Implementing RAG Application with Dynamic Index Refresh on Azure Storage Blob Changes

Chandresh Maniya 0 Reputation points
2024-06-06T13:46:26.6133333+00:00

Hello,

I am working on creating a Retrieval-Augmented Generation (RAG) application and I need assistance with dynamically refreshing the index based on changes in Azure Storage Blob. Specifically, I want to trigger reindexing when new documents are added or existing documents are deleted from the blob storage.

Scenario:

  • The RAG application initially indexes documents stored in Azure Storage Blob.
  • When new documents are added or existing documents are deleted, the index should automatically refresh to include/exclude these documents.
  • The updated index should be used for future queries to ensure the latest documents are considered.

Requirements:

  1. Detecting Changes: How can I detect changes (additions or deletions) in Azure Storage Blob?
  2. Triggering Reindexing: What is the best way to trigger the reindexing process upon detecting changes?
  3. Sample Code: Any example code or guidance on implementing this would be highly appreciated.

Current Progress:

I am familiar with setting up a RAG application and indexing documents, but I am unsure about the best approach to monitor and handle changes in Azure Storage Blob to dynamically refresh the index.

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  1. brtrach-MSFT 15,701 Reputation points Microsoft Employee
    2024-06-13T03:05:31.2166667+00:00

    @Chandresh Maniya To dynamically refresh the index based on changes in Azure Storage Blob, you can use Azure Functions and Azure Blob Storage triggers. Here are the steps you can follow:

    Create an Azure Function with a Blob Storage trigger. This function will be triggered whenever a new blob is added or deleted from the storage account.

    1. In the function code, use the Azure AI Search SDK to update the index. You can use the SDK to add or delete documents from the index based on the changes in the blob storage.
    2. To ensure that the index is updated in real-time, you can use the Azure AI Search indexer. The indexer can be configured to run on a schedule or to be triggered by changes in the data source. You can use the indexer to automatically update the index whenever there are changes in the blob storage.

    Here is some sample code to get you started:

    using Microsoft.Azure.WebJobs;
using Microsoft.Extensions.Logging;
using Microsoft.Azure.Search;
using Microsoft.Azure.Search.Models;

public static void Run(
    [BlobTrigger("mycontainer/{name}", Connection = "AzureWebJobsStorage")] Stream myBlob, 
    string name, 
    ILogger log)
{
    // Get the search service client
    SearchServiceClient searchClient = new SearchServiceClient("<search-service-name>", new SearchCredentials("<api-key>"));

    // Get the index client
    SearchIndexClient indexClient = searchClient.Indexes.GetClient("<index-name>");

    // Create a new document
    var document = new Document();
    document.Add("id", name);
    document.Add("content", myBlob);

    // Add the document to the index
    indexClient.Documents.Index(new IndexBatch(new[] { document }));
}

    In this code, the function is triggered whenever a new blob is added to the "mycontainer" container in the storage account. The function then creates a new document and adds it to the index using the Azure AI Search SDK.

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