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Update memory bank with enhanced report detail levels and synthesis process

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Steve White 2025-02-28 09:01:32 -06:00
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.note/current_focus.md
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# Current Focus: Report Generation Module Implementation (Phase 4)
## Latest Update (2025-02-27)
## Latest Update (2025-02-28)
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.
We have successfully implemented Phases 1, 2, and 3 of the Report Generation module, and are now making progress on Phase 4: Advanced Features. We have completed the implementation of customizable report detail levels, allowing users to select different levels of detail for generated reports, and have enhanced the analytical depth of detailed and comprehensive reports.
### Recent Progress
1. **Report Generation Module Phase 3 Implementation**:
1. **Enhanced Report Detail Levels**:
- Enhanced the template modifiers for DETAILED and COMPREHENSIVE detail levels to focus more on analytical depth, evidence density, and perspective diversity rather than just adding additional sections
- Improved the document chunk processing to extract more meaningful information from each chunk for detailed and comprehensive reports
- Added detail-level-specific extraction prompts that guide the LLM to extract different types of information based on the selected detail level
- Modified the map-reduce approach to pass detail level parameters throughout the process
2. **Customizable Report Detail Levels Implementation**:
- Created a `ReportDetailLevelManager` class in `report_detail_levels.py` that defines four detail levels:
- Brief: Concise summary with key findings (uses llama-3.1-8b-instant model)
- Standard: Balanced report with analysis and conclusions (uses llama-3.1-8b-instant model)
- Detailed: Comprehensive report with in-depth analysis (uses llama-3.3-70b-versatile model)
- Comprehensive: Exhaustive report with all available information (uses llama-3.3-70b-versatile model)
- Each detail level has specific configuration parameters:
- Number of search results per engine
- Token budget for report generation
- Chunk size and overlap size for document processing
- Recommended LLM model
- Updated the report synthesis module to use different templates based on detail level
- Modified the report generator to automatically configure parameters based on the selected detail level
- Updated the query_to_report.py script to accept a detail_level parameter
- Created test scripts to demonstrate the different detail levels
3. **Report Generation Module Phase 3 Implementation**:
- Integrated with Groq's Llama 3.3 70B Versatile model for report synthesis
- Implemented a map-reduce approach for processing document chunks:
- Map: Process individual chunks to extract key information
@ -16,27 +38,20 @@ We have successfully implemented Phases 1, 2, and 3 of the Report Generation mod
- Implemented Markdown formatting for reports
- Created comprehensive test scripts to verify functionality
2. **LLM Integration Enhancements**:
4. **LLM Integration Enhancements**:
- Created a dedicated ReportSynthesizer class for report generation
- Configured proper integration with Groq and OpenRouter providers
- Implemented error handling and logging throughout the process
- Added support for different query types with automatic detection
3. **Testing Framework Updates**:
- Created a dedicated test script for the report synthesis functionality
- Implemented tests with both sample data and real URLs
- Added support for mock data to avoid API dependencies during testing
- Verified end-to-end functionality from document scraping to report generation
### Current Tasks
1. **Report Generation Module Implementation (Phase 4)**:
- Adding support for alternative models with larger context windows
- 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
- Implementing customizable report detail levels
2. **Integration with UI**:
- Adding report generation options to the UI
@ -46,43 +61,34 @@ We have successfully implemented Phases 1, 2, and 3 of the Report Generation mod
3. **Performance Optimization**:
- Optimizing token usage for more efficient LLM utilization
- Implementing caching strategies for report templates and common queries
- Enhancing parallel processing for the map phase of report generation
- Improving error recovery and retry mechanisms
- Implementing caching strategies for document scraping and LLM calls
- Parallelizing document scraping and processing
- Exploring parallel processing for the map phase of report synthesis
### Next Steps
1. **Complete Phase 4 of Report Generation Module**:
- Implement support for alternative models with larger context windows
- Develop progressive report generation for very large research tasks
- Create visualization components for data mentioned in reports
- Add interactive elements to the generated reports
- Implement report versioning and comparison
- Implement customizable report detail levels with the following options:
- Adjustable number of search results
- Configurable token budget
- Customizable synthesis prompts
- Different report style templates
- Adjustable chunking parameters
- Model selection options
1. **Testing and Refinement of Enhanced Detail Levels**:
- Conduct thorough testing of the enhanced detail level features with various query types
- Compare the analytical depth and quality of reports generated with the new prompts
- Gather user feedback on the improved reports at different detail levels
- Further refine the detail level configurations based on testing and feedback
2. **Enhance UI Integration**:
- Add report generation options to the UI
- Implement progress indicators for document scraping and report generation
- Create visualization components for generated reports
- Add options to customize report generation parameters
2. **Progressive Report Generation**:
- Design and implement a system for generating reports progressively for very large research tasks
- Create a mechanism for updating reports as new information is processed
- Implement a progress tracking system for report generation
3. **Comprehensive Testing and Documentation**:
- Create end-to-end tests for the complete pipeline
- Test with various document types and sizes
- Evaluate performance and optimize as needed
- Create comprehensive documentation for the report generation module
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
### Technical Notes
- Using Groq's Llama 3.3 70B Versatile model for report synthesis
- Implemented map-reduce approach for processing document chunks
- Created report templates for different query types (factual, exploratory, comparative)
- 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

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.note/session_log.md
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### Overview
Fixed Serper API integration in the search execution module, ensuring proper functionality for both regular search and Scholar search.
