RAG Web Application

A sophisticated web application leveraging Retrieval-Augmented Generation (RAG) to enhance user exploration of technical documentation knowledge bases.

Key Achievements

• Advanced LLM integration: Implemented cutting-edge language models for query refinement (subquery generation), cross-encoder scoring for document relevance ranking, and contextually informed answer generation.
• Chroma vector database: Utilized ChromaDB as a high-performance vector database to store and retrieve document embeddings, enabling efficient semantic search within the knowledge base.
• PDF ingestion pipeline: Designed a robust process to ingest and preprocess technical documentation in PDF format, transforming unstructured data into valuable insights.
• Full-stack development expertise: Developed the entire application stack, using React for the frontend, Flask for the backend, PostgreSQL for logging, and Docker for seamless containerization.
• Customizable logging: Built a Python-based logger to capture errors and user interactions, providing valuable data for analysis and improvement.