[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-72575":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":15,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":24,"hasPages":22,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":26,"readmeContent":27,"aiSummary":28,"trendingCount":16,"starSnapshotCount":16,"syncStatus":18,"lastSyncTime":29,"discoverSource":30},72575,"AI-reads-books-page-by-page","echohive42\u002FAI-reads-books-page-by-page","echohive42","AI reads books: Page-by-Page PDF Knowledge Extractor & Summarizer. script performs an intelligent page-by-page analysis of PDF books, methodically extracting knowledge points and generating progressive summaries at specified intervals","https:\u002F\u002Fwww.echohive.ai\u002F",null,"Python",2145,212,18,3,0,1,2,26,64.59,"MIT License",false,"main",true,[],"2026-06-12 04:01:06","# 📚 AI reads books: Page-by-Page PDF Knowledge Extractor & Summarizer\r\n\r\nThe `read_books.py` script performs an intelligent page-by-page analysis of PDF books, methodically extracting knowledge points and generating progressive summaries at specified intervals. It processes each page individually, allowing for detailed content understanding while maintaining the contextual flow of the book. Below is a detailed explanation of how the script works:\r\n\r\n### Features\r\n\r\n- 📚 Automated PDF book analysis and knowledge extraction\r\n- 🤖 AI-powered content understanding and summarization\r\n- 📊 Interval-based progress summaries\r\n- 💾 Persistent knowledge base storage\r\n- 📝 Markdown-formatted summaries\r\n- 🎨 Color-coded terminal output for better visibility\r\n- 🔄 Resume capability with existing knowledge base\r\n- ⚙️ Configurable analysis intervals and test modes\r\n- 🚫 Smart content filtering (skips TOC, index pages, etc.)\r\n- 📂 Organized directory structure for outputs\r\n\r\n## ❤️Join my AI Community & Get 400+ AI Projects & 1000x Cursor Course\r\n\r\nThis is one of 400+ fascinating projects in my collection! [Support me on Patreon](https:\u002F\u002Fwww.patreon.com\u002Fc\u002Fechohive42\u002Fmembership) to get:\r\n\r\n- 🎯 Access to 400+ AI projects (and growing daily!)\r\n  - Including advanced projects like [2 Agent Real-time voice template with turn taking](https:\u002F\u002Fwww.patreon.com\u002Fposts\u002F2-agent-real-you-118330397)\r\n- 📥 Full source code & detailed explanations\r\n- 📚 1000x Cursor Course\r\n- 🎓 Live coding sessions & AMAs\r\n- 💬 1-on-1 consultations (higher tiers)\r\n- 🎁 Exclusive discounts on AI tools & platforms (up to $180 value)\r\n\r\n## How to Use\r\n\r\n1. **Setup**\r\n   ```bash\r\n   # Clone the repository\r\n   git clone [repository-url]\r\n   cd [repository-name]\r\n\r\n   # Install requirements\r\n   pip install -r requirements.txt\r\n   ```\r\n\r\n2. **Configure**\r\n   - Place your PDF file in the project root directory\r\n   - Open `read_books.py` and update the `PDF_NAME` constant with your PDF filename\r\n   - (Optional) Adjust other constants like `ANALYSIS_INTERVAL` or `TEST_PAGES`\r\n\r\n3. **Run**\r\n   ```bash\r\n   python read_books.py\r\n   ```\r\n\r\n4. **Output**\r\n   The script will generate:\r\n   - `book_analysis\u002Fknowledge_bases\u002F`: JSON files containing extracted knowledge\r\n   - `book_analysis\u002Fsummaries\u002F`: Markdown files with interval and final summaries\r\n   - `book_analysis\u002Fpdfs\u002F`: Copy of your PDF file\r\n\r\n5. **Customization Options**\r\n   - Set `ANALYSIS_INTERVAL = None` to skip interval summaries\r\n   - Set `TEST_PAGES = None` to process entire book\r\n   - Adjust `MODEL` and `ANALYSIS_MODEL` for different AI models\r\n\r\n### Configuration Constants\r\n\r\n- `PDF_NAME`: The name of the PDF file to be analyzed.\r\n- `BASE_DIR`: The base directory for the analysis.\r\n- `PDF_DIR`: Directory where the PDF file is stored.\r\n- `KNOWLEDGE_DIR`: Directory where the knowledge base will be saved.