Retrieval-Augmented Generation (RAG) is revolutionizing how applications interact with knowledge bases. In this project, I developed a comprehensive RAG system that spans 10 specialized domains, combining semantic search with LLM-powered response generation. This approach enables intelligent knowledge retrieval and synthesis across diverse knowledge areas, providing users with precise, contextual answers backed by reliable sources.
The architecture leverages LlamaIndex to orchestrate the RAG pipeline, GPT-4 for advanced language understanding, and Pinecone as a vector database for high-performance semantic search. By utilizing embeddings and vector similarity, the system achieves an 80% reduction in retrieval time compared to traditional search methods. The frontend, built with Next.js 14, provides a modern, responsive interface with JWT authentication, conversation history management, and real-time updates. The backend, powered by FastAPI, handles document processing, vector indexing, and LLM orchestration with optimal efficiency.
Deployment was handled through Docker containerization, GitHub Actions CI/CD pipelines, and Nginx reverse proxy configuration. To ensure reliability and performance, I implemented Prometheus for metrics collection and Grafana for real-time monitoring dashboards. This enterprise-grade setup allows for seamless scaling, automatic updates, and comprehensive system observability. The entire pipeline demonstrates how modern AI/ML systems can be built with production-grade infrastructure, combining cutting-edge language models with robust DevOps practices to deliver reliable, scalable AI solutions.