@langchain/community
Version:
Third-party integrations for LangChain.js
225 lines (223 loc) • 10.5 kB
JavaScript
import { __export } from "../_virtual/rolldown_runtime.js";
import { ALL_VECTOR_METADATA, CreateVectorIndex, VectorDeleteItemBatch, VectorSearch, VectorSearchAndFetchVectors, VectorUpsertItemBatch } from "@gomomento/sdk-core";
import { Document } from "@langchain/core/documents";
import * as uuid from "uuid";
import { VectorStore } from "@langchain/core/vectorstores";
import { maximalMarginalRelevance } from "@langchain/core/utils/math";
//#region src/vectorstores/momento_vector_index.ts
var momento_vector_index_exports = {};
__export(momento_vector_index_exports, { MomentoVectorIndex: () => MomentoVectorIndex });
/**
* A vector store that uses the Momento Vector Index.
*
* @remarks
* To sign up for a free Momento account, visit https://console.gomomento.com.
*/
var MomentoVectorIndex = class MomentoVectorIndex extends VectorStore {
client;
indexName;
textField;
_ensureIndexExists;
_vectorstoreType() {
return "momento";
}
/**
* Creates a new `MomentoVectorIndex` instance.
* @param embeddings The embeddings instance to use to generate embeddings from documents.
* @param args The arguments to use to configure the vector store.
*/
constructor(embeddings, args) {
super(embeddings, args);
this.embeddings = embeddings;
this.client = args.client;
this.indexName = args.indexName ?? "default";
this.textField = args.textField ?? "text";
this._ensureIndexExists = args.ensureIndexExists ?? true;
}
/**
* Returns the Momento Vector Index client.
* @returns The Momento Vector Index client.
*/
getClient() {
return this.client;
}
/**
* Creates the index if it does not already exist.
* @param numDimensions The number of dimensions of the vectors to be stored in the index.
* @returns Promise that resolves to true if the index was created, false if it already existed.
*/
async ensureIndexExists(numDimensions) {
const response = await this.client.createIndex(this.indexName, numDimensions);
if (response instanceof CreateVectorIndex.Success) return true;
else if (response instanceof CreateVectorIndex.AlreadyExists) return false;
else if (response instanceof CreateVectorIndex.Error) throw new Error(response.toString());
else throw new Error(`Unknown response type: ${response.toString()}`);
}
/**
* Converts the documents to a format that can be stored in the index.
*
* This is necessary because the Momento Vector Index requires that the metadata
* be a map of strings to strings.
* @param vectors The vectors to convert.
* @param documents The documents to convert.
* @param ids The ids to convert.
* @returns The converted documents.
*/
prepareItemBatch(vectors, documents, ids) {
return vectors.map((vector, idx) => ({
id: ids[idx],
vector,
metadata: {
...documents[idx].metadata,
[this.textField]: documents[idx].pageContent
}
}));
}
/**
* Adds vectors to the index.
*
* @remarks If the index does not already exist, it will be created if `ensureIndexExists` is true.
* @param vectors The vectors to add to the index.
* @param documents The documents to add to the index.
* @param documentProps The properties of the documents to add to the index, specifically the ids.
* @returns Promise that resolves when the vectors have been added to the index. Also returns the ids of the
* documents that were added.
*/
async addVectors(vectors, documents, documentProps) {
if (vectors.length === 0) return;
if (documents.length !== vectors.length) throw new Error(`Number of vectors (${vectors.length}) does not equal number of documents (${documents.length})`);
if (vectors.some((v) => v.length !== vectors[0].length)) throw new Error("All vectors must have the same length");
if (documentProps?.ids !== void 0 && documentProps.ids.length !== vectors.length) throw new Error(`Number of ids (${documentProps?.ids?.length || "null"}) does not equal number of vectors (${vectors.length})`);
if (this._ensureIndexExists) await this.ensureIndexExists(vectors[0].length);
const documentIds = documentProps?.ids ?? documents.map(() => uuid.v4());
const batchSize = 128;
const numBatches = Math.ceil(vectors.length / batchSize);
for (let i = 0; i < numBatches; i += 1) {
const [startIndex, endIndex] = [i * batchSize, Math.min((i + 1) * batchSize, vectors.length)];
const batchVectors = vectors.slice(startIndex, endIndex);
const batchDocuments = documents.slice(startIndex, endIndex);
const batchDocumentIds = documentIds.slice(startIndex, endIndex);
const response = await this.client.upsertItemBatch(this.indexName, this.prepareItemBatch(batchVectors, batchDocuments, batchDocumentIds));
if (response instanceof VectorUpsertItemBatch.Success) continue;
else if (response instanceof VectorUpsertItemBatch.Error) throw new Error(response.toString());
else throw new Error(`Unknown response type: ${response.toString()}`);
}
}
/**
* Adds vectors to the index. Generates embeddings from the documents
* using the `Embeddings` instance passed to the constructor.
