ClickHouse

兼容性

仅适用于 Node.js。

ClickHouse 是一个强大且开源的列式数据库，用于处理分析查询和高效存储，ClickHouse 设计用于提供向量搜索和分析的强大组合。

设置

1. 启动 ClickHouse 集群。有关详细信息，请参考 ClickHouse 安装指南。
2. 启动 ClickHouse 集群后，从集群的“操作”菜单中检索“连接详细信息”。您将需要主机、端口、用户名和密码。
3. 在您的工作区中安装 ClickHouse 所需的 Node.js 对等依赖项。

您需要安装以下对等依赖项

npm

npm install -S @clickhouse/client mysql2

Yarn

yarn add @clickhouse/client mysql2

pnpm

pnpm add @clickhouse/client mysql2

提示

请参阅 此部分以获取有关安装集成包的常规说明。

npm

npm install @langchain/openai @langchain/community

Yarn

yarn add @langchain/openai @langchain/community

pnpm

pnpm add @langchain/openai @langchain/community

索引和查询文档

import { ClickHouseStore } from "@langchain/community/vectorstores/clickhouse";
import { OpenAIEmbeddings } from "@langchain/openai";

// Initialize ClickHouse store from texts
const vectorStore = await ClickHouseStore.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.CLICKHOUSE_HOST || "localhost",
    port: process.env.CLICKHOUSE_PORT || 8443,
    username: process.env.CLICKHOUSE_USER || "username",
    password: process.env.CLICKHOUSE_PASSWORD || "password",
    database: process.env.CLICKHOUSE_DATABASE || "default",
    table: process.env.CLICKHOUSE_TABLE || "vector_table",
  }
);

// Sleep 1 second to ensure that the search occurs after the successful insertion of data.
// eslint-disable-next-line no-promise-executor-return
await new Promise((resolve) => setTimeout(resolve, 1000));

// Perform similarity search without filtering
const results = await vectorStore.similaritySearch("hello world", 1);
console.log(results);

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

API 参考

• ClickHouseStore 来自 @langchain/community/vectorstores/clickhouse
• OpenAIEmbeddings 来自 @langchain/openai

从现有集合查询文档

import { ClickHouseStore } from "@langchain/community/vectorstores/clickhouse";
import { OpenAIEmbeddings } from "@langchain/openai";

// Initialize ClickHouse store
const vectorStore = await ClickHouseStore.fromExistingIndex(
  new OpenAIEmbeddings(),
  {
    host: process.env.CLICKHOUSE_HOST || "localhost",
    port: process.env.CLICKHOUSE_PORT || 8443,
    username: process.env.CLICKHOUSE_USER || "username",
    password: process.env.CLICKHOUSE_PASSWORD || "password",
    database: process.env.CLICKHOUSE_DATABASE || "default",
    table: process.env.CLICKHOUSE_TABLE || "vector_table",
  }
);

// Sleep 1 second to ensure that the search occurs after the successful insertion of data.
// eslint-disable-next-line no-promise-executor-return
await new Promise((resolve) => setTimeout(resolve, 1000));

// Perform similarity search without filtering
const results = await vectorStore.similaritySearch("hello world", 1);
console.log(results);

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

API 参考

• ClickHouseStore 来自 @langchain/community/vectorstores/clickhouse
• OpenAIEmbeddings 来自 @langchain/openai

相关

• 向量存储 概念指南
• 向量存储 操作指南