📚 AI reads books: Page-by-Page PDF Knowledge Extractor & Summarizer
What is the project about?
The project is about intelligent, page-by-page analysis of PDF books. It extracts key knowledge points and generates summaries at specified intervals.
What problem does it solve?
It automates the process of reading, understanding, and summarizing PDF books, saving time and effort for users who need to extract key information from lengthy documents. It also maintains the contextual flow of the book.
What are the features of the project?
- 📚 Automated PDF book analysis and knowledge extraction.
- 🤖 AI-powered content understanding and summarization.
- 📊 Interval-based progress summaries.
- 💾 Persistent knowledge base storage (JSON format).
- 📝 Markdown-formatted summaries.
- 🎨 Color-coded terminal output.
- 🔄 Resume capability with existing knowledge base.
- ⚙️ Configurable analysis intervals and test modes.
- 🚫 Smart content filtering (skips TOC, index, etc.).
- 📂 Organized directory structure for outputs.
What are the technologies used in the project?
- Python
- OpenAI API (for content understanding and summarization)
- Pydantic (for data modeling)
- PDF processing libraries (implied, but not explicitly named)
What are the benefits of the project?
- Efficiently extracts key information from PDF books.
- Provides progressive summaries, allowing users to understand the content at different stages.
- Saves time and effort compared to manual reading and summarization.
- Maintains context by processing pages sequentially.
- Offers customizable options for different analysis needs.
What are the use cases of the project?
- Research: Quickly extracting key information from academic papers or books.
- Education: Summarizing textbooks or study materials.
- Content creation: Gathering information for articles, reports, or presentations.
- Personal knowledge management: Building a knowledge base from books read.
- Any situation where efficient extraction of information from PDF documents is needed.
