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Book Mind is a web application that allows users to explore character relationships and storylines in books using AI-powered visualizations. This leverages Llama 4 Maverick's impressive 1M token context windows to process entire books at once, enabling comprehensive analysis of complex narratives and character relationships across lengthy texts.
Model | Meta Llama4 Maverick | Meta Llama4 Scout | OpenAI GPT-4.5 | Claude Sonnet 3.7 |
---|---|---|---|---|
Context Window | 1M tokens | 10M tokens | 128K tokens | 1K tokens |
Because of the long context length, Book Mind can process entire books at once, providing a comprehensive understanding of complex narratives and character relationships.
We implemented a step-by-step approach to ensure the model outputs' reliability.
Character Identification: Identify all characters in the book and summarize their roles.
You are a highly detailed literary analyst AI. Your sole mission is to meticulously extract comprehensive information about characters and the *nuances* of their relationships from the provided text segment. This data will be used later to build a relationship graph.
Character Relationships: Determine the relationships between characters.
You are an expert data architect AI specializing in transforming literary analysis into structured graph data. Your task is to synthesize character and relationship information into a specific JSON format containing nodes and links, including a title and summary.
JSON Format: Output the results in a JSON format for easy parsing and visualization.
You are an extremely precise and strict JSON extractor.
Extract only the complete JSON object from the input. Get the last one if there are multiple.
We also implemented a chat interface to interact with the book. Users can ask questions about the book's characters, plot, and relationships. The model will respond with a concise answer based on the book's content and the relationships between characters.
You are an expert search AI designed to help users find detailed information about character relationships from a book. Your task is to assist users in querying the relationship data extracted from the book.
To communicate with the server/server.py, we use React.js
and axios
.
npm install
npm start
We use Flask
to serve the model's responses and vllm
to run the Llama 4 Maverick model.
Install dependencies:
cd server
pip install -r requirements.txt
Run the server:
python server.py