My Story: Why I Built a Local RAG System
A few months ago, I found myself frustrated with the limitations and privacy concerns of cloud-based AI tools. I wanted to experiment with Retrieval-Augmented Generation (RAG) on my own terms—locally, with full control over my data and the ability to tinker under the hood. As a developer who loves open source, C#, and learning by doing, I decided to build my own local RAG system from scratch.
The process was both challenging and rewarding. I learned a lot about vector databases, embedding models, and how to connect everything together in C#. There were moments of confusion (and a few late nights), but seeing my own documents being queried and summarized by a local LLM was incredibly satisfying. If you’re curious about RAG or want to run your own AI-powered search and Q&A system at home, this guide is for you.
What is a RAG System?
Retrieval-Augmented Generation (RAG) combines a language model (like Llama or Mistral) with a retrieval system (like a vector database) to answer questions using your own documents. Instead of relying solely on the model’s training data, RAG fetches relevant information from your data and feeds it to the model for more accurate, up-to-date answers.
Step 1: Choose Your Local LLM and Vector Database
- LLM: I used Ollama to run models like Llama 2 and Mistral locally. It’s easy to set up and works well on consumer hardware.
- Vector Database: I picked Qdrant because it’s extremely easy to deploy as a Docker container and fits perfectly into my Docker server setup. I also appreciate Qdrant’s visual data representation, which makes it easy to inspect and debug your collections.
Step 2: Prepare Your Documents
Gather the files you want to query—PDFs, Markdown, text files, or even web pages. I started with my own notes and some open-source documentation. Clean up the text as much as possible for better results.
Step 3: Generate Embeddings
Use an embedding model (like all-MiniLM-L6-v2 from Hugging Face) to convert your documents into vectors. I wrote all the code for document chunking and embedding generation in C#, leveraging .NET libraries and APIs. The embeddings are then stored in Qdrant using its REST API or gRPC interface. There are C# clients available for Qdrant, which makes integration straightforward.
Step 4: Connect the Pieces
- When you ask a question, the system generates an embedding for your query.
- It searches Qdrant for the most relevant document chunks.
- These chunks are passed to the LLM as context, and the model generates an answer.
I used a simple ASP.NET Core backend to glue everything together, but you can use other .NET frameworks or even run everything in a console app.
Step 5: Ask Questions and Iterate
Once everything was running, I started asking questions about my own notes and documents. The first answers weren’t perfect, but with some tuning (like adjusting chunk sizes and retrieval parameters), the results improved quickly. The best part? Everything ran locally—no internet required, and my data stayed private.
Lessons Learned and Tips
- Start simple: Don’t overcomplicate the setup. Get a basic pipeline working, then iterate.
- Hardware matters: Running LLMs locally is resource-intensive. A decent GPU helps, but you can start with CPU-only models.
- Qdrant is developer-friendly: The Docker deployment and web UI make it easy to manage and visualize your data.
- Open source is your friend: The community has built amazing tools—don’t be afraid to use them or ask for help.
- Document your process: I kept notes on what worked and what didn’t, which made troubleshooting much easier.
Conclusion
Building a local RAG system with C#, Qdrant, and Ollama was one of the most rewarding tech projects I’ve tackled. It gave me a deeper understanding of how modern AI systems work and the confidence to experiment further. If you’re interested in privacy, customization, or just learning something new, I highly recommend giving it a try. Feel free to reach out if you have questions or want to share your own journey!