* @param documents Array of `Document` instances to be added to the index.
* @returns Promise that resolves when the documents have been added to the index.
*/
async addDocuments(documents, documentProps) {
const texts = documents.map(({ pageContent }) => pageContent);
await this.addVectors(await this.embeddings.embedDocuments(texts), documents, documentProps);
}
/**
* Deletes vectors from the index by id.
* @param params The parameters to use to delete the vectors, specifically the ids.
*/
async delete(params) {
const response = await this.client.deleteItemBatch(this.indexName, params.ids);
if (response instanceof VectorDeleteItemBatch.Success) {} else if (response instanceof VectorDeleteItemBatch.Error) throw new Error(response.toString());
else throw new Error(`Unknown response type: ${response.toString()}`);
}
/**
* Searches the index for the most similar vectors to the query vector.
* @param query The query vector.
* @param k The number of results to return.
* @returns Promise that resolves to the documents of the most similar vectors
* to the query vector.
*/
async similaritySearchVectorWithScore(query, k) {
const response = await this.client.search(this.indexName, query, {
topK: k,
metadataFields: ALL_VECTOR_METADATA
});
if (response instanceof VectorSearch.Success) {
if (response.hits === void 0) return [];
return response.hits().map((hit) => [new Document({
pageContent: hit.metadata[this.textField]?.toString() ?? "",
metadata: Object.fromEntries(Object.entries(hit.metadata).filter(([key]) => key !== this.textField))
}), hit.score]);
} else if (response instanceof VectorSearch.Error) throw new Error(response.toString());
else throw new Error(`Unknown response type: ${response.toString()}`);
}
/**
* Return documents selected using the maximal marginal relevance.
* Maximal marginal relevance optimizes for similarity to the query AND diversity
* among selected documents.
*
* @param {string} query - Text to look up documents similar to.
* @param {number} options.k - Number of documents to return.
* @param {number} options.fetchK - Number of documents to fetch before passing to the MMR algorithm.
* @param {number} options.lambda - Number between 0 and 1 that determines the degree of diversity among the results,
* where 0 corresponds to maximum diversity and 1 to minimum diversity.
* @param {this["FilterType"]} options.filter - Optional filter
* @param _callbacks
*
* @returns {Promise<Document[]>} - List of documents selected by maximal marginal relevance.
*/
async maxMarginalRelevanceSearch(query, options) {
const queryEmbedding = await this.embeddings.embedQuery(query);
const response = await this.client.searchAndFetchVectors(this.indexName, queryEmbedding, {
topK: options.fetchK ?? 20,
metadataFields: ALL_VECTOR_METADATA
});
if (response instanceof VectorSearchAndFetchVectors.Success) {
const hits = response.hits();
const embeddingList = hits.map((hit) => hit.vector);
const mmrIndexes = maximalMarginalRelevance(queryEmbedding, embeddingList, options.lambda, options.k);
const finalResult = mmrIndexes.map((index) => {
const hit = hits[index];
const { [this.textField]: pageContent,...metadata } = hit.metadata;
return new Document({
metadata,
pageContent
});
});
return finalResult;
} else if (response instanceof VectorSearchAndFetchVectors.Error) throw new Error(response.toString());
else throw new Error(`Unknown response type: ${response.toString()}`);
}
/**
* Stores the documents in the index.
*
* Converts the documents to vectors using the `Embeddings` instance passed.
* @param texts The texts to store in the index.
* @param metadatas The metadata to store in the index.
* @param embeddings The embeddings instance to use to generate embeddings from the documents.
* @param dbConfig The configuration to use to instantiate the vector store.
* @param documentProps The properties of the documents to add to the index, specifically the ids.
* @returns Promise that resolves to the vector store.
*/
static async fromTexts(texts, metadatas, embeddings, dbConfig, documentProps) {
if (Array.isArray(metadatas) && texts.length !== metadatas.length) throw new Error(`Number of texts (${texts.length}) does not equal number of metadatas (${metadatas.length})`);
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 await this.fromDocuments(docs, embeddings, dbConfig, documentProps);
}
/**
* Stores the documents in the index.
* @param docs The documents to store in the index.
* @param embeddings The embeddings instance to use to generate embeddings from the documents.
* @param dbConfig The configuration to use to instantiate the vector store.
* @param documentProps The properties of the documents to add to the index, specifically the ids.
* @returns Promise that resolves to the vector store.
*/
static async fromDocuments(docs, embeddings, dbConfig, documentProps) {
const vectorStore = new MomentoVectorIndex(embeddings, dbConfig);
await vectorStore.addDocuments(docs, documentProps);
return vectorStore;
}
};
//#endregion
export { MomentoVectorIndex, momento_vector_index_exports };
//# sourceMappingURL=momento_vector_index.js.map