Enhanced provider selection stability tests with additional scenarios and edge cases
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# Current Focus: UI Bug Fixes, Project Directory Reorganization, and Embedding Usage
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# Current Focus: LLM-Based Query Classification, UI Bug Fixes, and Project Directory Reorganization
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## Active Work
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### LLM-Based Query Domain Classification
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- ✅ Implemented LLM-based query domain classification to replace keyword-based approach
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- ✅ Added `classify_query_domain` method to `LLMInterface` class
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- ✅ Created `_structure_query_with_llm` method in `QueryProcessor` to use LLM classification results
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- ✅ Added fallback to keyword-based classification for resilience
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- ✅ Enhanced structured query with domain, confidence, and reasoning fields
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- ✅ Added comprehensive test script to verify functionality
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- ✅ Added detailed documentation about the new implementation
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- ✅ Updated configuration to support the new classification method
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- ✅ Improved logging for better monitoring of classification results
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### UI Bug Fixes
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- ✅ Fixed AttributeError in report generation progress callback
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- ✅ Updated UI progress callback to use direct value assignment instead of update method
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- ✅ Enhanced progress callback to use Gradio's built-in progress tracking mechanism for better UI updates during async operations
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- ✅ Consolidated redundant progress indicators in the UI to use only Gradio's built-in progress tracking
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- ✅ Committed changes with message "Enhanced UI progress callback to use Gradio's built-in progress tracking mechanism for better real-time updates during report generation"
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- ✅ Fixed model selection issue in report generation to ensure the model selected in the UI is properly used throughout the report generation process
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- ✅ Fixed model provider selection to correctly use the provider specified in the config.yaml file (e.g., ensuring Gemini models use the Gemini provider)
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- ✅ Added detailed logging for model and provider selection to aid in debugging
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- ✅ Implemented comprehensive tests for provider selection stability across multiple initializations, model switches, and configuration changes
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- ✅ Enhanced provider selection stability tests to include fallback mechanisms, edge cases with invalid providers, and provider selection consistency between singleton and new instances
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- ✅ Added test for provider selection stability after config reload
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- ✅ Committed changes with message "Enhanced provider selection stability tests with additional scenarios and edge cases"
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### Project Directory Reorganization
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- ✅ Reorganized project directory structure for better maintainability
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# Session Log
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## Session: 2025-03-19 - Model Provider Selection Fix in Report Generation
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### Overview
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Fixed an issue with model provider selection in the report generation process, ensuring that the provider specified in the config.yaml file is correctly used throughout the report generation pipeline.
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### Key Activities
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1. Identified the root cause of the model provider selection issue:
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- The model selected in the UI was correctly passed to the report generator
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- However, the provider information was not being properly respected
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- The code was trying to guess the provider based on the model name instead of using the provider from the config
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2. Implemented fixes to ensure proper provider selection:
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- Modified the `generate_completion` method in `ReportSynthesizer` to use the provider from the config file
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- Removed code that was trying to guess the provider based on the model name
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- Added proper formatting for different providers (Gemini, Groq, Anthropic, OpenAI)
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- Enhanced model parameter formatting to handle provider-specific requirements
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3. Added detailed logging:
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- Added logging of the provider and model being used at key points in the process
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- Added logging of the final model parameter and provider being used
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- This helps with debugging any future issues with model selection
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### Insights
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- Different LLM providers have different requirements for model parameter formatting
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- For Gemini models, LiteLLM requires setting `custom_llm_provider` to 'vertex_ai'
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- Detailed logging is essential for tracking model and provider usage in complex systems
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### Challenges
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- Understanding the specific requirements for each provider in LiteLLM
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- Ensuring backward compatibility with existing code
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- Balancing between automatic provider detection and respecting explicit configuration
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### Next Steps
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1. ✅ Test the fix with various models and providers to ensure it works in all scenarios
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2. ✅ Implement comprehensive unit tests for provider selection stability
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3. Update documentation to clarify how model and provider selection works
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### Testing Results
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Created and executed a comprehensive test script (`report_synthesis_test.py`) to verify the model provider selection fix:
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1. **Groq Provider (llama-3.3-70b-versatile)**:
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- Successfully initialized with provider "groq"
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- Completion parameters correctly showed: `'model': 'groq/llama-3.3-70b-versatile'`
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- LiteLLM logs confirmed: `LiteLLM completion() model= llama-3.3-70b-versatile; provider = groq`
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2. **Gemini Provider (gemini-2.0-flash)**:
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- Successfully initialized with provider "gemini"
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- Completion parameters correctly showed: `'model': 'gemini-2.0-flash'` with `'custom_llm_provider': 'vertex_ai'`
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- Confirmed our fix for Gemini models using the correct vertex_ai provider
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## Session: 2025-03-19 - Provider Selection Stability Testing
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### Overview
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Implemented comprehensive tests to ensure provider selection remains stable across multiple initializations, model switches, and direct configuration changes.
