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Vercel Postgres

LangChain.js 支持使用 @vercel/postgres 包将通用 Postgres 数据库用作向量存储，前提是这些数据库支持 pgvector Postgres 扩展。

¥LangChain.js supports using the @vercel/postgres package to use generic Postgres databases as vector stores, provided they support the pgvector Postgres extension.

这种集成在 Edge Functions 等 Web 环境中尤其有用。

¥This integration is particularly useful from web environments like Edge functions.

设置​

¥Setup

要使用 Vercel Postgres，你需要安装 @vercel/postgres 包：

¥To work with Vercel Postgres, you need to install the @vercel/postgres package:

npm install @vercel/postgres
tip

请参阅 此部分包含有关安装集成包的一般说明。

¥See this section for general instructions on installing integration packages.

npm install @langchain/community @langchain/core

此集成使用 process.env.POSTGRES_URL 下设置的连接字符串自动连接。你也可以像这样手动传递连接字符串：

¥This integration automatically connects using the connection string set under process.env.POSTGRES_URL. You can also pass a connection string manually like this:

const vectorstore = await VercelPostgres.initialize(new OpenAIEmbeddings(), {
  postgresConnectionOptions: {
    connectionString:
      "postgres://<username>:<password>@<hostname>:<port>/<dbname>",
  },
});

连接到 Vercel Postgres​

¥Connecting to Vercel Postgres

一个简单的入门方法是创建一个无服务器 Vercel Postgres 实例。如果你要部署到与 Vercel Postgres 实例关联的 Vercel 项目，则所需的 POSTGRES_URL 环境变量将已在托管环境中填充。

¥A simple way to get started is to create a serverless Vercel Postgres instance. If you're deploying to a Vercel project with an associated Vercel Postgres instance, the required POSTGRES_URL environment variable will already be populated in hosted environments.

连接到其他数据库​

¥Connecting to other databases

如果你希望托管自己的 Postgres 实例，则可以使用与 LangChain 的 PGVector 向量存储集成类似的流程，并将连接字符串设置为环境变量或如上所示。

¥If you prefer to host your own Postgres instance, you can use a similar flow to LangChain's PGVector vectorstore integration and set the connection string either as an environment variable or as shown above.

用法​

¥Usage

import { CohereEmbeddings } from "@langchain/cohere";
import { VercelPostgres } from "@langchain/community/vectorstores/vercel_postgres";

// Config is only required if you want to override default values.
const config = {
  // tableName: "testvercelvectorstorelangchain",
  // postgresConnectionOptions: {
  //   connectionString: "postgres://<username>:<password>@<hostname>:<port>/<dbname>",
  // },
  // columns: {
  //   idColumnName: "id",
  //   vectorColumnName: "vector",
  //   contentColumnName: "content",
  //   metadataColumnName: "metadata",
  // },
};

const vercelPostgresStore = await VercelPostgres.initialize(
  new CohereEmbeddings({ model: "embed-english-v3.0" }),
  config
);

const docHello = {
  pageContent: "hello",
  metadata: { topic: "nonsense" },
};
const docHi = { pageContent: "hi", metadata: { topic: "nonsense" } };
const docMitochondria = {
  pageContent: "Mitochondria is the powerhouse of the cell",
  metadata: { topic: "science" },
};

const ids = await vercelPostgresStore.addDocuments([
  docHello,
  docHi,
  docMitochondria,
]);

const results = await vercelPostgresStore.similaritySearch("hello", 2);
console.log(results);
/*
  [
    Document { pageContent: 'hello', metadata: { topic: 'nonsense' } },
    Document { pageContent: 'hi', metadata: { topic: 'nonsense' } }
  ]
*/

// Metadata filtering
const results2 = await vercelPostgresStore.similaritySearch(
  "Irrelevant query, metadata filtering",
  2,
  {
    topic: "science",
  }
);
console.log(results2);
/*
  [
    Document {
      pageContent: 'Mitochondria is the powerhouse of the cell',
      metadata: { topic: 'science' }
    }
  ]
*/

// Metadata filtering with IN-filters works as well
const results3 = await vercelPostgresStore.similaritySearch(
  "Irrelevant query, metadata filtering",
  3,
  {
    topic: { in: ["science", "nonsense"] },
  }
);
console.log(results3);
/*
  [
    Document {
      pageContent: 'hello',
      metadata: { topic: 'nonsense' }
    },
    Document {
      pageContent: 'hi',
      metadata: { topic: 'nonsense' }
    },
    Document {
      pageContent: 'Mitochondria is the powerhouse of the cell',
      metadata: { topic: 'science' }
    }
  ]
*/

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

const results4 = await vercelPostgresStore.similaritySearch(
  "What is the powerhouse of the cell?",
  1
);
console.log(results4);
/*
  [
    Document {
      pageContent: 'ATP is the powerhouse of the cell',
      metadata: { topic: 'science' }
    }
  ]
*/

await vercelPostgresStore.delete({ ids: [ids[2]] });

const results5 = await vercelPostgresStore.similaritySearch(
  "No more metadata",
  2,
  {
    topic: "science",
  }
);
console.log(results5);
/*
  []
*/

// Remember to call .end() to close the connection!
await vercelPostgresStore.end();

API Reference:

¥Related