ira/.note/current_focus.md

Current Focus: Report Generation Module Implementation (Phase 4)

Latest Update (2025-02-28)

We have successfully implemented Phases 1, 2, and 3 of the Report Generation module, and are now making progress on Phase 4: Advanced Features. We have completed the implementation of customizable report detail levels, allowing users to select different levels of detail for generated reports, and have enhanced the analytical depth of detailed and comprehensive reports.

Recent Progress

1. Enhanced Report Detail Levels:

   • Enhanced the template modifiers for DETAILED and COMPREHENSIVE detail levels to focus more on analytical depth, evidence density, and perspective diversity rather than just adding additional sections
   • Improved the document chunk processing to extract more meaningful information from each chunk for detailed and comprehensive reports
   • Added detail-level-specific extraction prompts that guide the LLM to extract different types of information based on the selected detail level
   • Modified the map-reduce approach to pass detail level parameters throughout the process

2. Customizable Report Detail Levels Implementation:

   • Created a ReportDetailLevelManager class in report_detail_levels.py that defines four detail levels:
     • Brief: Concise summary with key findings (uses llama-3.1-8b-instant model)
     • Standard: Balanced report with analysis and conclusions (uses llama-3.1-8b-instant model)
     • Detailed: Comprehensive report with in-depth analysis (uses llama-3.3-70b-versatile model)
     • Comprehensive: Exhaustive report with all available information (uses llama-3.3-70b-versatile model)
   • Each detail level has specific configuration parameters:
     • Number of search results per engine
     • Token budget for report generation
     • Chunk size and overlap size for document processing
     • Recommended LLM model
   • Updated the report synthesis module to use different templates based on detail level
   • Modified the report generator to automatically configure parameters based on the selected detail level
   • Updated the query_to_report.py script to accept a detail_level parameter
   • Created test scripts to demonstrate the different detail levels

3. Gradio UI Enhancements:

   • Updated the Gradio interface to include report generation with detail levels
   • Added custom model selection for report generation
   • Implemented processing of thinking tags in the model output
   • Fixed method names and improved query processing for search execution
   • Enhanced error handling for report generation

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

Next Steps

1. Testing and Refinement of Enhanced Detail Levels:

   • Conduct thorough testing of the enhanced detail level features with various query types
   • Compare the analytical depth and quality of reports generated with the new prompts
   • Gather user feedback on the improved reports at different detail levels
   • Further refine the detail level configurations based on testing and feedback

2. Progressive Report Generation:

   • Design and implement a system for generating reports progressively for very large research tasks
   • Create a mechanism for updating reports as new information is processed
   • Implement a progress tracking system for report generation

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

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