### Key Activities
1. Fixed the Serper API integration:
- Modified the LLM interface to return only the enhanced query text without explanations
- Updated the query enhancement prompt to be more specific about the desired output format
- Added query truncation to handle long queries (Serper API has a 2048 character limit)
2. Streamlined the search execution process:
- Removed the redundant Google search handler (as Serper serves as a front-end for Google search)
- Fixed the Serper API endpoint URL and request parameters
- Improved error handling for API requests
3. Enhanced result processing:
- Improved the result collector to properly process and deduplicate results from multiple sources
- Added better debug output to help diagnose issues with search results
4. Improved testing:
- Created a dedicated test script for all search handlers
- Added detailed output of search results for better debugging
- Implemented comprehensive testing across multiple queries
### Insights
- The Serper API has a 2048 character limit for queries, requiring truncation for long enhanced queries
- The LLM's tendency to add explanations to enhanced queries can cause issues with search APIs
- Proper error handling is crucial for API integrations, especially when dealing with multiple search engines
- The Scholar handler uses the same Serper API but with a different endpoint (/scholar)
### Challenges
- Managing the length of enhanced queries to stay within API limits
- Ensuring consistent result format across different search engines
- Handling API-specific requirements and limitations
### Next Steps
1. Integrate the search execution module with the query processor
2. Implement the report generation module
3. Develop the Gradio UI for user interaction
4. Test the complete pipeline from query to report
## Session: 2025-02-27 - Gradio UI Implementation
### Overview
Implemented a Gradio web interface for the intelligent research system, providing users with an intuitive way to interact with the system.
### Key Activities
1. Created the Gradio interface:
- Implemented a clean and user-friendly UI design
- Added query input with configurable number of results
- Created a markdown-based result display
- Included example queries for easy testing
2. Integrated with existing modules:
- Connected the UI to the query processor
- Integrated with the search executor
- Used the result collector for processing search results
- Added functionality to save results to JSON files
3. Added project management files:
- Created a comprehensive README.md with project overview and usage instructions
- Set up a git repository with proper .gitignore
- Made an initial commit with all project files
4. Updated documentation:
- Added UI module interfaces to interfaces.md
- Updated current_focus.md with UI development progress
- Added session log entry for UI implementation
### Insights
- Gradio provides a simple yet powerful way to create web interfaces for ML/AI systems
- The modular architecture of our system made UI integration straightforward
- Markdown formatting provides a clean way to display search results
- Saving results to files allows for easier debugging and analysis
### Challenges
- Ensuring a good user experience with potentially slow API calls
- Formatting different types of search results consistently
- Balancing simplicity and functionality in the UI
### Next Steps
1. Enhance the UI with more configuration options
2. Implement report generation in the UI
3. Add visualization components for search results
4. Test the UI with various query types and search engines
## Session: 2025-02-27 (Afternoon)
### Overview
In this session, we focused on debugging and fixing the Jina Reranker API integration to ensure it correctly processes queries and documents, enhancing the relevance of search results.
### Key Activities
1. **Jina Reranker API Integration**:
- Updated the `rerank` method in the JinaReranker class to match the expected API request format
@ -653,3 +566,97 @@ Successfully tested the end-to-end query to report pipeline with a specific quer
3. Develop progressive report generation for very large research tasks
4. Create visualization components for data mentioned in reports
5. Add interactive elements to the generated reports
## Session: 2025-02-28
### Overview
Implemented customizable report detail levels for the Report Generation Module, allowing users to select different levels of detail for generated reports.
