273 lines
16 KiB
Markdown
273 lines
16 KiB
Markdown
# Current Focus: LLM-Based Query Classification, UI Bug Fixes, and Project Directory Reorganization
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## Active Work
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### LLM-Based Query Domain Classification
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- ✅ Implemented LLM-based query domain classification to replace keyword-based approach
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- ✅ Added `classify_query_domain` method to `LLMInterface` class
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- ✅ Created `_structure_query_with_llm` method in `QueryProcessor` to use LLM classification results
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- ✅ Added fallback to keyword-based classification for resilience
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- ✅ Enhanced structured query with domain, confidence, and reasoning fields
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- ✅ Added comprehensive test script to verify functionality
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- ✅ Added detailed documentation about the new implementation
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- ✅ Updated configuration to support the new classification method
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- ✅ Improved logging for better monitoring of classification results
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### UI Bug Fixes
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- ✅ Fixed AttributeError in report generation progress callback
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- ✅ Updated UI progress callback to use direct value assignment instead of update method
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- ✅ Enhanced progress callback to use Gradio's built-in progress tracking mechanism for better UI updates during async operations
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- ✅ Consolidated redundant progress indicators in the UI to use only Gradio's built-in progress tracking
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- ✅ Fixed model selection issue in report generation to ensure the model selected in the UI is properly used throughout the report generation process
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- ✅ Fixed model provider selection to correctly use the provider specified in the config.yaml file (e.g., ensuring Gemini models use the Gemini provider)
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- ✅ Added detailed logging for model and provider selection to aid in debugging
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- ✅ Implemented comprehensive tests for provider selection stability across multiple initializations, model switches, and configuration changes
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- ✅ Enhanced provider selection stability tests to include fallback mechanisms, edge cases with invalid providers, and provider selection consistency between singleton and new instances
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- ✅ Added test for provider selection stability after config reload
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- ✅ Committed changes with message "Enhanced provider selection stability tests with additional scenarios and edge cases"
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### Project Directory Reorganization
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- ✅ Reorganized project directory structure for better maintainability
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- ✅ Moved utility scripts to the `utils/` directory
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- ✅ Organized test files into subdirectories under `tests/`
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- ✅ Moved sample data to the `examples/data/` directory
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- ✅ Created proper `__init__.py` files for all packages
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- ✅ Verified pipeline functionality after reorganization
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### Embedding Usage Analysis
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- ✅ Confirmed that the pipeline uses Jina AI's Embeddings API through the `JinaSimilarity` class
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- ✅ Verified that the `JinaReranker` class uses embeddings for document reranking
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- ✅ Analyzed how embeddings are integrated into the search and ranking process
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### Pipeline Testing
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- ✅ Tested the pipeline after reorganization to ensure functionality
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- ✅ Verified that the UI works correctly with the new directory structure
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- ✅ Confirmed that all imports are working properly with the new structure
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## Repository Cleanup
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- Reorganized test files into dedicated directories under `tests/`
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- Created `examples/` directory for sample data
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- Moved utility scripts to `utils/`
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- Committed changes with message 'Clean up repository: Remove unused test files and add new test directories'
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## Recent Changes
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### Directory Structure Reorganization
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- Created a dedicated `utils/` directory for utility scripts
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- Moved `jina_similarity.py` to `utils/`
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- Added `__init__.py` to make it a proper Python package
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- Organized test files into subdirectories under `tests/`
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- Created subdirectories for each module (query, execution, ranking, report, ui, integration)
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- Added `__init__.py` files to all test directories
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- Created an `examples/` directory with subdirectories for data and scripts
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- Moved sample data to `examples/data/`
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- Added `__init__.py` files to make them proper Python packages
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- Added a dedicated `scripts/` directory for utility scripts
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- Moved `query_to_report.py` to `scripts/`
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### Pipeline Verification
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- Verified that the pipeline functions correctly after reorganization
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- Confirmed that the `JinaSimilarity` class in `utils/jina_similarity.py` is properly used for embeddings
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- Tested the reranking functionality with the `JinaReranker` class
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- Checked that the report generation process works with the new structure
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### Query Type Selection in Gradio UI
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- ✅ Added a dropdown menu for query type selection in the "Generate Report" tab
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- ✅ Included options for "auto-detect", "factual", "exploratory", and "comparative"
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- ✅ Added descriptive tooltips explaining each query type
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- ✅ Set "auto-detect" as the default option
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- ✅ Modified the `generate_report` method in the `GradioInterface` class to handle the new query_type parameter
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- ✅ Updated the report button click handler to pass the query type to the generate_report method
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- ✅ Updated the `generate_report` method in the `ReportGenerator` class to accept a query_type parameter
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- ✅ Modified the report synthesizer calls to pass the query_type parameter
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- ✅ Added a "Query Types" section to the Gradio UI explaining each query type
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- ✅ Committed changes with message "Add query type selection to Gradio UI and improve report generation"
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## Next Steps
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1. Run comprehensive tests to ensure all functionality works with the new directory structure
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2. Update any remaining documentation to reflect the new directory structure
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3. Consider moving the remaining test files in the root of the `tests/` directory to appropriate subdirectories
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4. Review import statements throughout the codebase to ensure they follow the new structure
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5. Add more comprehensive documentation about the directory structure
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6. Consider creating a development guide for new contributors
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7. Implement automated tests to verify the directory structure remains consistent
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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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- ✅ Added a `set_progress_callback` method to the `ReportGenerator` class
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- ✅ Implemented progress tracking in both standard and progressive report synthesizers
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- ✅ Updated the Gradio UI to display progress during report generation
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- ✅ Fixed issues with progress reporting in the UI
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- ✅ Ensured proper initialization of the report generator in the UI
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- ✅ Added proper error handling for progress updates
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- ✅ **Add Query Type Selection**:
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- ✅ Added a dropdown menu for query type selection in the "Generate Report" tab
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- ✅ Included options for "auto-detect", "factual", "exploratory", "comparative", and "code"
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- ✅ Added descriptive tooltips explaining each query type
