Build a Local RAG Chatbot in 30 Minutes
I uploaded a 40-page API spec to my local chatbot. I asked about the rate limit for a specific endpoint.
The bot answered in three seconds. It cited the exact section of the document.
The best part? It cost $0. Everything runs on my laptop using .NET 8, Ollama, and React. No data leaves my machine.
Most tutorials tell you to use OpenAI. That works for demos, but it fails for real work because:
- Privacy: You do not want sensitive company data in a cloud training pipeline.
- Cost: Sending large files to an LLM adds up quickly.
- Hallucinations: Models often forget the middle of long documents.
The solution is RAG (Retrieval-Augmented Generation). Instead of sending a whole PDF, you send only the relevant chunks.
Here is how I built this pipeline:
• Text Extraction: I used PdfPig to extract text from PDFs. • Chunking: I split text into 500-word pieces with a 50-word overlap. This overlap ensures the model does not miss answers that span across two chunks. • Embeddings: I used the nomic-embed-text model via Ollama to turn text into vectors. • Vector Store: I built a simple in-memory list using cosine similarity to find the best matches. • Generation: I used llama3.2 to answer questions based only on the found chunks.
Three lessons I learned during the build:
- Use a long timeout: Local models take time to load into memory. I set my HttpClient timeout to 5 minutes to avoid errors during cold starts.
- Prompt engineering is key: I tell the model to answer using ONLY the provided context. This reduces hallucinations by 80%.
- Chunking matters: I chunk by words rather than characters. This keeps the size predictable and within the model limits.
You can run this entire stack locally. It is a professional-grade tool on a zero-dollar budget.
Check out the full build log and code here:
Optional learning community: https://t.me/GyaanSetuAi