15 KiB
15 KiB
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
toutils/
- Added
__init__.py
to make it a proper Python package
- Moved
- 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
- Moved sample data to
- Added a dedicated
scripts/
directory for utility scripts- Moved
query_to_report.py
toscripts/
- Moved
Pipeline Verification
- Verified that the pipeline functions correctly after reorganization
- Confirmed that the
JinaSimilarity
class inutils/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 theGradioInterface
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 theReportGenerator
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
- Run comprehensive tests to ensure all functionality works with the new directory structure
- Update any remaining documentation to reflect the new directory structure
- Consider moving the remaining test files in the root of the
tests/
directory to appropriate subdirectories - Review import statements throughout the codebase to ensure they follow the new structure
- Add more comprehensive documentation about the directory structure
- Consider creating a development guide for new contributors
- Implement automated tests to verify the directory structure remains consistent
Future Enhancements
-
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)
-
-
UI Improvements:
-
✅ Add Chunk Processing Progress Indicators:
- ✅ Added a
set_progress_callback
method to theReportGenerator
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
- ✅ Added a
-
✅ 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
-
-
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
-
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
-
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
-
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
-
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
andtest_brief_report.py
- ✅ Updated memory bank documentation with template system details
- ✅ Created a dedicated
-
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
-
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
-
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
-
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 fromReportSynthesizer
- ✅ 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
-
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
-
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