89 lines
4.3 KiB
Markdown
89 lines
4.3 KiB
Markdown
# Current Focus: Report Generation Module Implementation (Phase 4)
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## Latest Update (2025-02-27)
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We have successfully implemented Phases 1, 2, and 3 of the Report Generation module. The next focus is on Phase 4: Advanced Features, which includes support for alternative models, progressive report generation, visualization components, and interactive elements.
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### Recent Progress
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1. **Report Generation Module Phase 3 Implementation**:
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- Integrated with Groq's Llama 3.3 70B Versatile model for report synthesis
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- Implemented a map-reduce approach for processing document chunks:
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- Map: Process individual chunks to extract key information
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- Reduce: Synthesize extracted information into a coherent report
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- Created report templates for different query types (factual, exploratory, comparative)
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- Added citation generation and reference management
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- Implemented Markdown formatting for reports
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- Created comprehensive test scripts to verify functionality
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2. **LLM Integration Enhancements**:
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- Created a dedicated ReportSynthesizer class for report generation
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- Configured proper integration with Groq and OpenRouter providers
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- Implemented error handling and logging throughout the process
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- Added support for different query types with automatic detection
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3. **Testing Framework Updates**:
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- Created a dedicated test script for the report synthesis functionality
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- Implemented tests with both sample data and real URLs
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- Added support for mock data to avoid API dependencies during testing
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- Verified end-to-end functionality from document scraping to report generation
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### Current Tasks
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1. **Report Generation Module Implementation (Phase 4)**:
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- Adding 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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- Implementing customizable report detail levels
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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 report templates and common queries
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- Enhancing parallel processing for the map phase of report generation
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- Improving error recovery and retry mechanisms
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### Next Steps
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1. **Complete Phase 4 of Report Generation Module**:
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- Implement support for alternative models with larger context windows
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- Develop progressive report generation for very large research tasks
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- Create visualization components for data mentioned in reports
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- Add interactive elements to the generated reports
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- Implement report versioning and comparison
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- Implement customizable report detail levels with the following options:
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- Adjustable number of search results
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- Configurable token budget
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- Customizable synthesis prompts
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- Different report style templates
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- Adjustable chunking parameters
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- Model selection options
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2. **Enhance UI Integration**:
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- Add report generation options to the UI
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- Implement progress indicators for document scraping and report generation
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- Create visualization components for generated reports
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- Add options to customize report generation parameters
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3. **Comprehensive Testing and Documentation**:
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- Create end-to-end tests for the complete pipeline
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- Test with various document types and sizes
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- Evaluate performance and optimize as needed
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- Create comprehensive documentation for the report generation module
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### Technical Notes
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- Using Groq's Llama 3.3 70B Versatile model for report synthesis
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- Implemented map-reduce approach for processing document chunks
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- Created report templates for different query types (factual, exploratory, comparative)
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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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