### Key Activities
1. **Created Report Detail Levels Module**:
- Implemented a new module `report_detail_levels.py` with an enum for detail levels (Brief, Standard, Detailed, Comprehensive)
- Created a `ReportDetailLevelManager` class to manage detail level configurations
- Defined specific parameters for each detail level (num_results, token_budget, chunk_size, overlap_size, model)
- Added methods to validate and retrieve detail level configurations
2. **Updated Report Synthesis Module**:
- Modified the `ReportSynthesizer` class to accept and use detail level parameters
- Updated synthesis templates to adapt based on the selected detail level
- Adjusted the map-reduce process to handle different levels of detail
- Implemented model selection based on detail level requirements
3. **Enhanced Report Generator**:
- Added methods to set and get detail levels in the `ReportGenerator` class
- Updated the document preparation process to use detail level configurations
- Modified the report generation workflow to incorporate detail level settings
- Implemented validation for detail level parameters
4. **Updated Query to Report Script**:
- Added command-line arguments for detail level selection
- Implemented a `--list-detail-levels` option to display available options
- Updated the main workflow to pass detail level parameters to the report generator
- Added documentation for the new parameters
5. **Created Test Scripts**:
- Updated `test_ev_query.py` to support detail level selection
- Created a new `test_detail_levels.py` script to generate reports with all detail levels for comparison
- Added metrics collection (timing, report size, word count) for comparison
### Insights
- Different detail levels significantly affect report length, depth, and generation time
- The brief level is useful for quick summaries, while comprehensive provides exhaustive information
- Using different models for different detail levels offers a good balance between speed and quality
- Configuring multiple parameters (num_results, token_budget, etc.) together creates a coherent detail level experience
### Challenges
- Ensuring that the templates produce appropriate output for each detail level
- Balancing between speed and quality for different detail levels
- Managing token budgets effectively across different detail levels
- Ensuring backward compatibility with existing code
### Next Steps
1. Conduct thorough testing of the detail level features with various query types
2. Gather user feedback on the quality and usefulness of reports at different detail levels
3. Refine the detail level configurations based on testing and feedback
4. Implement progressive report generation for very large research tasks
5. Develop visualization components for data mentioned in reports
## Session: 2025-02-28 - Enhanced Report Detail Levels
### Overview
In this session, we enhanced the report detail levels to focus more on analytical depth rather than just adding additional sections. We improved the document chunk processing to extract more meaningful information from each chunk for detailed and comprehensive reports.
### Key Activities
1. **Enhanced Template Modifiers for Detailed and Comprehensive Reports**:
- Rewrote the template modifiers to focus on analytical depth, evidence density, and perspective diversity
- Added explicit instructions to prioritize depth over breadth
- Emphasized multi-layered analysis, causal relationships, and interconnections
- Added instructions for exploring second and third-order effects
2. **Improved Document Chunk Processing**:
- Created a new `_get_extraction_prompt` method that provides different extraction prompts based on detail level
- For DETAILED reports: Added focus on underlying principles, causal relationships, and different perspectives
- For COMPREHENSIVE reports: Added focus on multi-layered analysis, complex causal networks, and theoretical frameworks
- Modified the `map_document_chunks` method to pass the detail level parameter
3. **Enhanced MapReduce Approach**:
- Updated the map phase to use detail-level-specific extraction prompts
- Ensured the detail level parameter is passed throughout the process
- Maintained the efficient processing of document chunks while improving the quality of extraction
### Insights
- The MapReduce approach is well-suited for LLM-based report generation, allowing processing of more information than would fit in a single context window
- Different extraction prompts for different detail levels significantly affect the quality and depth of the extracted information
- Focusing on analytical depth rather than additional sections provides more value to the end user
- The enhanced prompts guide the LLM to provide deeper analysis of causal relationships, underlying mechanisms, and interconnections
### Challenges
- Balancing between depth and breadth in detailed reports
- Ensuring that the extraction prompts extract the most relevant information for each detail level
- Managing the increased processing time for detailed and comprehensive reports with enhanced extraction
### Next Steps
1. Conduct thorough testing of the enhanced detail level features with various query types
2. Compare the analytical depth and quality of reports generated with the new prompts
3. Gather user feedback on the improved reports at different detail levels
4. Explore parallel processing for the map phase to reduce overall report generation time
5. Further refine the detail level configurations based on testing and feedback