Developed an intelligent Retrieval-Augmented Generation (RAG) application that enables users to upload documents and interact with them through natural language questions. The system processes document content, generates vector embeddings, stores them in a vector database, and retrieves the most relevant context before generating responses with an LLM. The solution improves answer accuracy, reduces hallucinations, and provides an efficient way to extract knowledge from large document collections.