In an era characterized by the proliferation of digital documents, the need for efficient and intelligent tools for document analysis and comprehension has become paramount. This paper presents the development and implementation of a novel product that facilitates document understanding through automated keyword extraction, question answering, and question generation functionalities. The system allows users to upload PDF files, from which relevant keywords are extracted to provide a concise summary of the document's content. Additionally, users can pose inquiries about the uploaded document, to which the system responds with accurate answers derived from the document's context. Furthermore, the system employs advanced natural language processing techniques to generate probable questions based on the document's content, fostering deeper engagement and comprehension. The product's architecture integrates PDF parsing, keyword extraction algorithms, question answering models, and question generation techniques into a cohesive user interface, ensuring seamless

interaction and usability. Through comprehensive testing and evaluation, the system demonstrates robust performance in document analysis tasks, offering valuable insights into the document's content while empowering users with enhanced comprehension capabilities. This paper contributes to the advancement of document analysis tools and sets the stage for further research and development in the field of intelligent document processing.

Key Components used:

1. Flask Integration
2. Document Processing
3. Vector Embedding and FAISS Integration
4. Question answering pipeline
5. NLP Techniques.
6. Google Generative AI