Google Vertex RAG Engine with C# .Net

What is RAG?

Ever asked an AI chatbot about your company's products or policies only to get a generic answer or worse - confidently incorrect information? This highlights the core limitation of standard LLMs: they know general information but nothing specific about your organization.

Retrieval Augmented Generation (RAG) solves this problem elegantly:

Without RAG:

User: "What's our refund policy for premium customers?"

AI: Provides generic refund information or makes up details

With RAG:

User: "What's our refund policy for premium customers?"

AI: "According to our current policy (updated last month), premium customers can request refunds within 60 days of purchase with no questions asked. Standard customers have a 30-day window."

How RAG Works

RAG enhances AI by connecting it to your specific knowledge:

1. When a question arrives, RAG searches your documents for relevant information
2. It provides this context to the AI along with the original question
3. The AI generates a response grounded in your actual data

It's like giving your AI assistant access to your company's documentation before it answers questions.

Why RAG Matters for Developers

• Accuracy: Responses based on your actual data, not guesswork
• Freshness: Add new documents anytime without retraining models
• Privacy: Your data stays separate from the model
• Specificity: Handle domain-specific questions with confidence

For practical AI applications in business settings, RAG isn't just nice to have—it's essential for delivering reliable, specific answers.

RAG Implementation Components

Building a RAG application typically involves managing several components:

1. Document processing and chunking
2. Embedding generation
3. Vector database management
4. Retrieval mechanisms
5. Context integration with LLMs

Google Vertex RAG Engine integrates these components into a managed service, providing a unified approach to RAG implementation.

Key Features of Vertex RAG Engine

Integrated Architecture

Vertex RAG provides an end-to-end solution that integrates:

• Document processing capabilities
• Embedding generation using Google's models
• Vector storage management
• Retrieval mechanisms
• Integration with generative models

Infrastructure Management

The managed service approach helps reduce infrastructure overhead by handling:

• Vector database scaling
• Embedding pipeline execution
• Retrieval algorithm optimization
• Component integration

Data Source Connectors

Vertex RAG supports several data sources:

• Google Cloud Storage
• Slack
• Jira
• SharePoint
• Direct uploads

Easy Implementation with Google_GenerativeAI SDK

Let's walk through the complete process of implementing RAG with Google Vertex RAG Engine using C#.

1. Initialize Vertex AI

The first step is to initialize the Vertex AI client with appropriate authentication:

// Initialize Vertex AI with authentication
var vertexAi = new VertexAI(projectId, region,
    authenticator:
    new GoogleServiceAccountAuthenticator("path/to/your/service/account.json")
    // or another authenticator that suits your credentials
);

You'll need to ensure your credentials have the necessary IAM permissions:

• Vertex AI RAG Data Service Agent
• Vertex AI User
• Secret Manager Secret Accessor
• AI Platform Developer

2. Create a RAG Manager

Next, create a RAG Manager to handle corpus operations:

var ragManager = vertexAi.CreateRagManager();

3. Create Your Knowledge Base (Corpus)

Now you can create a corpus that will serve as your knowledge base:

var corpus = await ragManager.CreateCorpusAsync("My New Corpus", "My description");

You can also specify a custom vector database if needed:

// Example with Pinecone
var corpus = await ragManager.CreateCorpusAsync(
    "My New Corpus", 
    "My description",
    pineconeConfig: new RagVectorDbConfigPinecone(...),
    apiKeyResourceName: "projects/my-project/secrets/pinecone-key/versions/1"
);

4. Import Data into the Corpus

Import your data from a specified source:

// Import from Google Cloud Storage
var fileSource = new GcsSource() 
{ 
    Uris = { "gs://your-bucket/your-document.pdf" }
};
await ragManager.ImportFilesAsync(corpus.Name, fileSource);

// Or upload a local file directly
await ragManager.UploadLocalFileAsync(corpus.Name, "path/to/local/file.pdf");

The SDK supports multiple data sources:

• Google Cloud Storage
• Slack
• Jira
• SharePoint
• Direct file uploads

5. Create a Generative Model with RAG Configuration

Connect a generative model to your knowledge base:

var model = vertexAi.CreateGenerativeModel(
    VertexAIModels.Gemini.Gemini2Flash, 
    corpusIdForRag: corpus.Name
);

6. Generate Content

Now you can generate responses grounded in your knowledge base:

// For a one-time response
var result = await model.GenerateContentAsync("How do I reset my password?");
Console.WriteLine(result.Text);

// Or create a conversational chat
var chat = model.StartChat();
var response = await chat.GenerateContentAsync("Tell me about our product features.");

The Fastest Path from Idea to Working Application

With the combination of Google Vertex RAG Engine and the Google_GenerativeAI SDK, you can go from concept to working prototype in hours, not weeks:

1. Morning: Set up your Google Cloud project and install the SDK
2. Midday: Create your corpus and import initial documents
3. Afternoon: Connect to models and test your first queries
4. Next day: Refine and expand your application

Compare this to traditional approaches that might require:

• Weeks of architecture discussions
• Days of infrastructure setup
• Complex integration between multiple services
• Ongoing maintenance of various components

Real-World Implementation: Documentation to Q&A System in Minutes

Here's how you can build a complete RAG application that scrapes any documentation website, creates a knowledge base, and provides an instant question-answering system. The full source code is available in the Google_GenerativeAI samples repository.

