BookMind

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Draw book mind maps to see the relationship between characters easily :)

BookMind 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# BookMind

[![thumbnail](https://github.com/user-attachments/assets/11c6f1f3-59db-4638-9b1a-68f1d25efec4)](https://youtu.be/DL4-DswxfEM)

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.

## Features

- 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.

## Prerequisites

- Node.js
- Python >= 3.10
- LlamaStack server running locally
- Environment variables:
  - LLAMA_STACK_PORT
  - INFERENCE_MODEL
  - REACT_APP_GOOGLE_BOOKS_API_KEY

## Getting Started

### Backend Setup

1. Install dependencies:

```
pip install -r server/requirements.txt
```

2. Install and run our forked `llama-stack` docker

```
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
```

3. Run the server:

```
python server/server.py
```

### Frontend Setup

1. Install dependencies:

```
npm install
```

2. Run the application:

```
npm start
```

## Usage

1. Initialize Memory: Upload your book or choose from the library to initialize memory.
2. AI Analysis: The AI analyzes the book and generates a mind map.
3. Explore Insights: Explore relationships, themes, and Q&A insights.

## What did we use Llama-stack in BookMind?

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.

BookMind

BookMind 최신버젼 다운로드

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