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Azure Cosmos DB for NoSQL

Azure Cosmos DB for NoSQL 提供对使用灵活模式的项目进行查询的支持，并原生支持 JSON。现在它提供向量索引和搜索。此功能旨在处理高维向量，从而实现任何规模的高效且准确的向量搜索。您现在可以将向量直接存储在文档中以及您的数据旁边。数据库中的每个文档不仅可以包含传统的无模式数据，还可以包含作为文档其他属性的高维向量。

了解如何从此页面利用 Azure Cosmos DB for NoSQL 的向量搜索功能。如果您没有 Azure 帐户，可以创建一个免费帐户开始使用。

设置​

您首先需要安装 @langchain/azure-cosmosdb 包

提示

有关安装集成包的通用说明，请参阅此部分。

npm install @langchain/azure-cosmosdb @langchain/core

您还需要运行 Azure Cosmos DB for NoSQL 实例。您可以按照本指南在 Azure 门户上免费部署一个版本，无需任何费用。

一旦您的实例运行起来，请确保您拥有连接字符串。您可以在 Azure 门户的实例的“设置 / 密钥”部分找到它们。然后您需要设置以下环境变量

# Use connection string to authenticate
AZURE_COSMOSDB_NOSQL_CONNECTION_STRING=

# Use managed identity to authenticate
AZURE_COSMOSDB_NOSQL_ENDPOINT=

API 参考

    使用 Azure 托管身份​

    如果您正在使用 Azure 托管身份，您可以像这样配置凭据

    import { AzureCosmosDBNoSQLVectorStore } from "@langchain/azure-cosmosdb";
    import { OpenAIEmbeddings } from "@langchain/openai";

    // Create Azure Cosmos DB vector store
    const store = new AzureCosmosDBNoSQLVectorStore(new OpenAIEmbeddings(), {
      // Or use environment variable AZURE_COSMOSDB_NOSQL_ENDPOINT
      endpoint: "https://my-cosmosdb.documents.azure.com:443/",

      // Database and container must already exist
      databaseName: "my-database",
      containerName: "my-container",
    });

    API 参考

    信息

    当使用 Azure 托管身份和基于角色的访问控制时，您必须确保数据库和容器已预先创建。RBAC 不提供创建数据库和容器的权限。您可以在 Azure Cosmos DB 文档中获得有关权限模型的更多信息。

    使用示例​

    以下示例演示了如何在 Azure Cosmos DB for NoSQL 中索引文件中的文档，运行向量搜索查询，最后使用链来回答基于检索到的文档的自然语言问题。

    import { AzureCosmosDBNoSQLVectorStore } from "@langchain/azure-cosmosdb";
    import { ChatPromptTemplate } from "@langchain/core/prompts";
    import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";
    import { createStuffDocumentsChain } from "langchain/chains/combine_documents";
    import { createRetrievalChain } from "langchain/chains/retrieval";
    import { TextLoader } from "langchain/document_loaders/fs/text";
    import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";

    // Load documents from file
    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);

    // Create Azure Cosmos DB vector store
    const store = await AzureCosmosDBNoSQLVectorStore.fromDocuments(
      documents,
      new OpenAIEmbeddings(),
      {
        databaseName: "langchain",
        containerName: "documents",
      }
    );

    // Performs a similarity search
    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.
    */

    // Use the store as part of a chain
    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.
    */

    // Clean up
    await store.delete();

    API 参考

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