Retrieval‑Augmented Generation (RAG) has quickly become one of the most practical ways to build AI systems that work with real‑world data instead of relying only on a model’s training knowledge.
Most RAG examples today are written in Python and often depend on frameworks like LangChain. While powerful, this leaves a gap for developers working primarily in .NET and C#.
In this post, I’ll walk through a .NET Console application I built that implements a complete RAG pipeline using GitHub Models for embeddings and chat completion.
What This Project Does?
At a high level, this application allows you to:
- Provide a website URL
- Automatically scrape and clean the page content
- Convert the content into embeddings
- Store embeddings in an in‑memory vector store
- Ask questions about the website
- Get accurate, context‑aware answers generated by an AI model
All of this runs inside a .NET Console App, making it easy to debug, extend, and later expose as an API.
