131 lines
6.8 KiB
Markdown
131 lines
6.8 KiB
Markdown
# Current Focus: Project Directory Reorganization, Testing, and Embedding Usage
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## Active Work
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### Project Directory Reorganization
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- ✅ Reorganized project directory structure for better maintainability
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- ✅ Moved utility scripts to the `utils/` directory
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- ✅ Organized test files into subdirectories under `tests/`
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- ✅ Moved sample data to the `examples/data/` directory
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- ✅ Created proper `__init__.py` files for all packages
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- ✅ Verified pipeline functionality after reorganization
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### Embedding Usage Analysis
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- ✅ Confirmed that the pipeline uses Jina AI's Embeddings API through the `JinaSimilarity` class
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- ✅ Verified that the `JinaReranker` class uses embeddings for document reranking
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- ✅ Analyzed how embeddings are integrated into the search and ranking process
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### Pipeline Testing
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- ✅ Tested the pipeline after reorganization to ensure functionality
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- ✅ Verified that the UI works correctly with the new directory structure
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- ✅ Confirmed that all imports are working properly with the new structure
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## Recent Changes
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### Directory Structure Reorganization
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- Created a dedicated `utils/` directory for utility scripts
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- Moved `jina_similarity.py` to `utils/`
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- Added `__init__.py` to make it a proper Python package
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- Organized test files into subdirectories under `tests/`
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- Created subdirectories for each module (query, execution, ranking, report, ui, integration)
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- Added `__init__.py` files to all test directories
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- Created an `examples/` directory with subdirectories for data and scripts
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- Moved sample data to `examples/data/`
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- Added `__init__.py` files to make them proper Python packages
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- Added a dedicated `scripts/` directory for utility scripts
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- Moved `query_to_report.py` to `scripts/`
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### Pipeline Verification
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- Verified that the pipeline functions correctly after reorganization
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- Confirmed that the `JinaSimilarity` class in `utils/jina_similarity.py` is properly used for embeddings
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- Tested the reranking functionality with the `JinaReranker` class
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- Checked that the report generation process works with the new structure
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## Next Steps
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1. Run comprehensive tests to ensure all functionality works with the new directory structure
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2. Update any remaining documentation to reflect the new directory structure
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3. Consider moving the remaining test files in the root of the `tests/` directory to appropriate subdirectories
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4. Review import statements throughout the codebase to ensure they follow the new structure
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5. Add more comprehensive documentation about the directory structure
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6. Consider creating a development guide for new contributors
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7. Implement automated tests to verify the directory structure remains consistent
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### Future Enhancements
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1. **Query Processing Improvements**:
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- **Multiple Query Variation Generation**:
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- Generate several similar queries with different keywords and expanded intent for better search coverage
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- Enhance the `QueryProcessor` class to generate multiple query variations (3-4 per query)
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- Update the `execute_search` method to handle multiple queries and merge results
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- Implement deduplication for results from different query variations
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- Estimated difficulty: Moderate (3-4 days of work)
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- **Threshold-Based Reranking with Larger Document Sets**:
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- Process more initial documents and use reranking to select the top N most relevant ones
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- Modify detail level configurations to include parameters for initial results count and final results after reranking
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- Update the `SearchExecutor` to fetch more results initially
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- Enhance the reranking process to filter based on a score threshold or top N
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- Estimated difficulty: Easy to Moderate (2-3 days of work)
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2. **UI Improvements**:
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- **Add Chunk Processing Progress Indicators**:
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- Modify the `report_synthesis.py` file to add logging during the map phase of the map-reduce process
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- Add a counter variable to track which chunk is being processed
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- Use the existing logging infrastructure to output progress messages in the UI
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- Estimated difficulty: Easy (15-30 minutes of work)
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3. **Visualization Components**:
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- Identify common data types in reports that would benefit from visualization
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- Design and implement visualization components for these data types
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- Integrate visualization components into the report generation process
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### Current Tasks
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1. **Report Generation Module Implementation (Phase 4)**:
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- Implementing support for alternative models with larger context windows
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- Implementing progressive report generation for very large research tasks
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- Creating visualization components for data mentioned in reports
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- Adding interactive elements to the generated reports
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- Implementing report versioning and comparison
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2. **Integration with UI**:
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- Adding report generation options to the UI
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- Implementing progress indicators for document scraping and report generation
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- Creating visualization components for generated reports
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- Adding options to customize report generation parameters
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3. **Performance Optimization**:
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- Optimizing token usage for more efficient LLM utilization
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- Implementing caching strategies for document scraping and LLM calls
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- Parallelizing document scraping and processing
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- Exploring parallel processing for the map phase of report synthesis
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### Next Steps
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1. **Testing and Refinement of Enhanced Detail Levels**:
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- Conduct thorough testing of the enhanced detail level features with various query types
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- Compare the analytical depth and quality of reports generated with the new prompts
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- Gather user feedback on the improved reports at different detail levels
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- Further refine the detail level configurations based on testing and feedback
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2. **Progressive Report Generation**:
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- Design and implement a system for generating reports progressively for very large research tasks
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- Create a mechanism for updating reports as new information is processed
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- Implement a progress tracking system for report generation
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3. **Visualization Components**:
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- Identify common data types in reports that would benefit from visualization
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- Design and implement visualization components for these data types
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- Integrate visualization components into the report generation process
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### Technical Notes
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- Using Groq's Llama 3.3 70B Versatile model for detailed and comprehensive report synthesis
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- Using Groq's Llama 3.1 8B Instant model for brief and standard report synthesis
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- Implemented map-reduce approach for processing document chunks with detail-level-specific extraction
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- Created enhanced report templates focused on analytical depth rather than just additional sections
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- Added citation generation and reference management
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- Using asynchronous processing for improved performance in report generation
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- Managing API keys securely through environment variables and configuration files
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