addDocuments(documents) {
        const texts = documents.map(({ pageContent }) => pageContent);
        if (documents.length === 0) {
            return;
        }
        const firstVector = (await this.embeddings.embedDocuments(texts.slice(0, 1)))[0];
        if (this.numDimensions === null) {
            this.numDimensions = firstVector.length;
        }
        else if (this.numDimensions !== firstVector.length) {
            throw new Error(`Vectors must have the same length as the number of dimensions (${this.numDimensions})`);
        }
        const restResults = await this.embeddings.embedDocuments(texts.slice(1));
        await this.addVectors([firstVector, ...restResults], documents);
    }
    /**
     * Adds vectors to the Voy database. The vectors are associated with
     * the provided documents.
     * @param vectors An array of vectors to be added to the database.
     * @param documents An array of `Document` instances associated with the vectors.
     */
    async addVectors(vectors, documents) {
        if (vectors.length === 0) {
            return;
        }
        if (this.numDimensions === null) {
            this.numDimensions = vectors[0].length;
        }
        if (vectors.length !== documents.length) {
            throw new Error(`Vectors and metadata must have the same length`);
        }
        if (!vectors.every((v) => v.length === this.numDimensions)) {
            throw new Error(`Vectors must have the same length as the number of dimensions (${this.numDimensions})`);
        }
        vectors.forEach((item, idx) => {
            const doc = documents[idx];
            this.docstore.push({ embeddings: item, document: doc });
        });
        const embeddings = this.docstore.map((item, idx) => ({
            id: String(idx),
            embeddings: item.embeddings,
            title: "",
            url: "",
        }));
        this.client.index({ embeddings });
    }
    /**
     * Searches for vectors in the Voy database that are similar to the
     * provided query vector.
     * @param query The query vector.
     * @param k The number of similar vectors to return.
     * @returns A promise that resolves with an array of tuples, each containing a `Document` instance and a similarity score.
     */
    async similaritySearchVectorWithScore(query, k) {
        if (this.numDimensions === null) {
            throw new Error("There aren't any elements in the index yet.");
        }
        if (query.length !== this.numDimensions) {
            throw new Error(`Query vector must have the same length as the number of dimensions (${this.numDimensions})`);
        }
        const itemsToQuery = Math.min(this.docstore.length, k);
        if (itemsToQuery > this.docstore.length) {
            console.warn(`k (${k}) is greater than the number of elements in the index (${this.docstore.length}), setting k to ${itemsToQuery}`);
        }
        const results = this.client.search(new Float32Array(query), itemsToQuery);
        return results.neighbors.map(({ id }, idx) => [this.docstore[parseInt(id, 10)].document, idx]);
    }
    /**
     * Method to delete data from the Voy index. It can delete data based
     * on specific IDs or a filter.
     * @param params Object that includes either an array of IDs or a filter for the data to be deleted.
     * @returns Promise that resolves when the deletion is complete.
     */
    async delete(params) {
        if (params.deleteAll === true) {
            await this.client.clear();
        }
        else {
            throw new Error(`You must provide a "deleteAll" parameter.`);
        }
    }
    /**
     * Creates a new `VoyVectorStore` instance from an array of text strings. The text
     * strings are converted to `Document` instances and added to the Voy
     * database.
     * @param texts An array of text strings.
     * @param metadatas An array of metadata objects or a single metadata object. If an array is provided, it must have the same length as the `texts` array.
     * @param embeddings An `Embeddings` instance used to generate embeddings for the documents.
     * @param client An instance of Voy client to use in the underlying operations.
     * @returns A promise that resolves with a new `VoyVectorStore` instance.
     */
    static async fromTexts(texts, metadatas, embeddings, client) {
        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 VoyVectorStore.fromDocuments(docs, embeddings, client);
    }
    /**
     * Creates a new `VoyVectorStore` instance from an array of `Document` instances.
     * The documents are added to the Voy database.
     * @param docs An array of `Document` instances.
     * @param embeddings An `Embeddings` instance used to generate embeddings for the documents.
     * @param client An instance of Voy client to use in the underlying operations.
     * @returns A promise that resolves with a new `VoyVectorStore` instance.
     */
    static async fromDocuments(docs, embeddings, client) {
        const instance = new VoyVectorStore(client, embeddings);
        await instance.addDocuments(docs);
        return instance;
    }
}
exports.VoyVectorStore = VoyVectorStore;