@langchain/community/dist/vectorstores/voy.js

Third-party integrations for LangChain.js

import { __export } from "../_virtual/rolldown_runtime.js";
import { Document } from "@langchain/core/documents";
import { VectorStore } from "@langchain/core/vectorstores";

//#region src/vectorstores/voy.ts
var voy_exports = {};
__export(voy_exports, { VoyVectorStore: () => VoyVectorStore });
/**
* 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.
*/
var VoyVectorStore = class VoyVectorStore extends VectorStore {
	client;
	numDimensions = null;
	docstore = [];
	_vectorstoreType() {
		return "voi";
	}
	constructor(client, embeddings) {
		super(embeddings, {});
		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 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;
	}
};

//#endregion
export { VoyVectorStore, voy_exports };
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