# Current Focus: UI Bug Fixes, Project Directory Reorganization, and Embedding Usage

## Active Work

### UI Bug Fixes
- ✅ Fixed AttributeError in report generation progress callback
- ✅ Updated UI progress callback to use direct value assignment instead of update method
- ✅ Enhanced progress callback to use Gradio's built-in progress tracking mechanism for better UI updates during async operations
- ✅ Committed changes with message "Fix AttributeError in report progress callback by using direct value assignment instead of update method"

### Project Directory Reorganization
- ✅ Reorganized project directory structure for better maintainability
- ✅ Moved utility scripts to the `utils/` directory
- ✅ Organized test files into subdirectories under `tests/`
- ✅ Moved sample data to the `examples/data/` directory
- ✅ Created proper `__init__.py` files for all packages
- ✅ Verified pipeline functionality after reorganization

### Embedding Usage Analysis
- ✅ Confirmed that the pipeline uses Jina AI's Embeddings API through the `JinaSimilarity` class
- ✅ Verified that the `JinaReranker` class uses embeddings for document reranking
- ✅ Analyzed how embeddings are integrated into the search and ranking process

### Pipeline Testing
- ✅ Tested the pipeline after reorganization to ensure functionality
- ✅ Verified that the UI works correctly with the new directory structure
- ✅ Confirmed that all imports are working properly with the new structure

## Repository Cleanup
- Reorganized test files into dedicated directories under `tests/`
- Created `examples/` directory for sample data
- Moved utility scripts to `utils/`
- Committed changes with message 'Clean up repository: Remove unused test files and add new test directories'

## Recent Changes

### Directory Structure Reorganization
- Created a dedicated `utils/` directory for utility scripts
  - Moved `jina_similarity.py` to `utils/`
  - Added `__init__.py` to make it a proper Python package
- Organized test files into subdirectories under `tests/`
  - Created subdirectories for each module (query, execution, ranking, report, ui, integration)
  - Added `__init__.py` files to all test directories
- Created an `examples/` directory with subdirectories for data and scripts
  - Moved sample data to `examples/data/`
  - Added `__init__.py` files to make them proper Python packages
- Added a dedicated `scripts/` directory for utility scripts
  - Moved `query_to_report.py` to `scripts/`

### Pipeline Verification
- Verified that the pipeline functions correctly after reorganization
- Confirmed that the `JinaSimilarity` class in `utils/jina_similarity.py` is properly used for embeddings
- Tested the reranking functionality with the `JinaReranker` class
- Checked that the report generation process works with the new structure

### Query Type Selection in Gradio UI
- ✅ Added a dropdown menu for query type selection in the "Generate Report" tab
- ✅ Included options for "auto-detect", "factual", "exploratory", and "comparative"
- ✅ Added descriptive tooltips explaining each query type
- ✅ Set "auto-detect" as the default option
- ✅ Modified the `generate_report` method in the `GradioInterface` class to handle the new query_type parameter
- ✅ Updated the report button click handler to pass the query type to the generate_report method
- ✅ Updated the `generate_report` method in the `ReportGenerator` class to accept a query_type parameter
- ✅ Modified the report synthesizer calls to pass the query_type parameter
- ✅ Added a "Query Types" section to the Gradio UI explaining each query type
- ✅ Committed changes with message "Add query type selection to Gradio UI and improve report generation"

## Next Steps

1. Run comprehensive tests to ensure all functionality works with the new directory structure
2. Update any remaining documentation to reflect the new directory structure
3. Consider moving the remaining test files in the root of the `tests/` directory to appropriate subdirectories
4. Review import statements throughout the codebase to ensure they follow the new structure
5. Add more comprehensive documentation about the directory structure
6. Consider creating a development guide for new contributors
7. Implement automated tests to verify the directory structure remains consistent

### 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**:
     - ✅ Added a `set_progress_callback` method to the `ReportGenerator` class
     - ✅ Implemented progress tracking in both standard and progressive report synthesizers
     - ✅ Updated the Gradio UI to display progress during report generation
     - ✅ Fixed issues with progress reporting in the UI
     - ✅ Ensured proper initialization of the report generator in the UI
     - ✅ Added proper error handling for progress updates
   
   - ✅ **Add Query Type Selection**:
     - ✅ Added a dropdown menu for query type selection in the "Generate Report" tab
     - ✅ Included options for "auto-detect", "factual", "exploratory", "comparative", and "code"
     - ✅ Added descriptive tooltips explaining each query type
     - ✅ Modified the report generation logic to handle the selected query type
     - ✅ Added documentation to help users understand when to use each query type

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
   - ✅ Adding query type selection to the UI
   - 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

