# Current Focus: Google Gemini Integration, Reference Formatting, and NoneType Error Fixes

## Active Work

### Google Gemini Integration
- ✅ Fixed the integration of Google Gemini models with LiteLLM
- ✅ Updated message formatting for Gemini models
- ✅ Added proper handling for the 'gemini' provider in environment variables
- ✅ Fixed reference formatting issues with Gemini models
- ✅ Converted LLM interface methods to async to fix runtime errors

### Gradio UI Updates
- ✅ Updated the Gradio interface to handle async methods
- ✅ Fixed parameter ordering in the report generation function
- ✅ Improved error handling in the UI

### Bug Fixes
- ✅ Fixed NoneType error in report synthesis when chunk titles are None
- ✅ Added defensive null checks throughout document processing and report synthesis
- ✅ Improved chunk counter in map_document_chunks method

## Recent Changes

### Reference Formatting Improvements
- Enhanced the instructions for reference formatting to ensure URLs are included
- Added a recovery mechanism for truncated references
- Improved context preparation to better extract URLs for references
- Added duplicate URL fields in the context to emphasize their importance

### Async LLM Interface
- Made `generate_completion`, `classify_query`, `enhance_query`, and `generate_search_queries` methods async
- Updated dependent code to properly await these methods
- Fixed runtime errors related to async/await patterns in the QueryProcessor

### Error Handling Improvements
- Added null checks for chunk titles in report synthesis
- Improved chunk counter in map_document_chunks method
- Added defensive code to ensure all chunks have titles
- Updated document processor to handle None titles with default values

## Next Steps

1. Continue testing with Gemini models to ensure stable operation
2. Consider adding more robust error handling for LLM provider-specific issues
3. Improve the reference formatting further if needed
4. Update documentation to reflect the changes made to the LLM interface
5. Consider adding more unit tests for the async methods
6. Add more comprehensive null checks throughout the codebase
7. Implement better error handling and recovery mechanisms

### 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
