AI Document Assistant (RAG Chatbot)

Problem:
Organizations struggle to extract useful insights from large volumes of unstructured documents.

Solution:
Built a Retrieval-Augmented Generation (RAG) system that enables users to query documents using natural language.

Impact:

• Enabled intelligent document search and summarization
• Improved efficiency in accessing information from large datasets
• Designed scalable pipeline
• Supports real-time document querying

Tech Stack:
Python, LangChain, OpenAI API, FAISS, Streamlit

Key Highlights:

• Designed end-to-end RAG pipeline (chunking → embeddings → retrieval → LLM)
• Implemented semantic search using vector database (FAISS)
• Applied prompt engineering for accurate, grounded responses
• Built interactive UI for real-time document Q&A