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### Key Activities
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1. Designed and implemented a test suite for provider selection stability:
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- Created `test_provider_selection_stability` function in `report_synthesis_test.py`
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- Implemented three main test scenarios to verify provider stability
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- Fixed issues with the test approach to properly use the global config singleton
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2. Test 1: Stability across multiple initializations with the same model
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- Verified that multiple synthesizers created with the same model consistently use the same provider
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- Ensured that provider selection is deterministic and not affected by initialization order
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3. Test 2: Stability when switching between models
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- Tested switching between different models (llama, gemini, claude, gpt) multiple times
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- Verified that each model consistently selects the appropriate provider based on configuration
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- Confirmed that switching back and forth between models maintains correct provider selection
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4. Test 3: Stability with direct configuration changes
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- Tested the system's response to direct changes in the configuration
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- Modified the global config singleton to change a model's provider
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- Verified that new synthesizer instances correctly reflect the updated provider
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- Implemented proper cleanup to restore the original config state after testing
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### Insights
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- The `ReportSynthesizer` class correctly uses the global config singleton for provider selection
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- Provider selection remains stable across multiple initializations with the same model
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- Provider selection correctly adapts when switching between different models
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- Provider selection properly responds to direct changes in the configuration
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- Using a try/finally block for config modifications ensures proper cleanup after tests
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### Challenges
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- Initial approach using a custom `TestSynthesizer` class didn't work as expected
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- The custom class was not correctly inheriting the config instance
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- Switched to directly modifying the global config singleton for more accurate testing
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- Needed to ensure proper cleanup to avoid side effects on other tests
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### Next Steps
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1. Consider adding more comprehensive tests for edge cases (e.g., invalid providers)
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2. Add tests for provider fallback mechanisms when specified providers are unavailable
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3. Document the provider selection process in the codebase for future reference
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## Session: 2025-03-20 - Enhanced Provider Selection Stability Testing
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### Overview
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Expanded the provider selection stability tests to include additional scenarios such as fallback mechanisms, edge cases with invalid providers, provider selection when using singleton vs. creating new instances, and stability after config reload.
