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Neon Postgres

Neon 是一个完全托管的无服务器 PostgreSQL 数据库。它将存储和计算分离，从而提供诸如即时分支和自动扩展等功能。

通过 pgvector 扩展，Neon 提供了一个向量存储，可以与 LangChain.js 一起使用来存储和查询嵌入向量。

准备工作​

选择一个 Neon 项目​

如果您还没有 Neon 账户，请前往 Neon 注册一个。登录 Neon 控制台后，进入 项目 页面，选择现有项目或创建一个新项目。

您的 Neon 项目附带了一个开箱即用的 PostgreSQL 数据库，名为 neondb，可用于存储嵌入向量。进入连接详细信息部分，找到数据库连接字符串。它应该类似于以下内容：

postgres://alex:[email protected]/dbname?sslmode=require

请将连接字符串保存下来以供后续使用。

应用代码​

要使用 Neon Postgres，您需要安装 @neondatabase/serverless 包，该包提供了一个用于连接数据库的 JavaScript/TypeScript 驱动程序。

npm install @neondatabase/serverless

:::提示 请参阅安装集成包的一般说明部分。 :::

npm install @langchain/community @langchain/core

要初始化 NeonPostgres 向量存储，您需要提供 Neon 数据库的连接字符串。您可以直接使用上面获取的连接字符串，或者将其存储为环境变量并在代码中引用。

const vectorStore = await NeonPostgres.initialize(embeddings, {
  connectionString: NEON_POSTGRES_CONNECTION_STRING,
});

使用方法​

import { OpenAIEmbeddings } from "@langchain/openai";
import { NeonPostgres } from "@langchain/community/vectorstores/neon";

// Initialize an embeddings instance
const embeddings = new OpenAIEmbeddings({
  apiKey: process.env.OPENAI_API_KEY,
  dimensions: 256,
  model: "text-embedding-3-small",
});

// Initialize a NeonPostgres instance to store embedding vectors
const vectorStore = await NeonPostgres.initialize(embeddings, {
  connectionString: process.env.DATABASE_URL as string,
});

// You can add documents to the store, strings in the `pageContent` field will be embedded
// and stored in the database
const documents = [
  { pageContent: "Hello world", metadata: { topic: "greeting" } },
  { pageContent: "Bye bye", metadata: { topic: "greeting" } },
  {
    pageContent: "Mitochondria is the powerhouse of the cell",
    metadata: { topic: "science" },
  },
];
const idsInserted = await vectorStore.addDocuments(documents);

// You can now query the store for similar documents to the input query
const resultOne = await vectorStore.similaritySearch("hola", 1);
console.log(resultOne);
/*
[
  Document {
    pageContent: 'Hello world',
    metadata: { topic: 'greeting' }
  }
]
*/

// You can also filter by metadata
const resultTwo = await vectorStore.similaritySearch("Irrelevant query", 2, {
  topic: "science",
});
console.log(resultTwo);
/*
[
  Document {
    pageContent: 'Mitochondria is the powerhouse of the cell',
    metadata: { topic: 'science' }
  }
]
*/

// Metadata filtering with IN-filters works as well
const resultsThree = await vectorStore.similaritySearch("Irrelevant query", 2, {
  topic: { in: ["greeting"] },
});
console.log(resultsThree);
/*
[
  Document { pageContent: 'Bye bye', metadata: { topic: 'greeting' } },
  Document {
    pageContent: 'Hello world',
    metadata: { topic: 'greeting' }
  }
]
*/

// Upserting is supported as well
await vectorStore.addDocuments(
  [
    {
      pageContent: "ATP is the powerhouse of the cell",
      metadata: { topic: "science" },
    },
  ],
  { ids: [idsInserted[2]] }
);

const resultsFour = await vectorStore.similaritySearch(
  "powerhouse of the cell",
  1
);
console.log(resultsFour);
/*
[
  Document {
    pageContent: 'ATP is the powerhouse of the cell',
    metadata: { topic: 'science' }
  }
]
*/

API Reference:

相关内容​

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