ira/.note/current_focus.md

Current Focus: Project Directory Reorganization, Testing, and Embedding Usage

Active Work

Project Directory Reorganization

• ✅ Reorganized project directory structure for better maintainability
• ✅ Moved utility scripts to the utils/ directory
• ✅ Organized test files into subdirectories under tests/
• ✅ Moved sample data to the examples/data/ directory
• ✅ Created proper __init__.py files for all packages
• ✅ Verified pipeline functionality after reorganization

Embedding Usage Analysis

• ✅ Confirmed that the pipeline uses Jina AI's Embeddings API through the JinaSimilarity class
• ✅ Verified that the JinaReranker class uses embeddings for document reranking
• ✅ Analyzed how embeddings are integrated into the search and ranking process

Pipeline Testing

• ✅ Tested the pipeline after reorganization to ensure functionality
• ✅ Verified that the UI works correctly with the new directory structure
• ✅ Confirmed that all imports are working properly with the new structure

Repository Cleanup

• Reorganized test files into dedicated directories under tests/
• Created examples/ directory for sample data
• Moved utility scripts to utils/
• Committed changes with message 'Clean up repository: Remove unused test files and add new test directories'

Recent Changes

Directory Structure Reorganization

• Created a dedicated utils/ directory for utility scripts
  • Moved jina_similarity.py to utils/
  • Added __init__.py to make it a proper Python package
• Organized test files into subdirectories under tests/
  • Created subdirectories for each module (query, execution, ranking, report, ui, integration)
  • Added __init__.py files to all test directories
• Created an examples/ directory with subdirectories for data and scripts
  • Moved sample data to examples/data/
  • Added __init__.py files to make them proper Python packages
• Added a dedicated scripts/ directory for utility scripts
  • Moved query_to_report.py to scripts/

Pipeline Verification

• Verified that the pipeline functions correctly after reorganization
• Confirmed that the JinaSimilarity class in utils/jina_similarity.py is properly used for embeddings
• Tested the reranking functionality with the JinaReranker class
• Checked that the report generation process works with the new structure

Next Steps

1. Run comprehensive tests to ensure all functionality works with the new directory structure
2. Update any remaining documentation to reflect the new directory structure
3. Consider moving the remaining test files in the root of the tests/ directory to appropriate subdirectories
4. Review import statements throughout the codebase to ensure they follow the new structure
5. Add more comprehensive documentation about the directory structure
6. Consider creating a development guide for new contributors
7. Implement automated tests to verify the directory structure remains consistent

Future Enhancements

1. Query Processing Improvements:

   • Multiple Query Variation Generation:

     • Generate several similar queries with different keywords and expanded intent for better search coverage
     • Enhance the QueryProcessor class to generate multiple query variations (3-4 per query)
     • Update the execute_search method to handle multiple queries and merge results
     • Implement deduplication for results from different query variations
     • Estimated difficulty: Moderate (3-4 days of work)

   • Threshold-Based Reranking with Larger Document Sets:

     • Process more initial documents and use reranking to select the top N most relevant ones
     • Modify detail level configurations to include parameters for initial results count and final results after reranking
     • Update the SearchExecutor to fetch more results initially
     • Enhance the reranking process to filter based on a score threshold or top N
     • Estimated difficulty: Easy to Moderate (2-3 days of work)

2. UI Improvements:

   • Add Chunk Processing Progress Indicators:
     • Modify the report_synthesis.py file to add logging during the map phase of the map-reduce process
     • Add a counter variable to track which chunk is being processed
     • Use the existing logging infrastructure to output progress messages in the UI
     • Estimated difficulty: Easy (15-30 minutes of work)

3. Visualization Components:

   • Identify common data types in reports that would benefit from visualization
   • Design and implement visualization components for these data types
   • Integrate visualization components into the report generation process

Current Tasks

1. Report Generation Module Implementation (Phase 4):

   • Implementing support for alternative models with larger context windows
   • Implementing progressive report generation for very large research tasks
   • Creating visualization components for data mentioned in reports
   • Adding interactive elements to the generated reports
   • Implementing report versioning and comparison