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### Key Activities
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1. Enhanced the existing provider selection stability tests with additional test cases:
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- Added Test 4: Provider selection when using singleton vs. creating new instances
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- Added Test 5: Edge case with invalid provider
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- Added Test 6: Provider fallback mechanism
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- Added a new test function: `test_provider_selection_after_config_reload`
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2. Test 4: Provider selection when using singleton vs. creating new instances
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- Verified that the singleton instance and a new instance with the same model use the same provider
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- Confirmed that the `get_report_synthesizer` function correctly handles model changes
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- Ensured consistent provider selection regardless of how the synthesizer is instantiated
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3. Test 5: Edge case with invalid provider
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- Tested how the system handles models with invalid providers
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- Verified that the invalid provider is preserved in the configuration
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- Confirmed that the system doesn't crash when encountering an invalid provider
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- Validated that error logging is appropriate for debugging
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4. Test 6: Provider fallback mechanism
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- Tested models with no explicit provider specified
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- Verified that the system correctly infers a provider based on the model name
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- Confirmed that the default fallback to groq works as expected
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5. Test for provider selection after config reload
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- Simulated a config reload by creating a new Config instance
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- Verified that provider selection remains stable after config reload
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- Ensured proper cleanup of global state after testing
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### Insights
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- The provider selection mechanism is robust across different instantiation methods
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- The system preserves invalid providers in the configuration, which is important for error handling and debugging
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- The fallback mechanism works correctly for models with no explicit provider
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- Provider selection remains stable even after config reload
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- Proper cleanup of global state is essential for preventing test interference
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### Challenges
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- Simulating config reload required careful manipulation of the global config singleton
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- Testing invalid providers required handling expected errors without crashing the tests
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- Ensuring proper cleanup of global state after each test to prevent side effects
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### Next Steps
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1. Document the provider selection process in the codebase for future reference
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2. Consider adding tests for more complex scenarios like provider failover
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3. Explore adding a provider validation step during initialization
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4. Add more detailed error messages for invalid provider configurations
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5. Consider implementing a provider capability check to ensure the selected provider can handle the requested model
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3. **Anthropic Provider (claude-3-opus-20240229)**:
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- Successfully initialized with provider "anthropic"
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- Completion parameters correctly showed: `'model': 'claude-3-opus-20240229'` with `'custom_llm_provider': 'anthropic'`
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- Received a successful response from Claude
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4. **OpenAI Provider (gpt-4-turbo)**:
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- Successfully initialized with provider "openai"
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- Completion parameters correctly showed: `'model': 'gpt-4-turbo'` with `'custom_llm_provider': 'openai'`
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- Received a successful response from GPT-4
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The test confirmed that our fix is working as expected, with the system now correctly:
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1. Using the provider specified in the config.yaml file
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2. Formatting the model parameters appropriately for each provider
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3. Logging the final model parameter and provider for better debugging
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## Session: 2025-03-18 - Model Selection Fix in Report Generation
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### Overview
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Fixed a critical issue with model selection in the report generation process, ensuring that the model selected in the UI is properly used throughout the entire report generation pipeline.
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### Key Activities
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1. Identified the root cause of the model selection issue:
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- The model selected in the UI was correctly extracted and passed to the report generator
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- However, the model was not being properly propagated to all components involved in the report generation process
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- The synthesizers were not being reinitialized with the selected model
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2. Implemented fixes to ensure proper model selection:
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- Modified the `generate_report` method in `ReportGenerator` to reinitialize synthesizers with the selected model
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- Enhanced the `generate_completion` method in `ReportSynthesizer` to double-check and enforce the correct model
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- Added detailed logging throughout the process to track model selection
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3. Added comprehensive logging:
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- Added logging statements to track the model being used at each stage of the report generation process
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- Implemented verification steps to confirm the model is correctly set
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- Enhanced error handling for model initialization failures
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### Insights
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- The singleton pattern used for synthesizers required explicit reinitialization when changing models
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- Model selection needed to be enforced at multiple points in the pipeline
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- Detailed logging was essential for debugging complex asynchronous processes
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### Challenges
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- Tracking model selection through multiple layers of abstraction
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- Ensuring consistent model usage across asynchronous operations
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- Maintaining backward compatibility with existing code
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### Next Steps
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1. Conduct thorough testing with different models to ensure the fix works in all scenarios
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2. Consider adding unit tests specifically for model selection
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3. Explore adding a model verification step at the beginning of each report generation
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4. Document the model selection process in the technical documentation
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## Session: 2025-03-18 - LLM-Based Query Classification Implementation
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### Overview
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Implemented LLM-based query domain classification to replace the keyword-based approach, providing more accurate and adaptable query classification.
