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Azure Cosmos DB for NoSQL

Azure Cosmos DB for NoSQL provides support for querying items with flexible schemas and native support for JSON. It now offers vector indexing and search. This feature is designed to handle high-dimensional vectors, enabling efficient and accurate vector search at any scale. You can now store vectors directly in the documents alongside your data. Each document in your database can contain not only traditional schema-free data, but also high-dimensional vectors as other properties of the documents.

Learn how to leverage the vector search capabilities of Azure Cosmos DB for NoSQL from this page. If you don't have an Azure account, you can create a free account to get started.

Setup​

You'll first need to install the @langchain/azure-cosmosdb package:

tip

See this section for general instructions on installing integration packages.

npm install @langchain/azure-cosmosdb

You'll also need to have an Azure Cosmos DB for NoSQL instance running. You can deploy a free version on Azure Portal without any cost, following this guide.

Once you have your instance running, make sure you have the connection string. You can find them in the Azure Portal, under the "Settings / Keys" section of your instance. Then you need to set the following environment variables:

# Use connection string to authenticate
AZURE_COSMOSDB_NOSQL_CONNECTION_STRING=

# Use managed identity to authenticate
AZURE_COSMOSDB_NOSQL_ENDPOINT=

API Reference:

    Using Azure Managed Identity​

    If you're using Azure Managed Identity, you can configure the credentials like this:

    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 Reference:

    info

    When using Azure Managed Identity and role-based access control, you must ensure that the database and container have been created beforehand. RBAC does not provide permissions to create databases and containers. You can get more information about the permission model in the Azure Cosmos DB documentation.

    Usage example​

    Below is an example that indexes documents from a file in Azure Cosmos DB for NoSQL, runs a vector search query, and finally uses a chain to answer a question in natural language based on the retrieved documents.

    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 Reference:

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