📚🤖 I'm excited to share my latest project—a RAG-based question-answering system designed using Streamlit and the GROQ API! This system leverages hierarchical tree-based indexing to navigate through multiple topics within a selected book, delivering precise and contextually relevant responses to user queries.I have here used the "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition by Aurélien Géron" book to create the hierarchical tree of topics.

• Organizes content into a hierarchical structure, making it easy to traverse chapters, sections, and topics.
• Combines the power of traditional retrieval methods like Rank-BM25 with modern transformer-based models for enhanced accuracy.
• Interacts seamlessly through a user-friendly Streamlit interface.

• PYPdf2: For PDF parsing.
• GROQ API: Core to the retrieval process.
• Streamlit: For building the interactive UI.
• Rank-BM25: To ensure relevant content retrieval.
• Transformers & PyTorch: For powerful natural language processing.
• NLTK & Gensim: For text processing and topic modeling.
• Numpy: For efficient numerical operations.

Check out the live demo here: BookBot on Streamlit 🌐