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Azure DocumentDB 可以轻松创建一个具有完整原生 MongoDB 支持的数据库。您可以通过将应用程序指向连接字符串来应用您的 MongoDB 经验,并继续使用您喜欢的 MongoDB 驱动程序、SDK 和工具。在 Azure DocumentDB 中使用向量搜索,可以将您的基于 AI 的应用程序与存储在 Azure DocumentDB 中的数据无缝集成。
Azure DocumentDB 为开发人员提供了一个完全托管的 MongoDB 兼容数据库服务,用于使用熟悉的架构构建现代应用程序。 请从此页面了解如何利用 Azure DocumentDB 的向量搜索功能。如果您没有 Azure 账户,可以创建一个免费账户来开始使用。

设置

您首先需要安装 @langchain/azure-cosmosdb 包:
npm
npm install @langchain/azure-cosmosdb @langchain/core
您还需要有一个正在运行的 Azure DocumentDB 实例。您可以按照此指南在 Azure 门户上免费部署一个版本。 一旦您的实例开始运行,请确保您拥有连接字符串和管理员密钥。您可以在 Azure 门户中,在实例的“连接字符串”部分下找到它们。然后您需要设置以下环境变量:
.env vars
AZURE_DOCUMENTDB_CONNECTION_STRING=

示例

以下是一个示例,它将文件中的文档索引到 Azure DocumentDB 中,运行向量搜索查询,最后使用链根据检索到的文档以自然语言回答问题。
import {
  AzureDocumentDBVectorStore,
  AzureDocumentDBSimilarityType,
} from "@langchain/azure-cosmosdb";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";
import { createStuffDocumentsChain } from "@langchain/classic/chains/combine_documents";
import { createRetrievalChain } from "@langchain/classic/chains/retrieval";
import { TextLoader } from "@langchain/classic/document_loaders/fs/text";
import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";

// 从文件加载文档
const loader = new TextLoader("./state_of_the_union.txt");
const rawDocuments = await loader.load();
const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: 1000,
  chunkOverlap: 0,
});
const documents = await splitter.splitDocuments(rawDocuments);

// 创建 Azure DocumentDB 向量存储
const store = await AzureDocumentDBVectorStore.fromDocuments(
  documents,
  new OpenAIEmbeddings(),
  {
    databaseName: "langchain",
    collectionName: "documents",
    indexOptions: {
      numLists: 100,
      dimensions: 1536,
      similarity: AzureDocumentDBSimilarityType.COS,
    },
  }
);

// 执行相似性搜索
const resultDocuments = await store.similaritySearch(
  "What did the president say about Ketanji Brown Jackson?"
);

console.log("Similarity search results:");
console.log(resultDocuments[0].pageContent);
/*
  Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you're at it, pass the Disclose Act so Americans can know who is funding our elections.

  Tonight, I'd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.

  One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.

  And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation's top legal minds, who will continue Justice Breyer's legacy of excellence.
*/

// 将存储用作链的一部分
const model = new ChatOpenAI({ model: "gpt-3.5-turbo-1106" });
const questionAnsweringPrompt = ChatPromptTemplate.fromMessages([
  [
    "system",
    "Answer the user's questions based on the below context:\n\n{context}",
  ],
  ["human", "{input}"],
]);

const combineDocsChain = await createStuffDocumentsChain({
  llm: model,
  prompt: questionAnsweringPrompt,
});

const chain = await createRetrievalChain({
  retriever: store.asRetriever(),
  combineDocsChain,
});

const res = await chain.invoke({
  input: "What is the president's top priority regarding prices?",
});

console.log("Chain response:");
console.log(res.answer);
/*
  The president's top priority is getting prices under control.
*/

// 清理
await store.delete();

await store.close();

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