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Update memory bank with Gradio UI fixes and future enhancement plans

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Steve White 2025-02-28 10:25:24 -06:00
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.note/current_focus.md
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@ -28,21 +28,41 @@ We have successfully implemented Phases 1, 2, and 3 of the Report Generation mod
- 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
- Reduce: Synthesize extracted information into a coherent report
- Created report templates for different query types (factual, exploratory, comparative)
- Added citation generation and reference management
- Implemented Markdown formatting for reports
- Created comprehensive test scripts to verify functionality
3. **Gradio UI Enhancements**:
- Updated the Gradio interface to include report generation with detail levels
- Added custom model selection for report generation
- Implemented processing of thinking tags in the model output
- Fixed method names and improved query processing for search execution
- Enhanced error handling for report generation
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
### 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**:
- Modify the `report_synthesis.py` file to add logging during the map phase of the map-reduce process
- Add a counter variable to track which chunk is being processed
- Use the existing logging infrastructure to output progress messages in the UI
- Estimated difficulty: Easy (15-30 minutes of work)
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

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.note/session_log.md
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@ -165,154 +165,13 @@ Implemented module-specific model configuration and created the Jina AI Reranker
### Next Steps
1. Implement the remaining query processing components
2. Create the Gradio UI for user interaction
3. Develop the search execution module to integrate with search APIs
3. Test the full system with end-to-end workflows
## Session: 2025-02-27 (Update 5)
### Overview
Added support for OpenRouter and Groq as LLM providers and configured the system to use Groq for testing.
### Key Activities
1. Enhanced configuration:
- Added API key configurations for OpenRouter and Groq
- Added model configurations for Groq's Llama models (3.1-8b-instant and 3.3-70b-versatile)
- Added model configurations for OpenRouter's models (Mixtral and Claude)
- Updated default model to use Groq's Llama 3.1-8b-instant for testing
2. Updated LLM Interface:
- Enhanced the `_get_completion_params` method to handle Groq and OpenRouter providers
- Added special handling for OpenRouter's HTTP headers
- Updated the API key retrieval to support the new providers
3. Configured module-specific models:
- Set most modules to use Groq's Llama 3.1-8b-instant model for testing
- Kept Jina's reranker for document reranking
- Set report synthesis to use Groq's Llama 3.3-70b-versatile model for higher quality
### Insights
- Using Groq for testing provides fast inference times with high-quality models
- OpenRouter offers flexibility to access various models through a single API
- The modular approach allows for easy switching between different providers
### Next Steps
1. Test the system with Groq's models to evaluate performance
2. Implement the remaining query processing components
3. Create the Gradio UI for user interaction
4. Test the full system with end-to-end workflows
## Session: 2025-02-27 (Update 6)
### Overview
Tested the query processor module with Groq models to ensure functionality with the newly integrated LLM providers.
### Key Activities
1. Created test scripts for the query processor:
- Developed a basic test script (`test_query_processor.py`) to verify the query processing pipeline
- Created a comprehensive test script (`test_query_processor_comprehensive.py`) to test all aspects of query processing
- Implemented monkey patching to ensure tests use the Groq models
2. Verified query processor functionality:
- Tested query enhancement with Groq's Llama 3.1-8b-instant model
- Tested query classification with structured output
- Tested search query generation for multiple search engines
- Confirmed the entire processing pipeline works end-to-end
3. Resolved integration issues:
- Fixed configuration loading to properly use the Groq API key
- Ensured LLM interface correctly initializes with Groq models
- Verified that the query processor correctly uses the LLM interface
### Insights
- Groq's Llama 3.1-8b-instant model performs well for query processing tasks with fast response times
- The modular design allows for easy switching between different LLM providers
- The query processor successfully enhances queries by adding context and structure
- Query classification provides useful metadata for downstream processing
### Next Steps
1. Implement the search execution module to integrate with search APIs
2. Create the Gradio UI for user interaction
3. Test the full system with end-to-end workflows
## Session: 2025-02-27 - Comprehensive Testing of Query Processor
### Objectives
- Create a comprehensive test script for the query processor
- Test all aspects of the query processor with various query types
- Document the testing approach and results
### Accomplishments
1. Created a comprehensive test script (`test_query_processor_comprehensive.py`):
- Implemented tests for query enhancement in isolation
- Implemented tests for query classification in isolation
- Implemented tests for the full processing pipeline
- Implemented tests for search query generation
- Added support for saving test results to a JSON file
2. Tested a variety of query types:
- Factual queries (e.g., "What is quantum computing?")
