128 lines
6.3 KiB
Markdown
128 lines
6.3 KiB
Markdown
# Current Focus: Google Gemini Integration, Reference Formatting, and NoneType Error Fixes
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## Active Work
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### Google Gemini Integration
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- ✅ Fixed the integration of Google Gemini models with LiteLLM
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- ✅ Updated message formatting for Gemini models
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- ✅ Added proper handling for the 'gemini' provider in environment variables
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- ✅ Fixed reference formatting issues with Gemini models
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- ✅ Converted LLM interface methods to async to fix runtime errors
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### Gradio UI Updates
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- ✅ Updated the Gradio interface to handle async methods
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- ✅ Fixed parameter ordering in the report generation function
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- ✅ Improved error handling in the UI
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### Bug Fixes
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- ✅ Fixed NoneType error in report synthesis when chunk titles are None
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- ✅ Added defensive null checks throughout document processing and report synthesis
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- ✅ Improved chunk counter in map_document_chunks method
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## Recent Changes
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### Reference Formatting Improvements
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- Enhanced the instructions for reference formatting to ensure URLs are included
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- Added a recovery mechanism for truncated references
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- Improved context preparation to better extract URLs for references
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- Added duplicate URL fields in the context to emphasize their importance
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### Async LLM Interface
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- Made `generate_completion`, `classify_query`, `enhance_query`, and `generate_search_queries` methods async
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- Updated dependent code to properly await these methods
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- Fixed runtime errors related to async/await patterns in the QueryProcessor
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### Error Handling Improvements
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- Added null checks for chunk titles in report synthesis
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- Improved chunk counter in map_document_chunks method
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- Added defensive code to ensure all chunks have titles
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- Updated document processor to handle None titles with default values
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## Next Steps
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1. Continue testing with Gemini models to ensure stable operation
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2. Consider adding more robust error handling for LLM provider-specific issues
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3. Improve the reference formatting further if needed
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4. Update documentation to reflect the changes made to the LLM interface
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5. Consider adding more unit tests for the async methods
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6. Add more comprehensive null checks throughout the codebase
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7. Implement better error handling and recovery mechanisms
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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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