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### Key Activities
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1. Implemented LLM-based classification in the Query Processing Module:
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- Added `classify_query_domain` method to `LLMInterface` class
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- Created `_structure_query_with_llm` method in `QueryProcessor`
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- Updated `process_query` to use the new classification approach
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- Added fallback to keyword-based method for resilience
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- Enhanced structured query with domain, confidence, and reasoning fields
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- Updated configuration to support the new classification method
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2. Created comprehensive test suite:
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- Developed `test_domain_classification.py` to test the classification functionality
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- Added tests for raw domain classification, query processor integration, and comparisons with the keyword-based approach
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- Created an integration test to verify how classification affects search engine selection
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- Added support for saving test results to JSON files for analysis
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3. Added detailed documentation:
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- Created `llm_query_classification.md` in the docs directory
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- Documented implementation details, benefits, and future improvements
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- Updated the decision log with the rationale for the change
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- Updated the current_focus.md file with completed tasks
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### Insights
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- LLM-based classification provides more accurate results for ambiguous queries
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- Multi-domain classification with confidence scores effectively handles complex queries
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- Classification reasoning helps with debugging and transparency
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- Fallback mechanism ensures system resilience if the LLM call fails
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- The implementation is adaptable to new topics without code changes
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### Challenges
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- Ensuring consistent output format from the LLM for reliable parsing
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- Balancing between setting appropriate confidence thresholds for secondary domains
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- Maintaining backward compatibility with the existing search executor
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- Handling potential LLM API failures gracefully
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### Next Steps
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1. Run comprehensive tests with a variety of queries to fine-tune the confidence thresholds
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2. Consider adding caching for frequently asked or similar queries to reduce API calls
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3. Explore adding few-shot learning examples in the prompt to improve classification accuracy
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4. Evaluate the potential for expanding beyond the current four domains
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5. Consider exposing classification reasoning in the UI for advanced users
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## Session: 2025-03-17
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### Overview
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@ -24,6 +24,7 @@ from report.report_synthesis import ReportSynthesizer
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async def test_model_provider_selection():
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"""Test that model provider selection works correctly."""
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logger.info("=== Testing basic model provider selection ===")
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# Initialize config
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config = Config()
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@ -81,10 +82,249 @@ async def test_model_provider_selection():
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logger.info(f"===== Test completed for {model_name} with provider {provider} =====\n")
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async def test_provider_selection_stability():
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"""Test that provider selection remains stable across various scenarios."""
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logger.info("\n=== Testing provider selection stability ===")
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# Test 1: Stability across multiple initializations with the same model
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logger.info("\nTest 1: Stability across multiple initializations with the same model")
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model_name = "llama-3.3-70b-versatile"
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provider = "groq"
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# Create multiple synthesizers with the same model
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synthesizers = []
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for i in range(3):
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logger.info(f"Creating synthesizer {i+1} with model {model_name}")
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synthesizer = ReportSynthesizer(model_name=model_name)
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synthesizers.append(synthesizer)
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logger.info(f"Synthesizer {i+1} provider: {synthesizer.model_config.get('provider')}")
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# Verify all synthesizers have the same provider
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providers = [s.model_config.get('provider') for s in synthesizers]
