CompatibilityOnly available on Node.js.
DataStax Astra DB is a serverless vector-capable database built on Apache Cassandra and made conveniently available through an easy-to-use JSON API.

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Setup

  1. Create an Astra DB account.
  2. Create a vector enabled database.
  3. Grab your API Endpoint and Token from the Database Details.
  4. Set up the following env vars:
export ASTRA_DB_APPLICATION_TOKEN=YOUR_ASTRA_DB_APPLICATION_TOKEN_HERE
export ASTRA_DB_ENDPOINT=YOUR_ASTRA_DB_ENDPOINT_HERE
export ASTRA_DB_COLLECTION=YOUR_ASTRA_DB_COLLECTION_HERE
export OPENAI_API_KEY=YOUR_OPENAI_API_KEY_HERE
Where ASTRA_DB_COLLECTION is the desired name of your collection
  1. Install the Astra TS Client & the LangChain community package
See this section for general instructions on installing LangChain packages.
npm
npm install @langchain/openai @datastax/astra-db-ts @langchain/community @langchain/core

​
Indexing docs

import { OpenAIEmbeddings } from "@langchain/openai";
import {
  AstraDBVectorStore,
  AstraLibArgs,
} from "@langchain/community/vectorstores/astradb";

const astraConfig: AstraLibArgs = {
  token: process.env.ASTRA_DB_APPLICATION_TOKEN as string,
  endpoint: process.env.ASTRA_DB_ENDPOINT as string,
  collection: process.env.ASTRA_DB_COLLECTION ?? "langchain_test",
  collectionOptions: {
    vector: {
      dimension: 1536,
      metric: "cosine",
    },
  },
};

const vectorStore = await AstraDBVectorStore.fromTexts(
  [
    "AstraDB is built on Apache Cassandra",
    "AstraDB is a NoSQL DB",
    "AstraDB supports vector search",
  ],
  [{ foo: "foo" }, { foo: "bar" }, { foo: "baz" }],
  new OpenAIEmbeddings(),
  astraConfig
);

// Querying docs:
const results = await vectorStore.similaritySearch("Cassandra", 1);

// or filtered query:
const filteredQueryResults = await vectorStore.similaritySearch("A", 1, {
  foo: "bar",
});

​
Vector Types

Astra DB supports cosine (the default), dot_product, and euclidean similarity search; this is defined when the vector store is first created as part of the CreateCollectionOptions: 
  vector: {
      dimension: number;
      metric?: "cosine" | "euclidean" | "dot_product";
  };