What This Demo Does

This demo automates the entire RAG application pipeline:

1. It scrapes content from any documentation website you specify
2. Creates a knowledge base (corpus) from that content
3. Connects a Gemini model to this knowledge base
4. Provides an interactive chat interface where you can ask questions about the documentation

All of this happens with minimal code, thanks to the power of Google Vertex RAG Engine and the simplicity of the Google_GenerativeAI SDK.

Key Code Components

1. Initialization

Setting up the Vertex AI client and RAG manager:

public VertexRagDemo(string projectId, string region, string serviceAccountFilePath)
{
    _projectId = projectId;
    _region = region;
    var authenticator = new GoogleServiceAccountAuthenticator(serviceAccountFilePath);
    _vertexAi = new VertexAI(projectId, region, authenticator: authenticator);
    _ragManager = _vertexAi.CreateRagManager();
}

2. Creating or Retrieving a Corpus

The demo intelligently checks if a corpus already exists before creating a new one:

private async Task<RagCorpus> GetOrCreateCorpus(string corpusName, string corpusDescription)
{
    try
    {
        var existingCorpus = await _ragManager.GetCorpusAsync(corpusName);
        if (existingCorpus != null)
        {
            Console.WriteLine($"Corpus '{corpusName}' already exists.");
            this._corpus = existingCorpus;
            return existingCorpus;
        }

        return existingCorpus;
    }
    catch (Exception ex)
    {
        var newCorpus = await _ragManager.CreateCorpusAsync(corpusName, corpusDescription);
        this._corpus = newCorpus;
        Console.WriteLine($"Corpus '{_corpus.Name}' created.");

        return newCorpus;
    }
}

3. Web Scraping and Content Import

The demo uses a parallel web crawler to efficiently fetch content from documentation sites:

private async Task ScrapeAndImportData(string url)
{
    Console.WriteLine("Crawling documentation...");
    var crawler = new ParallelWebCrawler(url);
    var textList = await crawler.CrawlUrlsParallel(url);

    // Process documents in parallel for faster importing
    await Parallel.ForEachAsync(textList, new ParallelOptions() { MaxDegreeOfParallelism = 50 }, 
        async (text, ct) =>
        {
            try
            {
                // Create temp file and upload to corpus
                var tmp = Path.GetTempFileName() + ".html";
                await File.WriteAllTextAsync(tmp, text, ct);
                await _ragManager.UploadLocalFileAsync(_corpus.Name, tmp, cancellationToken: ct);
            }
            catch(Exception ex)
            {
                Console.WriteLine($"Error importing file: {ex.Message}");
            }
        });

    Console.WriteLine("Data import completed.");
}

4. Setting Up the Generative Model with RAG

Connecting a Gemini model to the knowledge base with a single line of code:

// Create generative model with RAG enabled
_model = _vertexAi.CreateGenerativeModel(
    VertexAIModels.Gemini.Gemini2Flash, 
    corpusIdForRag: _corpus.Name
);

5. Interactive Chat Interface

A simple but effective interactive chat interface for testing the knowledge base:

private async Task StartQaChat()
{
    var chat = _model.StartChat();

    while (true)
    {
        Console.Write("Ask a question (or 'exit'): ");
        string question = Console.ReadLine();

        if (question.ToLower() == "exit")
        {
            break;
        }

        try
        {
            var result = await chat.GenerateContentAsync(question);
            Console.WriteLine($"Answer: {result.Text}");
        }
        catch (Exception ex)
        {
            Console.WriteLine($"Error: {ex.Message}");
        }
    }
}

How to Use the Demo

To use this demo, simply create an instance and start it:

// Initialize the demo with your Google Cloud project details
var demo = new VertexRagDemo(
    projectId: "your-project-id",
    region: "us-central1",
    serviceAccountFilePath: "path/to/service-account.json"
);

// Start the demo with a documentation URL to scrape
await demo.StartDemo(
    documentationsUrl: "https://your-documentation-site.com",
    corpusName: "ProductDocumentation",
    corpusDescription: "Knowledge base for product documentation"
);

For the complete implementation, including the ParallelWebCrawler class and additional utilities, visit the Google_GenerativeAI samples repository on GitHub.

Practical Applications

This demo can be immediately adapted for various real-world scenarios:

1. Technical Documentation Assistant: Point it at your product documentation to create an instant support chatbot
2. Policy Guide: Use it with company policies to help employees navigate complex procedures
3. API Explorer: Target your API documentation to help developers understand your services
4. Training Materials: Convert training websites into interactive Q&A systems

The best part? Google Vertex RAG Engine handles all the complex vector operations, semantic search, and retrieval optimization behind the scenes, while the Google_GenerativeAI SDK provides a clean, intuitive interface that makes implementation straightforward for C# developers.

Start Building Today

If you want to build a RAG application that provides accurate, context-aware responses based on your specific knowledge, Google Vertex RAG Engine with the Google_GenerativeAI SDK is simply the easiest path forward.

No other approach offers the same combination of:

• Minimal development effort
• End-to-end integration
• Managed infrastructure
• Simple, intuitive API
• Rapid time to implementation

The next time you consider building a RAG application, remember that Google Vertex RAG Engine and the Google_GenerativeAI SDK for C# provide the simplest path from idea to working solution.

Side Note: I used AI to convert my ideas into presentable blog post. This blog post is created for education purpose.