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SingleStore

SingleStoreDB is a high-performance distributed SQL database that supports deployment both in the cloud and on-premise. It provides vector storage, as well as vector functions like dot_product and euclidean_distance, thereby supporting AI applications that require text similarity matching.

Compatibility

Only available on Node.js.

LangChain.js requires the mysql2 library to create a connection to a SingleStoreDB instance.

Setup​

  1. Establish a SingleStoreDB environment. You have the flexibility to choose between Cloud-based or On-Premise editions.
  2. Install the mysql2 JS client
npm install -S mysql2

Usage​

SingleStoreVectorStore manages a connection pool. It is recommended to call await store.end(); before terminating your application to assure all connections are appropriately closed and prevent any possible resource leaks.

Standard usage​

tip

See this section for general instructions on installing integration packages.

npm install @langchain/openai @langchain/community

Below is a straightforward example showcasing how to import the relevant module and perform a base similarity search using the SingleStoreVectorStore:

import { SingleStoreVectorStore } from "@langchain/community/vectorstores/singlestore";
import { OpenAIEmbeddings } from "@langchain/openai";

export const run = async () => {
  const vectorStore = await SingleStoreVectorStore.fromTexts(
    ["Hello world", "Bye bye", "hello nice world"],
    [{ id: 2 }, { id: 1 }, { id: 3 }],
    new OpenAIEmbeddings(),
    {
      connectionOptions: {
        host: process.env.SINGLESTORE_HOST,
        port: Number(process.env.SINGLESTORE_PORT),
        user: process.env.SINGLESTORE_USERNAME,
        password: process.env.SINGLESTORE_PASSWORD,
        database: process.env.SINGLESTORE_DATABASE,
      },
    }
  );

  const resultOne = await vectorStore.similaritySearch("hello world", 1);
  console.log(resultOne);
  await vectorStore.end();
};

API Reference:

Metadata Filtering​

If it is needed to filter results based on specific metadata fields, you can pass a filter parameter to narrow down your search to the documents that match all specified fields in the filter object:

import { SingleStoreVectorStore } from "@langchain/community/vectorstores/singlestore";
import { OpenAIEmbeddings } from "@langchain/openai";

export const run = async () => {
  const vectorStore = await SingleStoreVectorStore.fromTexts(
    ["Good afternoon", "Bye bye", "Boa tarde!", "Até logo!"],
    [
      { id: 1, language: "English" },
      { id: 2, language: "English" },
      { id: 3, language: "Portugese" },
      { id: 4, language: "Portugese" },
    ],
    new OpenAIEmbeddings(),
    {
      connectionOptions: {
        host: process.env.SINGLESTORE_HOST,
        port: Number(process.env.SINGLESTORE_PORT),
        user: process.env.SINGLESTORE_USERNAME,
        password: process.env.SINGLESTORE_PASSWORD,
        database: process.env.SINGLESTORE_DATABASE,
      },
      distanceMetric: "EUCLIDEAN_DISTANCE",
    }
  );

  const resultOne = await vectorStore.similaritySearch("greetings", 1, {
    language: "Portugese",
  });
  console.log(resultOne);
  await vectorStore.end();
};

API Reference:

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