### Recent Progress

1. **Report Templates Implementation**:
   - ✅ Created a dedicated `report_templates.py` module with a comprehensive template system
   - ✅ Implemented `QueryType` enum for categorizing queries (FACTUAL, EXPLORATORY, COMPARATIVE, CODE)
   - ✅ Created `DetailLevel` enum for different report detail levels (BRIEF, STANDARD, DETAILED, COMPREHENSIVE)
   - ✅ Designed a `ReportTemplate` class with validation for required sections
   - ✅ Implemented a `ReportTemplateManager` to manage and retrieve templates
   - ✅ Created 16 different templates (4 query types × 4 detail levels)
   - ✅ Added testing with `test_report_templates.py` and `test_brief_report.py`
   - ✅ Updated memory bank documentation with template system details

2. **Testing and Validation of Report Templates**:
   - ✅ Fixed template retrieval issues in the report synthesis module
   - ✅ Successfully tested all detail levels (brief, standard, detailed, comprehensive) with factual queries
   - ✅ Successfully tested all detail levels with exploratory queries
   - ✅ Successfully tested all detail levels with comparative queries
   - ✅ Improved error handling in template retrieval with fallback to standard templates
   - ✅ Added better logging for template retrieval process

3. **UI Enhancements**:
   - ✅ Added progress tracking for report generation
   - ✅ Added query type selection dropdown
   - ✅ Added documentation for query types and detail levels
   - ✅ Improved error handling in the UI

### Next Steps

1. **Further Refinement of Report Templates**:
   - Conduct additional testing with real-world queries and document sets
   - Compare the analytical depth and quality of reports generated with different detail levels
   - Gather user feedback on the improved reports at different detail levels
   - Further refine the detail level configurations based on testing and feedback
   - Integrate the template system with the UI to allow users to select detail levels
   - Add more specialized templates for specific research domains
   - Implement template customization options for users

2. **Progressive Report Generation Implementation**:
   - ✅ Implemented progressive report generation for comprehensive detail level reports
   - ✅ Created a hybrid system that uses standard map-reduce for brief/standard/detailed levels and progressive generation for comprehensive level
   - ✅ Added support for different models with adaptive batch sizing
   - ✅ Implemented progress tracking and callback mechanism
   - ✅ Created comprehensive test suite for progressive report generation
   - ⏳ Add UI controls to monitor and control the progressive generation process

   #### Implementation Details for Progressive Report Generation

   **Phase 1: Core Implementation (Completed)**
   - ✅ Created a new `ProgressiveReportSynthesizer` class extending from `ReportSynthesizer`
   - ✅ Implemented chunk prioritization algorithm based on relevance scores
   - ✅ Developed the iterative refinement process with specialized prompts
   - ✅ Added state management to track report versions and processed chunks
   - ✅ Implemented termination conditions (all chunks processed, diminishing returns, user intervention)

   **Phase 2: Model Flexibility (Completed)**
   - ✅ Modified the implementation to support different models beyond Gemini
   - ✅ Created model-specific configurations for progressive generation
   - ✅ Implemented adaptive batch sizing based on model context window
   - ✅ Added fallback mechanisms for when context windows are exceeded

   **Phase 3: UI Integration (In Progress)**
   - ✅ Added progress tracking callback mechanism
   - ⏳ Implement controls to pause, resume, or terminate the process
   - ⏳ Create a preview mode to see the current report state
   - ⏳ Add options to compare different versions of the report

   **Phase 4: Testing and Optimization (Completed)**
   - ✅ Created test script for progressive report generation
   - ✅ Added comparison functionality between progressive and standard approaches
   - ✅ Implemented optimization for token usage and processing efficiency
   - ✅ Fine-tuned prompts and parameters based on testing results

3. **Query Type Selection Enhancement**:
   - ✅ Added query type selection dropdown to the UI
   - ✅ Implemented handling of user-selected query types in the report generation process
   - ✅ Added documentation to help users understand when to use each query type
   - ✅ Added CODE as a new query type with specialized templates at all detail levels
   - ✅ Implemented code query detection with language, framework, and pattern recognition
   - ✅ Added GitHub and StackExchange search handlers for code-related queries
   - ⏳ Test the query type selection with various queries to ensure it works correctly
   - ⏳ Gather user feedback on the usefulness of manual query type selection
   - ⏳ Consider adding more specialized templates for specific query types
   - ⏳ Explore adding query type detection confidence scores to help users decide when to override
   - ⏳ Add examples of each query type to help users understand the differences

4. **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
   - Consider how visualizations can be incorporated into progressive reports

### 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
- Implemented progressive report generation for comprehensive detail level:
  - Uses iterative refinement process to gradually improve report quality
  - Processes document chunks in batches based on priority
  - Tracks improvement scores to detect diminishing returns
  - Adapts batch size based on model context window
  - Provides progress tracking through callback mechanism
- Added query type selection to the UI:
  - Allows users to explicitly select the query type (factual, exploratory, comparative, code)
  - Provides auto-detect option for convenience
  - Includes documentation to help users understand when to use each query type
  - Passes the selected query type through the report generation pipeline
- Implemented specialized code query support:
  - Added GitHub API for searching code repositories
  - Added StackExchange API for programming Q&A content
  - Created code detection based on programming languages, frameworks, and patterns
  - Designed specialized report templates for code content with syntax highlighting
  - Enhanced result ranking to prioritize code-related sources for programming queries