- Comparative queries (e.g., "Compare blockchain and traditional databases")
- Domain-specific queries (e.g., "Explain the implications of blockchain in finance")
- Complex queries with multiple aspects
3. Documented the testing approach:
- Updated the decision log with the testing strategy
- Added test script descriptions to the code structure document
- Added a section about query processor testing to the interfaces document
- Updated the project overview to reflect the current status
### Insights
- The query processor successfully handles a wide range of query types
- The Groq model provides consistent and high-quality results for all tested functions
- The monkey patching approach allows for effective testing without modifying core code
- Saving test results to a JSON file provides a valuable reference for future development
### Next Steps
1. Implement the search execution module to integrate with search APIs
2. Create the Gradio UI for user interaction
3. Test the full system with end-to-end workflows
## Session: 2025-02-27 - Search Execution Module Implementation
### Objectives
- Implement the search execution module to execute queries across multiple search engines
- Create handlers for different search APIs
- Develop a result collector for processing and organizing search results
- Create a test script to verify functionality
### Accomplishments
1. Created a modular search execution framework:
- Implemented a base handler interface (`BaseSearchHandler`) for all search API handlers
- Created handlers for Google Search, Serper, Google Scholar, and arXiv
- Developed a `SearchExecutor` class for managing search execution across multiple engines
- Implemented parallel search execution using thread pools for efficiency
2. Implemented a comprehensive result processing system:
- Created a `ResultCollector` class for processing and organizing search results
- Added functionality for deduplication, scoring, and sorting of results
- Implemented filtering capabilities based on various criteria
- Added support for saving and loading results to/from files
3. Created a test script for the search execution module:
- Integrated with the query processor to test the full pipeline
- Added support for testing with multiple query types
- Implemented result saving for analysis
### Insights
- The modular design allows for easy addition of new search engines
- Parallel execution significantly improves search performance
- Standardized result format simplifies downstream processing
- The search execution module integrates seamlessly with the query processor
### Next Steps
1. Test the search execution module with real API keys and live search engines
2. Develop the Gradio UI for user interaction
3. Implement the report generation module
## Session: 2025-02-27 - Serper API Integration Fixes
### Overview
Fixed Serper API integration in the search execution module, ensuring proper functionality for both regular search and Scholar search.
### Key Activities
1. **Jina Reranker API Integration**:
- Updated the `rerank` method in the JinaReranker class to match the expected API request format
@ -620,6 +479,7 @@ Implemented customizable report detail levels for the Report Generation Module,
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
@ -660,3 +520,50 @@ In this session, we enhanced the report detail levels to focus more on analytica
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
## Session: 2025-02-28 - Gradio UI Enhancements and Future Planning
### Overview
In this session, we fixed issues in the Gradio UI for report generation and planned future enhancements to improve search quality and user experience.
### Key Activities
1. **Fixed Gradio UI for Report Generation**:
- Updated the `generate_report` method in the Gradio UI to properly process queries and generate structured queries
- Integrated the `QueryProcessor` to create structured queries from user input
- Fixed method calls and parameter passing to the `execute_search` method
- Implemented functionality to process `<thinking>` tags in the generated report
- Added support for custom model selection in the UI
- Updated the interfaces documentation to include ReportGenerator and ReportDetailLevelManager interfaces
2. **Planned Future Enhancements**:
- **Multiple Query Variation Generation**:
- Designed an approach to generate several similar queries with different keywords for better search coverage
- Planned modifications to the QueryProcessor and SearchExecutor to handle multiple queries
- Estimated this as a moderate difficulty task (3-4 days of work)
- **Threshold-Based Reranking with Larger Document Sets**:
- Developed a plan to process more initial documents and use reranking to select the most relevant ones
- Designed new detail level configuration parameters for initial and final result counts
- Estimated this as an easy to moderate difficulty task (2-3 days of work)
- **UI Progress Indicators**:
- Identified the need for chunk processing progress indicators in the UI
- Planned modifications to report_synthesis.py to add logging during document processing
- Estimated this as a simple enhancement (15-30 minutes of work)
### Insights
- The modular architecture of the system makes it easy to extend with new features
- Providing progress indicators during report generation would significantly improve user experience
- Generating multiple query variations could substantially improve search coverage and result quality
- Using a two-stage approach (fetch more, then filter) for document retrieval would likely improve report quality
### Challenges
- Balancing between fetching enough documents for comprehensive coverage and maintaining performance
- Ensuring proper deduplication when using multiple query variations
- Managing the increased API usage that would result from processing more queries and documents
### Next Steps
1. Implement the chunk processing progress indicators as a quick win
2. Begin work on the multiple query variation generation feature
3. Test the current implementation with various query types to identify any remaining issues
4. Update the documentation to reflect the new features and future plans