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AzionVectorStore

AzionVectorStore 用于在 Azion 的 Edge 平台上，使用 Edge SQL 直接管理和搜索文档集合中的向量嵌入。

本指南提供了关于如何开始使用 Azion EdgeSQL 向量存储 的快速概述。如需了解 AzionVectorStore 所有功能和配置的详细文档，请参阅 API 参考文档。

概述​

集成详情​

类包[PY 支持]包最新版本
AzionVectorStore@langchain/community❌NPM - 版本

配置​

要使用 AzionVectorStore 向量存储，你需要安装 @langchain/community 包。除此之外，你还需要一个 Azion 账户 和一个 Token 来使用 Azion API，并将其配置为环境变量 AZION_TOKEN。更多相关信息可以在 文档 中找到。

本指南还将使用 OpenAI 嵌入，这需要你安装 @langchain/openai 集成包。如果需要，你也可以使用 其他支持的嵌入模型。

:::提示 请参阅安装集成包的一般说明部分。 :::

yarn add azion @langchain/openai @langchain/community

凭证​

完成这些步骤后，请设置 AZION_TOKEN 环境变量：

process.env.AZION_TOKEN = "your-api-key";

如果你在本指南中使用 OpenAI 嵌入，还需要设置你的 OpenAI 密钥：

process.env.OPENAI_API_KEY = "YOUR_API_KEY";

如果你想获得模型调用的自动追踪，也可以通过取消注释以下内容来设置你的 LangSmith API 密钥：

// process.env.LANGCHAIN_TRACING_V2="true"
// process.env.LANGCHAIN_API_KEY="your-api-key"

实例化​

import { AzionVectorStore } from "@langchain/community/vectorstores/azion_edgesql";
import { OpenAIEmbeddings } from "@langchain/openai";

const embeddings = new OpenAIEmbeddings({
  model: "text-embedding-3-small",
});

// Instantiate with the constructor if the database and table have already been created
const vectorStore = new AzionVectorStore(embeddings, {
  dbName: "langchain",
  tableName: "documents",
});

// If you have not created the database and table yet, you can do so with the setupDatabase method
// await vectorStore.setupDatabase({ columns:["topic","language"], mode: "hybrid" })

// OR instantiate with the static method if the database and table have not been created yet
// const vectorStore = await AzionVectorStore.initialize(embeddingModel, { dbName: "langchain", tableName: "documents" }, { columns:[], mode: "hybrid" })

管理向量存储​

向向量存储中添加项目​

import type { Document } from "@langchain/core/documents";

const document1: Document = {
  pageContent: "The powerhouse of the cell is the mitochondria",
  metadata: { language: "en", topic: "biology" },
};

const document2: Document = {
  pageContent: "Buildings are made out of brick",
  metadata: { language: "en", topic: "history" },
};

const document3: Document = {
  pageContent: "Mitochondria are made out of lipids",
  metadata: { language: "en", topic: "biology" },
};

const document4: Document = {
  pageContent: "The 2024 Olympics are in Paris",
  metadata: { language: "en", topic: "history" },
};

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

await vectorStore.addDocuments(documents);
Inserting chunks
Inserting chunk 0
Chunks inserted!

从向量存储中删除项目​

await vectorStore.delete(["4"]);
Deleted 1 items from documents

查询向量存储​

一旦创建了向量存储并添加了相关文档，在运行链或代理时很可能需要对其进行查询。

直接查询​

执行一个简单的相似性搜索可以按以下方式进行：

const filter = [{ operator: "=", column: "language", value: "en" }];

const hybridSearchResults = await vectorStore.azionHybridSearch("biology", {
  kfts: 2,
  kvector: 1,
  filter: [{ operator: "=", column: "language", value: "en" }],
});

console.log("Hybrid Search Results");
for (const doc of hybridSearchResults) {
  console.log(`${JSON.stringify(doc)}`);
}
Hybrid Search Results
[{"pageContent":"The Australian dingo is a unique species that plays a key role in the ecosystem","metadata":{"searchtype":"fulltextsearch"},"id":"6"},-0.25748711028997995]
[{"pageContent":"The powerhouse of the cell is the mitochondria","metadata":{"searchtype":"fulltextsearch"},"id":"16"},-0.31697985337654005]
[{"pageContent":"Australia s indigenous people have inhabited the continent for over 65,000 years","metadata":{"searchtype":"similarity"},"id":"3"},0.14822345972061157]
const similaritySearchResults = await vectorStore.azionSimilaritySearch(
  "australia",
  { kvector: 3, filter: [{ operator: "=", column: "topic", value: "history" }] }
);

console.log("Similarity Search Results");
for (const doc of similaritySearchResults) {
  console.log(`${JSON.stringify(doc)}`);
}
Similarity Search Results
[{"pageContent":"Australia s indigenous people have inhabited the continent for over 65,000 years","metadata":{"searchtype":"similarity"},"id":"3"},0.4486490488052368]

通过转换为检索器进行查询​

你还可以将向量存储转换为检索器，以便在你的链中更方便地使用。

const retriever = vectorStore.asRetriever({
  // Optional filter
  filter: filter,
  k: 2,
});
await retriever.invoke("biology");
[
  Document {
    pageContent: 'Australia s indigenous people have inhabited the continent for over 65,000 years',
    metadata: { searchtype: 'similarity' },
    id: '3'
  },
  Document {
    pageContent: 'Mitochondria are made out of lipids',
    metadata: { searchtype: 'similarity' },
    id: '18'
  }
]

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：

API 参考文档​

如需详细了解 AzionVectorStore 的所有功能和配置，请访问 API 参考文档。

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