\r\n- `SUMMARIES_DIR`: Directory where the summaries will be saved.\r\n- `PDF_PATH`: Full path to the PDF file.\r\n- `OUTPUT_PATH`: Path to the knowledge base JSON file.\r\n- `ANALYSIS_INTERVAL`: Number of pages after which an interval analysis is generated. Set to `None` to skip interval analyses.\r\n- `MODEL`: The model used for processing pages.\r\n- `ANALYSIS_MODEL`: The model used for generating analyses.\r\n- `TEST_PAGES`: Number of pages to process for testing. Set to `None` to process the entire book.\r\n\r\n### Classes and Functions\r\n\r\n#### `PageContent` Class\r\n\r\nA Pydantic model that represents the structure of the response from the OpenAI API for page content analysis. It has two fields:\r\n\r\n- `has_content`: A boolean indicating if the page has relevant content.\r\n- `knowledge`: A list of knowledge points extracted from the page.\r\n\r\n#### `load_or_create_knowledge_base() -> Dict[str, Any]`\r\n\r\nLoads the existing knowledge base from the JSON file if it exists. If not, it returns an empty dictionary.\r\n\r\n#### `save_knowledge_base(knowledge_base: list[str])`\r\n\r\nSaves the knowledge base to a JSON file. It prints a message indicating the number of items saved.\r\n\r\n#### `process_page(client: OpenAI, page_text: str, current_knowledge: list[str], page_num: int) -> list[str]`\r\n\r\nProcesses a single page of the PDF. It sends the page text to the OpenAI API for analysis and updates the knowledge base with the extracted knowledge points. It also saves the updated knowledge base to a JSON file.\r\n\r\n#### `load_existing_knowledge() -> list[str]`\r\n\r\nLoads the existing knowledge base from the JSON file if it exists. If not, it returns an empty list.\r\n\r\n#### `analyze_knowledge_base(client: OpenAI, knowledge_base: list[str]) -> str`\r\n\r\nGenerates a comprehensive summary of the entire knowledge base using the OpenAI API. It returns the summary in markdown format.\r\n\r\n#### `setup_directories()`\r\n\r\nSets up the necessary directories for the analysis. It clears any previously generated files and ensures the PDF file is in the correct location.\r\n\r\n#### `save_summary(summary: str, is_final: bool = False)`\r\n\r\nSaves the generated summary to a markdown file. It creates a file with a proper naming convention based on whether it is a final or interval summary.\r\n\r\n#### `print_instructions()`\r\n\r\nPrints instructions for using the script. It explains the configuration options and how to run the script.\r\n\r\n#### `main()`\r\n\r\nThe main function that orchestrates the entire process. It sets up directories, loads the knowledge base, processes each page of the PDF, generates interval and final summaries, and saves them.\r\n\r\n### How It Works\r\n\r\n1. **Setup**: The script sets up the necessary directories and ensures the PDF file is in the correct location.\r\n2. **Load Knowledge Base**: It loads the existing knowledge base if it exists.\r\n3. **Process Pages**: It processes each page of the PDF, extracting knowledge points and updating the knowledge base.\r\n4. **Generate Summaries**: It generates interval summaries based on the `ANALYSIS_INTERVAL` and a final summary after processing all pages.\r\n5. **Save Results**: It saves the knowledge base and summaries to their respective files.\r\n\r\n### Running the Script\r\n\r\n1. Place your PDF in the same directory as the script.\r\n2. Update the `PDF_NAME` constant with your PDF filename.\r\n3. Run the script. It will process the book, extract knowledge points, and generate summaries.\r\n\r\n### Example Usage\r\n","AI reads books: Page-by-Page PDF Knowledge Extractor & Summarizer 是一个用于逐页分析PDF书籍并提取知识点和生成摘要的工具。它利用AI技术进行内容理解和总结，支持在指定间隔自动生成阶段性摘要，并以Markdown格式输出。此外，该脚本还具备智能内容过滤（如跳过目录、索引页等）、持久化知识库存储及终端彩色输出等功能。适用于需要对长篇文档或书籍进行快速阅读理解与信息提取的场景，例如学术研究、资料整理等。项目使用Python编写，采用MIT许可证开放源代码。","2026-06-11 03:42:39","high_star"]