2. Integration with UI:

   • Adding report generation options to the UI
   • Implementing progress indicators for document scraping and report generation
   • Creating visualization components for generated reports
   • Adding options to customize report generation parameters

3. Performance Optimization:

   • Optimizing token usage for more efficient LLM utilization
   • Implementing caching strategies for document scraping and LLM calls
   • Parallelizing document scraping and processing
   • Exploring parallel processing for the map phase of report synthesis

Recent Progress

1. Report Templates Implementation:

   • ✅ Created a dedicated report_templates.py module with a comprehensive template system
   • ✅ Implemented QueryType enum for categorizing queries (FACTUAL, EXPLORATORY, COMPARATIVE)
   • ✅ Created DetailLevel enum for different report detail levels (BRIEF, STANDARD, DETAILED, COMPREHENSIVE)
   • ✅ Designed a ReportTemplate class with validation for required sections
   • ✅ Implemented a ReportTemplateManager to manage and retrieve templates
   • ✅ Created 12 different templates (3 query types × 4 detail levels)
   • ✅ Added testing with test_report_templates.py and test_brief_report.py
   • ✅ Updated memory bank documentation with template system details

2. Testing and Validation of Report Templates:

   • ✅ Fixed template retrieval issues in the report synthesis module
   • ✅ Successfully tested all detail levels (brief, standard, detailed, comprehensive) with factual queries
   • ✅ Successfully tested all detail levels with exploratory queries
   • ✅ Successfully tested all detail levels with comparative queries
   • ✅ Improved error handling in template retrieval with fallback to standard templates
   • ✅ Added better logging for template retrieval process

Next Steps

1. Further Refinement of Report Templates:

   • Conduct additional testing with real-world queries and document sets
   • Compare the analytical depth and quality of reports generated with different detail levels
   • Gather user feedback on the improved reports at different detail levels
   • Further refine the detail level configurations based on testing and feedback
   • Integrate the template system with the UI to allow users to select detail levels
   • Add more specialized templates for specific research domains
   • Implement template customization options for users

2. Progressive Report Generation Implementation:

   • Implement progressive report generation for comprehensive detail level reports
   • Enable support for different models with the progressive approach
   • Create a hybrid system that uses standard map-reduce for brief/standard/detailed levels and progressive generation for comprehensive level
   • Add UI controls to monitor and control the progressive generation process

   Implementation Plan for Progressive Report Generation

   Phase 1: Core Implementation (2-3 days)

   • Create a new ProgressiveReportSynthesizer class extending from ReportSynthesizer
   • Implement chunk prioritization algorithm based on relevance scores
   • Develop the iterative refinement process with specialized prompts
   • Add state management to track report versions and processed chunks
   • Implement termination conditions (all chunks processed, diminishing returns, user intervention)

   Phase 2: Model Flexibility (1-2 days)

   • Modify the implementation to support different models beyond Gemini
   • Create model-specific configurations for progressive generation
   • Implement adaptive batch sizing based on model context window
   • Add fallback mechanisms for when context windows are exceeded

   Phase 3: UI Integration (1-2 days)

   • Add progress tracking and visualization in the UI
   • Implement controls to pause, resume, or terminate the process
   • Create a preview mode to see the current report state
   • Add options to compare different versions of the report

   Phase 4: Testing and Optimization (2-3 days)

   • Conduct comprehensive testing with various document collections
   • Compare report quality between progressive and standard approaches
   • Optimize token usage and processing efficiency
   • Fine-tune prompts and parameters based on testing results

3. Visualization Components:

   • Identify common data types in reports that would benefit from visualization
   • Design and implement visualization components for these data types
   • Integrate visualization components into the report generation process
   • Consider how visualizations can be incorporated into progressive reports

Technical Notes

• Using Groq's Llama 3.3 70B Versatile model for detailed and comprehensive report synthesis
• Using Groq's Llama 3.1 8B Instant model for brief and standard report synthesis
• Implemented map-reduce approach for processing document chunks with detail-level-specific extraction
• Created enhanced report templates focused on analytical depth rather than just additional sections
• Added citation generation and reference management
• Using asynchronous processing for improved performance in report generation
• Managing API keys securely through environment variables and configuration files