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AzionRetriever

概述​

¥Overview

这将帮助你开始使用 AzionRetriever。有关所有 AzionRetriever 功能和配置的详细文档，请前往 API 参考。

¥This will help you getting started with the AzionRetriever. For detailed documentation of all AzionRetriever features and configurations head to the API reference.

集成详情​

¥Integration details

Retriever Self-host Cloud offering Package [Py support]
AzionRetriever ❌ ❌ @langchain/community ❌

设置​

¥Setup

要使用 AzionRetriever，你需要设置 AZION_TOKEN 环境变量。

¥To use the AzionRetriever, you need to set the AZION_TOKEN environment variable.

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

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

¥If you are using OpenAI embeddings for this guide, you’ll need to set your OpenAI key as well:

process.env.OPENAI_API_KEY = "YOUR_API_KEY";

如果你想自动追踪单个查询，也可以通过取消注释以下内容来设置你的 LangSmith API 密钥：

¥If you want to get automated tracing from individual queries, you can also set your LangSmith API key by uncommenting below:

// process.env.LANGSMITH_API_KEY = "<YOUR API KEY HERE>";
// process.env.LANGSMITH_TRACING = "true";

安装​

¥Installation

此检索器位于 @langchain/community/retrievers/azion_edgesql 包中：

¥This retriever lives in the @langchain/community/retrievers/azion_edgesql package:

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.

yarn add azion @langchain/openai @langchain/community

实例化​

¥Instantiation

现在我们可以实例化我们的检索器：

¥Now we can instantiate our retriever:

import { AzionRetriever } from "@langchain/community/retrievers/azion_edgesql";
import { OpenAIEmbeddings } from "@langchain/openai";
import { ChatOpenAI } from "@langchain/openai";

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

const chatModel = new ChatOpenAI({
  model: "gpt-4o-mini",
  apiKey: process.env.OPENAI_API_KEY,
});

const retriever = new AzionRetriever(embeddingModel, {
  dbName: "langchain",
  vectorTable: "documents", // table where the vector embeddings are stored
  ftsTable: "documents_fts", // table where the fts index is stored
  searchType: "hybrid", // search type to use for the retriever
  ftsK: 2, // number of results to return from the fts index
  similarityK: 2, // number of results to return from the vector index
  metadataItems: ["language", "topic"],
  filters: [{ operator: "=", column: "language", value: "en" }],
  entityExtractor: chatModel,
}); // number of results to return from the vector index

用法​

¥Usage

const query = "Australia";

await retriever.invoke(query);
[
  Document {
    pageContent: 'Australia s indigenous people have inhabited the continent for over 65,000 years',
    metadata: { language: 'en', topic: 'history', searchtype: 'similarity' },
    id: '3'
  },
  Document {
    pageContent: 'Australia is a leader in solar energy adoption and renewable technology',
    metadata: { language: 'en', topic: 'technology', searchtype: 'similarity' },
    id: '5'
  },
  Document {
    pageContent: 'Australia s tech sector is rapidly growing with innovation hubs in major cities',
    metadata: { language: 'en', topic: 'technology', searchtype: 'fts' },
    id: '7'
  }
]

在链中使用​

¥Use within a chain

与其他检索器一样，AzionRetriever 可以通过 chains 集成到 LLM 应用中。

¥Like other retrievers, AzionRetriever can be incorporated into LLM applications via chains.

我们需要一个 LLM 或聊天模型：

¥We will need a LLM or chat model:

Pick your chat model:

Install dependencies

tip

See this section for general instructions on installing integration packages.

yarn add @langchain/groq

Add environment variables

GROQ_API_KEY=your-api-key

Instantiate the model

import { ChatGroq } from "@langchain/groq";

const llm = new ChatGroq({
  model: "llama-3.3-70b-versatile",
  temperature: 0
});
import { ChatPromptTemplate } from "@langchain/core/prompts";
import {
  RunnablePassthrough,
  RunnableSequence,
} from "@langchain/core/runnables";
import { StringOutputParser } from "@langchain/core/output_parsers";

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

const prompt = ChatPromptTemplate.fromTemplate(`
Answer the question based only on the context provided.

Context: {context}

Question: {question}`);

const formatDocs = (docs: Document[]) => {
  return docs.map((doc) => doc.pageContent).join("\n\n");
};

// See https://langchain.nodejs.cn/docs/tutorials/rag
const ragChain = RunnableSequence.from([
  {
    context: retriever.pipe(formatDocs),
    question: new RunnablePassthrough(),
  },
  prompt,
  llm,
  new StringOutputParser(),
]);
await ragChain.invoke("Paris");
The context mentions that the 2024 Olympics are in Paris.

API 参考​

¥API reference

有关所有 AzionRetriever 功能和配置的详细文档，请前往 API 参考。

¥For detailed documentation of all AzionRetriever features and configurations head to the API reference.