Scriptorium Prototype

Overview

This is a minimal prototype of Scriptorium, a document processing and analysis tool. The current implementation demonstrates the basic framework and workflow, including document processing, placeholders for LLM integration, and a simple CLI interface.

Project Structure

scriptorium/
├── src/
│   ├── core/
│   │   ├── __init__.py
│   │   ├── document_processor.py
│   │   └── llm_integration.py
│   ├── ui/
│   │   ├── __init__.py
│   │   └── cli.py
│   └── __init__.py
├── tests/
│   ├── __init__.py
│   └── test_core.py
├── requirements.txt
└── main.py

Current Functionality

1. Basic document processing: Extracts frontmatter and main content from Markdown-like text.
2. Placeholder for content summarization.
3. Placeholder for LLM integration.
4. Simple CLI interface for user interaction.
5. Basic unit tests for core functionality.

Running the Prototype

1. Install dependencies: pip install -r requirements.txt
2. Run the CLI interface: python main.py
3. Run tests: python -m unittest tests/test_core.py

Next Steps for Development

1. Core Framework Enhancement:

   • Implement actual LLM integration using GPT4ALL bindings.
   • Develop a robust error handling and logging system.
   • Create a task queue management system for processing multiple documents.

2. Text Operations Module:

   • Implement actual text summarization functionality.
   • Develop outline generation, content extraction, and text comparison features.
   • Integrate advanced text operations with the LLM.

3. Metadata Management:

   • Implement full YAML frontmatter parsing and manipulation.
   • Develop a tagging system for documents.

4. Advanced LLM Integration:

   • Implement context window management for improved LLM performance.
   • Develop advanced prompting techniques (few-shot, chain-of-thought, etc.).

5. User Interface Improvements:

   • Develop a RESTful API for programmatic access.
   • Enhance the CLI with more features and better user experience.
   • Implement a progress reporting system for long-running tasks.

6. Performance Optimization:

   • Implement caching mechanisms for document processing and LLM responses.
   • Optimize for parallel processing where applicable.

7. Testing and Refinement:

   • Expand test coverage with more unit tests and integration tests.
   • Implement performance benchmarking.
   • Conduct user acceptance testing.

8. Documentation and Deployment:

   • Create comprehensive user and developer documentation.
   • Prepare deployment scripts and procedures.

Contributing

This project is in early development. If you're interested in contributing, please focus on implementing the next steps outlined above. Ensure all new code is accompanied by appropriate unit tests.