BookMind is a web application that allows users to explore character relationships and storylines in books using AI-powered visualizations. The application provides interactive mind maps, AI chatbots for deep questions, book summaries, and community contributions.
- Interactive Mind Maps: Visualize relationships between characters and plot elements.
- AI Chatbot: Ask deep questions about the book and get insightful answers.
- Book Summaries: Get concise overviews of plots and themes.
- Community Contributions: Add and refine maps with fellow book lovers.
- Node.js
- Python >= 3.10
- LlamaStack server running locally
- Environment variables:
- LLAMA_STACK_PORT
- INFERENCE_MODEL
- REACT_APP_GOOGLE_BOOKS_API_KEY
- Install dependencies:
pip install -r server/requirements.txt
- Install and run our forked
llama-stackdocker
git clone https://github.com/seyeong-han/llama-stack.git
cd llama-stack
pip install -e .
# Get your API_KEY from https://cloud.sambanova.ai/apis
export SSAMBANOVA_API_KEY=YOUR_API
llama stack run --env INFERENCE_MODEL=meta-llama/Llama-3.1-8B-Instruct sambanova
- Run the server:
python server/server.py
- Install dependencies:
npm install
- Run the application:
npm start
- Initialize Memory: Upload your book or choose from the library to initialize memory.
- AI Analysis: The AI analyzes the book and generates a mind map.
- Explore Insights: Explore relationships, themes, and Q&A insights.
1️⃣ Implemented SambaNova Systems Inference API: We contributed to the open-source Llama-stack by integrating the inference API and built a custom Docker container for the llama3.2-3B-instruct model.
2️⃣ RAG with FAISS: We leveraged FAISS in Llama-stack for Retrieval-Augmented Generation, enabling real-time responses to character relationship questions.
3️⃣ Multi-Hop Reasoning: Our system performs sequential inference—first extracting characters and relationships, then generating graphized mind map data in JSON for visual storytelling.