Built an Internal Document RAG Chatbot that enables users to upload documents and ask questions based on their contents. The application processes documents, splits text into chunks, generates embeddings, retrieves relevant information, and uses a Large Language Model (LLM) to generate context-grounded answers with source references.

The project focuses on document-based question answering, relevant information retrieval, and reducing unsupported answers through context-based generation.