roadmap.md

Weave Framework Roadmap

Completed

Core Framework

• Implemented modular architecture with plugin system
• Created base classes for DataGenerator and TaskCreator
• Developed a flexible Configuration system
• Implemented a Pipeline class for customizable data generation process
• Created the main SyntheticDataFramework class

Data Generation

• Implemented base DataGenerator class
• Created a sample TextGenerator

Task Creation

• Implemented base TaskCreator class
• Created LLMTaskCreator base class for LLM-based task creation

LLM Integration

• Defined BaseLLMProvider interface
• Implemented plugin registry for LLM providers

Examples

• Created a basic example for generating a QA dataset

In Progress

Data Generation

• Implement more diverse data generators (e.g., image, audio)
• Create connectors for various data sources (databases, APIs, web scraping)

Task Creation

• Implement concrete task creators for common tasks (QA, summarization, classification)
• Develop a system for chaining multiple task creators

LLM Integration

• Implement concrete LLM providers (OpenAI, Hugging Face, etc.)
• Develop caching and rate limiting for LLM providers

Evaluation and Quality Control

• Implement basic evaluation metrics
• Create a system for automated quality checks

Upcoming

Advanced Features

• Implement advanced data augmentation techniques
• Develop multi-task learning support
• Create a prompt management system with version control

Scalability and Performance

• Implement distributed processing capabilities
• Optimize for large-scale dataset creation

Visualization and Monitoring

• Create a basic web interface for exploring generated datasets
• Integrate with experiment tracking tools (e.g., MLflow, Weights & Biases)

Documentation and Community

• Write comprehensive API documentation
• Create tutorials and best practices guides
• Set up a GitHub repository with contribution guidelines

Future Enhancements

• Implement bias detection and mitigation tools
• Develop active learning strategies for data quality improvement
• Create interfaces for human-in-the-loop validation
• Implement model fine-tuning capabilities
• Develop custom dashboards for monitoring data generation processes