Building Enterprise RAG: .NET, Semantic Kernel, and Weaviate

As LLMs evolve toward GPT-5.4, the challenge for .NET developers isn't just "connecting to an AI"—it's building a reliable, secure, and scalable Retrieval-Augmented Generation (RAG) pipeline.

In this post, I’ll walk through a demo implementation using Semantic Kernel to orchestrate the flow, Weaviate as the vector memory, and a critical layer of Data Sanitization.

The Architecture

A production-ready RAG application consists of three main stages:

1. Sanitization: Cleaning raw data to remove noise and protect PII.

2. Ingestion: Embedding the clean data and storing it in Weaviate.

3. Orchestration: Using Semantic Kernel to retrieve context and generate answers via GPT-5.4.

---

1. The Gateway: Data Sanitization

Before data ever touches a vector database, it must be "sanitized." This prevents "Garbage In, Garbage Out" and ensures compliance.

Why Sanitize?

• Lower Costs: Removing HTML/boilerplate reduces token usage.

• Better Accuracy: Cleaner text leads to higher-quality embeddings.

• Security: Redacting PII ensures sensitive data isn't leaked to the LLM.

C#

// Simple Sanitization Utility
public string Sanitize(string rawText) {
    var clean = Regex.Replace(rawText, "<.*?>", string.Empty); // Remove HTML
    clean = Regex.Replace(clean, @"\s+", " "); // Normalize whitespace
    return clean.Trim();
}

2. The Memory: Weaviate + Semantic Kernel

Weaviate provides a highly scalable vector store that integrates seamlessly with .NET via the Semantic Kernel connectors. By using AddWeaviateVectorStore, we can perform sub-second semantic searches across millions of documents.

C#

var kernel = Kernel.CreateBuilder()
    .AddOpenAIChatCompletion("gpt-5.4", apiKey)
    .AddWeaviateVectorStore(endpoint, apiKey)
    .Build();

3. The RAG Flow in Action

The magic happens when Semantic Kernel acts as the "brain," retrieving only the most relevant snippets from Weaviate to ground the GPT-5.4 response in your private data.

Key Benefits of this Stack:

• Type Safety: Leverage C#’s strong typing for AI plugins.

• Dependency Injection: Seamlessly integrate AI services into your existing ASP.NET Core apps.

• Performance: Weaviate’s efficiency paired with the native speed of .NET 8/9.

---

Source Code: https://github.com/LeelaPrasadG/RAG_Weaviate_SemanticKernel_CSharp