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logger.info(f"Providers across synthesizers: {providers}")
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assert all(p == provider for p in providers), "Provider not stable across multiple initializations"
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logger.info("✅ Provider stable across multiple initializations")
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# Test 2: Stability when switching between models
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logger.info("\nTest 2: Stability when switching between models")
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model_configs = [
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{"name": "llama-3.3-70b-versatile", "provider": "groq"},
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{"name": "gemini-2.0-flash", "provider": "gemini"},
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{"name": "claude-3-opus-20240229", "provider": "anthropic"},
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{"name": "gpt-4-turbo", "provider": "openai"},
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]
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# Test switching between models multiple times
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for _ in range(2): # Do two rounds of switching
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for model_config in model_configs:
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model_name = model_config["name"]
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expected_provider = model_config["provider"]
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logger.info(f"Switching to model {model_name} with expected provider {expected_provider}")
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synthesizer = ReportSynthesizer(model_name=model_name)
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actual_provider = synthesizer.model_config.get('provider')
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logger.info(f"Model: {model_name}, Expected provider: {expected_provider}, Actual provider: {actual_provider}")
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assert actual_provider == expected_provider, f"Provider mismatch for {model_name}: expected {expected_provider}, got {actual_provider}"
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logger.info("✅ Provider selection stable when switching between models")
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# Test 3: Stability with direct configuration changes
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logger.info("\nTest 3: Stability with direct configuration changes")
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test_model = "test-model-stability"
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# Get the global config instance
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from config.config import config as global_config
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# Save original config state
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original_models = global_config.config_data.get('models', {}).copy()
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try:
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# Ensure models dict exists
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if 'models' not in global_config.config_data:
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global_config.config_data['models'] = {}
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# Set up test model with groq provider
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global_config.config_data['models'][test_model] = {
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"provider": "groq",
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"model_name": test_model,
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"temperature": 0.5,
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"max_tokens": 2048,
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"top_p": 1.0
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}
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# Create first synthesizer with groq provider
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logger.info(f"Creating first synthesizer with {test_model} using groq provider")
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synthesizer1 = ReportSynthesizer(model_name=test_model)
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provider1 = synthesizer1.model_config.get('provider')
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logger.info(f"Initial provider for {test_model}: {provider1}")
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# Change the provider in the global config
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global_config.config_data['models'][test_model]["provider"] = "anthropic"
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# Create second synthesizer with the updated config
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logger.info(f"Creating second synthesizer with {test_model} using anthropic provider")
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synthesizer2 = ReportSynthesizer(model_name=test_model)
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provider2 = synthesizer2.model_config.get('provider')
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logger.info(f"Updated provider for {test_model}: {provider2}")
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# Verify the provider was updated
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assert provider1 == "groq", f"Initial provider should be groq, got {provider1}"
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assert provider2 == "anthropic", f"Updated provider should be anthropic, got {provider2}"
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logger.info("✅ Provider selection responds correctly to configuration changes")
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# Test 4: Provider selection when using singleton vs. creating new instances
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logger.info("\nTest 4: Provider selection when using singleton vs. creating new instances")
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from report.report_synthesis import get_report_synthesizer
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# Set up a test model in the config
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test_model_singleton = "test-model-singleton"
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global_config.config_data['models'][test_model_singleton] = {
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"provider": "openai",
