RAG System

built a fully functional Retrieval-Augmented Generation (RAG) system to deeply understand how modern AI systems work in real-world production environments. The system allows users to upload documents, automatically processes them (chunking + embeddings), stores them in a vector database, and retrieves the most relevant context when answering user queries using a language model. Core Features Document upload and processing pipeline Text chunking and embedding generation Vector similarity search Context-aware LLM responses Background task handling Local model support (Ollama) Production-ready deployment setup Technical Stack & Architecture Backend: FastAPI for high-performance async APIs MVC architecture for clean code organization Background processing using Celery Local LLM integration via Ollama Data & Retrieval: Embeddings generation Vector database for similarity search RAG pipeline orchestration DevOps & Deployment: *Docker containerization CI/CD with GitHub Actions Production-ready deployment workflow Environment-based configuration