RAG Chatbot System
Production AI System for Context-Aware Responses
Role: Technical Product Owner & Developer
Type: Personal Project / Production System
Status: Live & Serving Users
The Challenge
Traditional chatbots and even modern LLMs struggle to provide accurate, context-aware responses about proprietary or specialized data. Users asking questions about specific content receive generic answers that lack the depth and accuracy needed for meaningful engagement.
Key Problem: How do you enable an AI to provide accurate, contextual answers about your own data without the hallucinations and generic responses typical of base LLM models?
The Solution
I designed and built a complete Retrieval-Augmented Generation (RAG) system from the ground up, implementing the full technology stack:
Technology Architecture
Backend
- FastAPI: High-performance Python API framework
- Ollama: Local LLM inference engine
- bge-m3: State-of-the-art embedding model
- Qdrant: Vector database for semantic search
Infrastructure
- Docker: Containerized deployment
- Docker Compose: Multi-service orchestration
- Nginx: Reverse proxy & SSL termination
- WordPress: Frontend integration
How It Works
- Document Ingestion: Content is chunked and processed through the embedding model
- Vector Storage: Embeddings are stored in Qdrant for efficient similarity search
- Query Processing: User questions are embedded and matched against stored vectors
- Context Retrieval: Most relevant document chunks are retrieved
- Response Generation: LLM generates answers using retrieved context
The Results
✅ Production Ready
Live system serving real users with context-aware responses
🎯 Accurate Answers
Responses grounded in actual proprietary content, reducing hallucinations
🔧 Full Stack
Complete end-to-end implementation demonstrating deep technical competency
Key Takeaways
This project demonstrates:
- Practical AI Implementation: Not just theory—a working production system
- Full-Stack Capability: From infrastructure to API to frontend integration
- Modern Tech Stack: Current, industry-relevant technologies
- Product Thinking: Solving real user problems with AI capabilities