ira/.note/current_focus.md

Current Focus: Report Generation Module Implementation (Phase 4)

Latest Update (2025-02-27)

We have successfully implemented Phases 1, 2, and 3 of the Report Generation module. The next focus is on Phase 4: Advanced Features, which includes support for alternative models, progressive report generation, visualization components, and interactive elements.

Recent Progress

1. Report Generation Module Phase 3 Implementation:

   • Integrated with Groq's Llama 3.3 70B Versatile model for report synthesis
   • Implemented a map-reduce approach for processing document chunks:
     • Map: Process individual chunks to extract key information
     • Reduce: Synthesize extracted information into a coherent report
   • Created report templates for different query types (factual, exploratory, comparative)
   • Added citation generation and reference management
   • Implemented Markdown formatting for reports
   • Created comprehensive test scripts to verify functionality

2. LLM Integration Enhancements:

   • Created a dedicated ReportSynthesizer class for report generation
   • Configured proper integration with Groq and OpenRouter providers
   • Implemented error handling and logging throughout the process
   • Added support for different query types with automatic detection

3. Testing Framework Updates:

   • Created a dedicated test script for the report synthesis functionality
   • Implemented tests with both sample data and real URLs
   • Added support for mock data to avoid API dependencies during testing
   • Verified end-to-end functionality from document scraping to report generation

Current Tasks

1. Report Generation Module Implementation (Phase 4):

   • Adding 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
   • Implementing customizable report detail levels

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 report templates and common queries
   • Enhancing parallel processing for the map phase of report generation
   • Improving error recovery and retry mechanisms

Next Steps

1. Complete Phase 4 of Report Generation Module:

   • Implement support for alternative models with larger context windows
   • Develop progressive report generation for very large research tasks
   • Create visualization components for data mentioned in reports
   • Add interactive elements to the generated reports
   • Implement report versioning and comparison
   • Implement customizable report detail levels with the following options:
     • Adjustable number of search results
     • Configurable token budget
     • Customizable synthesis prompts
     • Different report style templates
     • Adjustable chunking parameters
     • Model selection options

2. Enhance UI Integration:

   • Add report generation options to the UI
   • Implement progress indicators for document scraping and report generation
   • Create visualization components for generated reports
   • Add options to customize report generation parameters

3. Comprehensive Testing and Documentation:

   • Create end-to-end tests for the complete pipeline
   • Test with various document types and sizes
   • Evaluate performance and optimize as needed
   • Create comprehensive documentation for the report generation module

Technical Notes

• Using Groq's Llama 3.3 70B Versatile model for report synthesis
• Implemented map-reduce approach for processing document chunks
• Created report templates for different query types (factual, exploratory, comparative)
• 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