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MVI: the most productive, easiest to use, serverless vector index for your data. To get started with MVI, simply sign up for an account. There’s no need to handle infrastructure, manage servers, or be concerned about scaling. MVI is a service that scales automatically to meet your needs. Whether in Node.js, browser, or edge, Momento has you covered. To sign up and access MVI, visit the Momento Console.

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Setup

  1. Sign up for an API key in the Momento Console.
  2. Install the SDK for your environment. 2.1. For Node.js:
    npm
    npm install @gomomento/sdk
    2.2. For browser or edge environments:
    npm
    npm install @gomomento/sdk-web
  3. Setup Env variables for Momento before running the code 3.1 OpenAI 
    export OPENAI_API_KEY=YOUR_OPENAI_API_KEY_HERE
    3.2 Momento 
    export MOMENTO_API_KEY=YOUR_MOMENTO_API_KEY_HERE # https://console.gomomento.com

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Usage

See this section for general instructions on installing LangChain packages.
npm
npm install @langchain/openai @langchain/community @langchain/core
This example demonstrates using the fromTexts method to instantiate the vector store and index documents. If the index does not exist, then it will be created. If the index already exists, then the documents will be added to the existing index. The ids are optional; if you omit them, then Momento will generate UUIDs for you. 
import { MomentoVectorIndex } from "@langchain/community/vectorstores/momento_vector_index";
// For browser/edge, adjust this to import from "@gomomento/sdk-web";
import {
  PreviewVectorIndexClient,
  VectorIndexConfigurations,
  CredentialProvider,
} from "@gomomento/sdk";
import { OpenAIEmbeddings } from "@langchain/openai";
import { sleep } from "@langchain/classic/util/time";

const vectorStore = await MomentoVectorIndex.fromTexts(
  ["hello world", "goodbye world", "salutations world", "farewell world"],
  {},
  new OpenAIEmbeddings(),
  {
    client: new PreviewVectorIndexClient({
      configuration: VectorIndexConfigurations.Laptop.latest(),
      credentialProvider: CredentialProvider.fromEnvironmentVariable({
        environmentVariableName: "MOMENTO_API_KEY",
      }),
    }),
    indexName: "langchain-example-index",
  },
  { ids: ["1", "2", "3", "4"] }
);

// because indexing is async, wait for it to finish to search directly after
await sleep();

const response = await vectorStore.similaritySearch("hello", 2);

console.log(response);

/*
[
  Document { pageContent: 'hello world', metadata: {} },
  Document { pageContent: 'salutations world', metadata: {} }
]
*/
Similar to the above, this example demonstrates using the fromDocuments method to instantiate the vector store and index documents. If the index does not exist, then it will be created. If the index already exists, then the documents will be added to the existing index. Using fromDocuments allows you to seamlessly chain the various document loaders with indexing. 
import { MomentoVectorIndex } from "@langchain/community/vectorstores/momento_vector_index";
// For browser/edge, adjust this to import from "@gomomento/sdk-web";
import {
  PreviewVectorIndexClient,
  VectorIndexConfigurations,
  CredentialProvider,
} from "@gomomento/sdk";
import { OpenAIEmbeddings } from "@langchain/openai";
import { TextLoader } from "@langchain/classic/document_loaders/fs/text";
import { sleep } from "@langchain/classic/util/time";

// Create docs with a loader
const loader = new TextLoader("src/document_loaders/example_data/example.txt");
const docs = await loader.load();

const vectorStore = await MomentoVectorIndex.fromDocuments(
  docs,
  new OpenAIEmbeddings(),
  {
    client: new PreviewVectorIndexClient({
      configuration: VectorIndexConfigurations.Laptop.latest(),
      credentialProvider: CredentialProvider.fromEnvironmentVariable({
        environmentVariableName: "MOMENTO_API_KEY",
      }),
    }),
    indexName: "langchain-example-index",
  }
);

// because indexing is async, wait for it to finish to search directly after
await sleep();

// Search for the most similar document
const response = await vectorStore.similaritySearch("hello", 1);

console.log(response);
/*
[
  Document {
    pageContent: 'Foo\nBar\nBaz\n\n',
    metadata: { source: 'src/document_loaders/example_data/example.txt' }
  }
]
*/

​
Search from an existing collection

import { MomentoVectorIndex } from "@langchain/community/vectorstores/momento_vector_index";
// For browser/edge, adjust this to import from "@gomomento/sdk-web";
import {
  PreviewVectorIndexClient,
  VectorIndexConfigurations,
  CredentialProvider,
} from "@gomomento/sdk";
import { OpenAIEmbeddings } from "@langchain/openai";

const vectorStore = new MomentoVectorIndex(new OpenAIEmbeddings(), {
  client: new PreviewVectorIndexClient({
    configuration: VectorIndexConfigurations.Laptop.latest(),
    credentialProvider: CredentialProvider.fromEnvironmentVariable({
      environmentVariableName: "MOMENTO_API_KEY",
    }),
  }),
  indexName: "langchain-example-index",
});

const response = await vectorStore.similaritySearch("hello", 1);

console.log(response);
/*
[
  Document {
    pageContent: 'Foo\nBar\nBaz\n\n',
    metadata: { source: 'src/document_loaders/example_data/example.txt' }
  }
]
*/
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