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兼容性：仅在 Node.js 上可用。
这需要 MariaDB 11.7 或更高版本 本指南提供了 MariaDB 向量存储入门的快速概述。有关所有 MariaDB store 功能和配置的详细文档，请参阅 API 参考。

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概述

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集成细节

ClassPackagePY supportVersion
MariaDBStore@langchain/community✅NPM - Version

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设置

要使用 MariaDBVector 向量存储，您需要设置 MariaDB 11.7 版本或更高版本，并将 mariadb 连接器作为对等依赖项。 本指南还将使用 OpenAI 嵌入，这需要您安装 @langchain/openai 集成包。如果您愿意，也可以使用 其他支持的嵌入模型。 我们还将使用 uuid 包以所需的格式生成 ID。 
npm install @langchain/community @langchain/openai @langchain/core mariadb uuid

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设置实例

创建一个名为 docker-compose.yml 的文件，内容如下： 
# Run this command to start the database:
# docker-compose up --build
version: "3"
services:
  db:
    hostname: 127.0.0.1
    image: mariadb/mariadb:11.7-rc
    ports:
      - 3306:3306
    restart: always
    environment:
      - MARIADB_DATABASE=api
      - MARIADB_USER=myuser
      - MARIADB_PASSWORD=ChangeMe
      - MARIADB_ROOT_PASSWORD=ChangeMe
    volumes:
      - ./init.sql:/docker-entrypoint-initdb.d/init.sql
然后在同一目录中，运行 docker compose up 以启动容器。

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凭据

要连接到您的 MariaDB 实例，您需要相应的凭据。有关支持的选项的完整列表，请参阅 mariadb 文档。 如果您在本指南中使用 OpenAI 嵌入，您还需要设置您的 OpenAI 密钥： 
process.env.OPENAI_API_KEY = "YOUR_API_KEY";
如果您想获取模型调用的自动跟踪，您还可以通过取消注释以下内容来设置您的 LangSmith API 密钥： 
// process.env.LANGCHAIN_TRACING_V2="true"
// process.env.LANGCHAIN_API_KEY="your-api-key"

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实例化

要实例化向量存储，请调用 .initialize() 静态方法。这将自动检查表中是否存在 config 中给出的 tableName。如果不存在，它将使用所需的列创建它。 
import { OpenAIEmbeddings } from "@langchain/openai";

import {
   DistanceStrategy,
   MariaDBStore,
} from "@langchain/community/vectorstores/mariadb";
import { PoolConfig } from "mariadb";

const config = {
  connectionOptions: {
    type: "mariadb",
    host: "127.0.0.1",
    port: 3306,
    user: "myuser",
    password: "ChangeMe",
    database: "api",
  } as PoolConfig,
  distanceStrategy: 'EUCLIDEAN' as DistanceStrategy,
};
const vectorStore = await MariaDBStore.initialize(
  new OpenAIEmbeddings(),
   config
);

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管理向量存储

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向向量存储添加项目

import { v4 as uuidv4 } from "uuid";
import type { Document } from "@langchain/core/documents";

const document1: Document = {
  pageContent: "The powerhouse of the cell is the mitochondria",
  metadata: { source: "https://example.com" }
};

const document2: Document = {
  pageContent: "Buildings are made out of brick",
  metadata: { source: "https://example.com" }
};

const document3: Document = {
  pageContent: "Mitochondria are made out of lipids",
  metadata: { source: "https://example.com" }
};

const document4: Document = {
  pageContent: "The 2024 Olympics are in Paris",
  metadata: { source: "https://example.com" }
}

const documents = [document1, document2, document3, document4];

const ids = [uuidv4(), uuidv4(), uuidv4(), uuidv4()]

// ids are not mandatory, but that's for the example
await vectorStore.addDocuments(documents, { ids: ids });

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从向量存储删除项目

const id4 = ids[ids.length - 1];

await vectorStore.delete({ ids: [id4] });

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查询向量存储

一旦您的向量存储创建完毕并添加了相关文档，您很可能希望在运行链或代理期间对其进行查询。

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直接查询

执行简单的相似性搜索可以按如下方式完成： 
const similaritySearchResults = await vectorStore.similaritySearch("biology", 2, { "year": 2021 });
for (const doc of similaritySearchResults) {
  console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
}