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- ✅ Modified the report generation logic to handle the selected query type
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- ✅ Added documentation to help users understand when to use each query type
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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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- ✅ Adding query type selection to the UI
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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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### Recent Progress
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1. **Report Templates Implementation**:
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- ✅ Created a dedicated `report_templates.py` module with a comprehensive template system
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- ✅ Implemented `QueryType` enum for categorizing queries (FACTUAL, EXPLORATORY, COMPARATIVE, CODE)
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- ✅ Created `DetailLevel` enum for different report detail levels (BRIEF, STANDARD, DETAILED, COMPREHENSIVE)
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- ✅ Designed a `ReportTemplate` class with validation for required sections
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- ✅ Implemented a `ReportTemplateManager` to manage and retrieve templates
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- ✅ Created 16 different templates (4 query types × 4 detail levels)
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- ✅ Added testing with `test_report_templates.py` and `test_brief_report.py`
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- ✅ Updated memory bank documentation with template system details
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2. **Testing and Validation of Report Templates**:
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- ✅ Fixed template retrieval issues in the report synthesis module
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- ✅ Successfully tested all detail levels (brief, standard, detailed, comprehensive) with factual queries
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- ✅ Successfully tested all detail levels with exploratory queries
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- ✅ Successfully tested all detail levels with comparative queries
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- ✅ Improved error handling in template retrieval with fallback to standard templates
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- ✅ Added better logging for template retrieval process
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3. **UI Enhancements**:
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- ✅ Added progress tracking for report generation
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- ✅ Added query type selection dropdown
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- ✅ Added documentation for query types and detail levels
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- ✅ Improved error handling in the UI
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### Next Steps
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1. **Further Refinement of Report Templates**:
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- Conduct additional testing with real-world queries and document sets
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- Compare the analytical depth and quality of reports generated with different detail levels
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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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- Integrate the template system with the UI to allow users to select detail levels
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- Add more specialized templates for specific research domains
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- Implement template customization options for users
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2. **Progressive Report Generation Implementation**:
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- ✅ Implemented progressive report generation for comprehensive detail level reports
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- ✅ Created a hybrid system that uses standard map-reduce for brief/standard/detailed levels and progressive generation for comprehensive level
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- ✅ Added support for different models with adaptive batch sizing
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- ✅ Implemented progress tracking and callback mechanism
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- ✅ Created comprehensive test suite for progressive report generation
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- ⏳ Add UI controls to monitor and control the progressive generation process
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#### Implementation Details for Progressive Report Generation
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**Phase 1: Core Implementation (Completed)**
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- ✅ Created a new `ProgressiveReportSynthesizer` class extending from `ReportSynthesizer`
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- ✅ Implemented chunk prioritization algorithm based on relevance scores
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- ✅ Developed the iterative refinement process with specialized prompts
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- ✅ Added state management to track report versions and processed chunks
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- ✅ Implemented termination conditions (all chunks processed, diminishing returns, user intervention)
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**Phase 2: Model Flexibility (Completed)**
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- ✅ Modified the implementation to support different models beyond Gemini
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- ✅ Created model-specific configurations for progressive generation
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- ✅ Implemented adaptive batch sizing based on model context window
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- ✅ Added fallback mechanisms for when context windows are exceeded
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**Phase 3: UI Integration (In Progress)**
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- ✅ Added progress tracking callback mechanism
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- ⏳ Implement controls to pause, resume, or terminate the process
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- ⏳ Create a preview mode to see the current report state
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- ⏳ Add options to compare different versions of the report
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**Phase 4: Testing and Optimization (Completed)**
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- ✅ Created test script for progressive report generation
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- ✅ Added comparison functionality between progressive and standard approaches
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- ✅ Implemented optimization for token usage and processing efficiency
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- ✅ Fine-tuned prompts and parameters based on testing results
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3. **Query Type Selection Enhancement**:
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- ✅ Added query type selection dropdown to the UI
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- ✅ Implemented handling of user-selected query types in the report generation process
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- ✅ Added documentation to help users understand when to use each query type
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- ✅ Added CODE as a new query type with specialized templates at all detail levels
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- ✅ Implemented code query detection with language, framework, and pattern recognition
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- ✅ Added GitHub and StackExchange search handlers for code-related queries
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- ⏳ Test the query type selection with various queries to ensure it works correctly
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- ⏳ Gather user feedback on the usefulness of manual query type selection
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- ⏳ Consider adding more specialized templates for specific query types
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- ⏳ Explore adding query type detection confidence scores to help users decide when to override
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- ⏳ Add examples of each query type to help users understand the differences
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4. **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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- Consider how visualizations can be incorporated into progressive reports
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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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- Implemented progressive report generation for comprehensive detail level:
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- Uses iterative refinement process to gradually improve report quality
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- Processes document chunks in batches based on priority
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- Tracks improvement scores to detect diminishing returns
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- Adapts batch size based on model context window
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- Provides progress tracking through callback mechanism
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- Added query type selection to the UI:
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- Allows users to explicitly select the query type (factual, exploratory, comparative, code)
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- Provides auto-detect option for convenience
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- Includes documentation to help users understand when to use each query type
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- Passes the selected query type through the report generation pipeline
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- Implemented specialized code query support:
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- Added GitHub API for searching code repositories
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- Added StackExchange API for programming Q&A content
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- Created code detection based on programming languages, frameworks, and patterns
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- Designed specialized report templates for code content with syntax highlighting
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- Enhanced result ranking to prioritize code-related sources for programming queries
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