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CompatibilityOnly available on Node.js.
MyScale is an emerging AI database that harmonizes the power of vector search and SQL analytics, providing a managed, efficient, and responsive experience.

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Setup

  1. Launch a cluster through MyScale’s Web Console. See MyScale’s official documentation for more information.
  2. After launching a cluster, view your Connection Details from your cluster’s Actions menu. You will need the host, port, username, and password.
  3. Install the required Node.js peer dependency in your workspace.
See this section for general instructions on installing LangChain packages.
npm
npm install -S @langchain/openai @clickhouse/client @langchain/community @langchain/core

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Index and Query Docs

import { MyScaleStore } from "@langchain/community/vectorstores/myscale";
import { OpenAIEmbeddings } from "@langchain/openai";

const vectorStore = await MyScaleStore.fromTexts(
  ["Hello world", "Bye bye", "hello nice world"],
  [
    { id: 2, name: "2" },
    { id: 1, name: "1" },
    { id: 3, name: "3" },
  ],
  new OpenAIEmbeddings(),
  {
    host: process.env.MYSCALE_HOST || "localhost",
    port: process.env.MYSCALE_PORT || "8443",
    username: process.env.MYSCALE_USERNAME || "username",
    password: process.env.MYSCALE_PASSWORD || "password",
    database: "default", // defaults to "default"
    table: "your_table", // defaults to "vector_table"
  }
);

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

const filteredResults = await vectorStore.similaritySearch("hello world", 1, {
  whereStr: "metadata.name = '1'",
});
console.log(filteredResults);

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Query Docs From an Existing Collection

import { MyScaleStore } from "@langchain/community/vectorstores/myscale";
import { OpenAIEmbeddings } from "@langchain/openai";

const vectorStore = await MyScaleStore.fromExistingIndex(
  new OpenAIEmbeddings(),
  {
    host: process.env.MYSCALE_HOST || "localhost",
    port: process.env.MYSCALE_PORT || "8443",
    username: process.env.MYSCALE_USERNAME || "username",
    password: process.env.MYSCALE_PASSWORD || "password",
    database: "default", // defaults to "default"
    table: "your_table", // defaults to "vector_table"
  }
);

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

const filteredResults = await vectorStore.similaritySearch("hello world", 1, {
  whereStr: "metadata.name = '1'",
});
console.log(filteredResults);
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