Building a Local RAG System for My Static Hugo Blog

Since my blog is built with Hugo, which generates static sites, I needed a creative approach to integrate RAG (Retrieval-Augmented Generation) functionality. I wanted visitors to be able to ask questions about my blog content and receive intelligent responses based on what I’ve written. Here’s how I approached it:

1. Creating a Backend for RAG

Static websites like Hugo don’t natively handle dynamic content generation, so I built a backend service to manage the RAG processes:

  • Language Choice: I went with C# to create an ASP.NET Core application to implement the RAG pipeline, as it’s my preferred language.
  • Backend Features:
    1. Accept user queries submitted from my blog.
    2. Retrieve relevant blog content from my indexed articles.
    3. Process the query with my local LLM using Semantic Kernel.
    4. Return the generated response to the Hugo frontend.

2. Connecting Hugo to the Backend

To maintain the static nature of my Hugo site while adding dynamic functionality:

  • Front-End Integration:
    • I modified my Hugo templates to include JavaScript (using Axios) to make requests to my RAG backend.
    • This approach keeps my site static while enabling dynamic query handling via API calls.
  • Data Flow:
    1. A visitor enters a question on my blog.
    2. JavaScript sends the query to my backend API.
    3. The backend retrieves relevant blog content and generates a response using RAG.
    4. My Hugo site displays the response dynamically without page reloading.

3. Setting Up Content Indexing

To make my blog content searchable for the RAG system:

  • Leveraging Hugo’s JSON Output:
    • I configured Hugo to generate a JSON file containing all my blog content.
    • This serves as the data source for my RAG implementation.
  • Vector Database Integration:
    • I chose Qdrant as my vector database for its performance and easy local hosting.
    • Each blog post is converted to vector embeddings and stored for semantic search.

4. Implementing the RAG Workflow

The core of my implementation involves these steps:

  • Vector Search:
    • When a query comes in, I convert it to an embedding and search my vector database.
    • The database returns the most semantically relevant blog posts.
  • Context Assembly:
    • I combine the user query with retrieved blog content to create context for the LLM.
  • Response Generation:
    • Using Semantic Kernel, I pass the combined context to my local LLM.
    • The LLM generates a response based on the blog content.

5. Security Considerations

Since I’m exposing an API endpoint, I implemented several security measures:

  • HTTPS Enforcement: All communication is encrypted using SSL/TLS.
  • Rate Limiting: Prevent abuse by limiting requests per user/IP.
  • Input Validation: Sanitize all incoming queries to prevent injection attacks.
  • CORS Policies: Restrict API access to only my domain.

6. Deployment Setup

My deployment architecture consists of:

  • Hugo Site: Deployed as static files on my web server.
  • RAG Backend: Running as a containerized service on my home server.
  • Vector Database: Self-hosted Qdrant instance for privacy and control.

Results and Future Improvements

This implementation gives my static site dynamic AI capabilities while maintaining:

  • Fast load times (Hugo’s static pages)
  • Low hosting costs (only the backend needs computing resources)
  • Privacy (everything runs locally on my infrastructure)

For future enhancements, I’m planning to:

  • Add LLM-based topic suggestion for new blog posts
  • Implement automatic tag organization
  • Create optimized title generation
  • Develop blog series structuring

The beauty of this approach is that it combines the simplicity and performance of a static site with the interactive capabilities typically found only in dynamic web applications.

Related Posts