* The powerhouse of the cell is the mitochondria [{"year": 2021}]
* Mitochondria are made out of lipids [{"year": 2022}]
上述过滤器语法使用更复杂： 
# name = 'martin' OR firstname = 'john'
let res = await vectorStore.similaritySearch("biology", 2, {"$or": [{"name":"martin"}, {"firstname", "john"}] });
如果您想执行相似性搜索并接收相应的分数，您可以运行： 
const similaritySearchWithScoreResults = await vectorStore.similaritySearchWithScore("biology", 2)

for (const [doc, score] of similaritySearchWithScoreResults) {
  console.log(`* [SIM=${score.toFixed(3)}] ${doc.pageContent} [${JSON.stringify(doc.metadata)}]`);
}

* [SIM=0.835] The powerhouse of the cell is the mitochondria [{"source":"https://example.com"}]
* [SIM=0.852] Mitochondria are made out of lipids [{"source":"https://example.com"}]

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通过转换为检索器进行查询

您还可以将向量存储转换为 检索器，以便在链中更轻松地使用。 
const retriever = vectorStore.asRetriever({
  // Optional filter
  // filter: filter,
  k: 2,
});
await retriever.invoke("biology");

[
  Document {
    pageContent: 'The powerhouse of the cell is the mitochondria',
    metadata: { source: 'https://example.com' },
    id: undefined
  },
  Document {
    pageContent: 'Mitochondria are made out of lipids',
    metadata: { source: 'https://example.com' },
    id: undefined
  }
]

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用于检索增强生成的用法

有关如何将此向量存储用于检索增强生成 (RAG) 的指南，请参阅以下部分：

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高级：重用连接

您可以通过创建一个池来重用连接，然后通过构造函数直接创建新的 MariaDBStore 实例。 请注意，在使用构造函数之前，您应该至少调用一次 .initialize() 来设置数据库，以便正确设置表。 
import { OpenAIEmbeddings } from "@langchain/openai";
import { MariaDBStore } from "@langchain/community/vectorstores/mariadb";
import mariadb from "mariadb";

// First, follow set-up instructions at
// https://js.langchain.com/docs/modules/indexes/vector_stores/integrations/mariadb

const reusablePool = mariadb.createPool({
  host: "127.0.0.1",
  port: 3306,
  user: "myuser",
  password: "ChangeMe",
  database: "api",
});

const originalConfig = {
  pool: reusablePool,
  tableName: "testlangchainjs",
  collectionName: "sample",
  collectionTableName: "collections",
  columns: {
    idColumnName: "id",
    vectorColumnName: "vect",
    contentColumnName: "content",
    metadataColumnName: "metadata",
  },
};

// Set up the DB.
// Can skip this step if you've already initialized the DB.
// await MariaDBStore.initialize(new OpenAIEmbeddings(), originalConfig);
const mariadbStore = new MariaDBStore(new OpenAIEmbeddings(), originalConfig);

await mariadbStore.addDocuments([
  { pageContent: "what's this", metadata: { a: 2 } },
  { pageContent: "Cat drinks milk", metadata: { a: 1 } },
]);

const results = await mariadbStore.similaritySearch("water", 1);

console.log(results);

/*
  [ Document { pageContent: 'Cat drinks milk', metadata: { a: 1 } } ]
*/

const mariadbStore2 = new MariaDBStore(new OpenAIEmbeddings(), {
  pool: reusablePool,
  tableName: "testlangchainjs",
  collectionTableName: "collections",
  collectionName: "some_other_collection",
  columns: {
    idColumnName: "id",
    vectorColumnName: "vector",
    contentColumnName: "content",
    metadataColumnName: "metadata",
  },
});

const results2 = await mariadbStore2.similaritySearch("water", 1);

console.log(results2);

/*
  []
*/

await reusablePool.end();

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关闭连接

确保在完成后关闭连接，以避免过多的资源消耗： 
await vectorStore.end();

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API 参考

有关所有 MariaDBStore 功能和配置的详细文档，请参阅 API 参考。
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