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"model_name": test_model_singleton,
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"temperature": 0.7,
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"max_tokens": 1024
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}
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# Get singleton instance with the test model
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logger.info(f"Getting singleton instance with {test_model_singleton}")
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singleton_synthesizer = get_report_synthesizer(model_name=test_model_singleton)
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singleton_provider = singleton_synthesizer.model_config.get('provider')
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logger.info(f"Singleton provider: {singleton_provider}")
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# Create a new instance with the same model
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logger.info(f"Creating new instance with {test_model_singleton}")
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new_synthesizer = ReportSynthesizer(model_name=test_model_singleton)
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new_provider = new_synthesizer.model_config.get('provider')
|
||||
logger.info(f"New instance provider: {new_provider}")
|
||||
|
||||
# Verify both have the same provider
|
||||
assert singleton_provider == new_provider, f"Provider mismatch between singleton and new instance: {singleton_provider} vs {new_provider}"
|
||||
logger.info("✅ Provider selection consistent between singleton and new instances")
|
||||
|
||||
# Test 5: Edge case with invalid provider
|
||||
logger.info("\nTest 5: Edge case with invalid provider")
|
||||
|
||||
# Set up a test model with an invalid provider
|
||||
test_model_invalid = "test-model-invalid-provider"
|
||||
global_config.config_data['models'][test_model_invalid] = {
|
||||
"provider": "invalid_provider", # This provider doesn't exist
|
||||
"model_name": test_model_invalid,
|
||||
"temperature": 0.5
|
||||
}
|
||||
|
||||
# Create a synthesizer with the invalid provider model
|
||||
logger.info(f"Creating synthesizer with invalid provider for {test_model_invalid}")
|
||||
invalid_synthesizer = ReportSynthesizer(model_name=test_model_invalid)
|
||||
invalid_provider = invalid_synthesizer.model_config.get('provider')
|
||||
|
||||
# The provider should remain as specified in the config, even if invalid
|
||||
# This is important for error handling and debugging
|
||||
logger.info(f"Provider for invalid model: {invalid_provider}")
|
||||
assert invalid_provider == "invalid_provider", f"Invalid provider should be preserved, got {invalid_provider}"
|
||||
logger.info("✅ Invalid provider preserved in configuration")
|
||||
|
||||
# Test 6: Provider fallback mechanism
|
||||
logger.info("\nTest 6: Provider fallback mechanism")
|
||||
|
||||
# Create a model with no explicit provider
|
||||
test_model_no_provider = "test-model-no-provider"
|
||||
global_config.config_data['models'][test_model_no_provider] = {
|
||||
# No provider specified
|
||||
"model_name": test_model_no_provider,
|
||||
"temperature": 0.5
|
||||
}
|
||||
|
||||
# Create a synthesizer with this model
|
||||
logger.info(f"Creating synthesizer with no explicit provider for {test_model_no_provider}")
|
||||
no_provider_synthesizer = ReportSynthesizer(model_name=test_model_no_provider)
|
||||
|
||||
# The provider should be inferred based on the model name
|
||||
fallback_provider = no_provider_synthesizer.model_config.get('provider')
|
||||
logger.info(f"Fallback provider for model with no explicit provider: {fallback_provider}")
|
||||
|
||||
# Since our test model name doesn't match any known pattern, it should default to groq
|
||||
assert fallback_provider == "groq", f"Expected fallback to groq, got {fallback_provider}"
|
||||
logger.info("✅ Provider fallback mechanism works correctly")
|
||||
|
||||
finally:
|
||||
# Restore original config state
|
||||
global_config.config_data['models'] = original_models
|
||||
|
||||
async def test_provider_selection_after_config_reload():
|
||||
"""Test that provider selection remains stable after config reload."""
|
||||
logger.info("\n=== Testing provider selection after config reload ===")
|
||||
|
||||
# Get the global config instance
|
||||
from config.config import config as global_config
|
||||
from config.config import Config
|
||||
|
||||
# Save original config state
|
||||
original_models = global_config.config_data.get('models', {}).copy()
|
||||
original_config_path = global_config.config_path
|
||||
|
||||
try:
|
||||
# Set up a test model
|
||||
test_model = "test-model-config-reload"
|
||||
if 'models' not in global_config.config_data:
|
||||
global_config.config_data['models'] = {}
|
||||
|
||||
global_config.config_data['models'][test_model] = {
|
||||
"provider": "anthropic",
|
||||
"model_name": test_model,
|
||||
"temperature": 0.5
|
||||
}
|
||||
|
||||
# Create a synthesizer with this model
|
||||
logger.info(f"Creating synthesizer with {test_model} before config reload")
|
||||
synthesizer_before = ReportSynthesizer(model_name=test_model)
|
||||
provider_before = synthesizer_before.model_config.get('provider')
|
||||
logger.info(f"Provider before reload: {provider_before}")
|
||||
|
||||
# Simulate config reload by creating a new Config instance
|
||||
logger.info("Simulating config reload...")
|
||||
new_config = Config(config_path=original_config_path)
|
||||
|
||||
# Add the same test model to the new config
|
||||
if 'models' not in new_config.config_data:
|
||||
new_config.config_data['models'] = {}
|
||||
|
||||
new_config.config_data['models'][test_model] = {
|
||||
"provider": "anthropic", # Same provider
|
||||
"model_name": test_model,
|
||||
"temperature": 0.5
|
||||
}
|
||||
|
||||
# Temporarily replace the global config
|
||||
from config.config import config
|
||||
original_config = config
|
||||
import config.config
|
||||
config.config.config = new_config
|
||||
|
||||
# Create a new synthesizer after the reload
|
||||
logger.info(f"Creating synthesizer with {test_model} after config reload")
|
||||
synthesizer_after = ReportSynthesizer(model_name=test_model)
|
||||
provider_after = synthesizer_after.model_config.get('provider')
|
||||
logger.info(f"Provider after reload: {provider_after}")
|
||||
|
||||
# Verify the provider remains the same
|
||||
assert provider_before == provider_after, f"Provider changed after config reload: {provider_before} vs {provider_after}"
|
||||
logger.info("✅ Provider selection stable after config reload")
|
||||
|
||||
finally:
|
||||
# Restore original config state
|
||||
global_config.config_data['models'] = original_models
|
||||
# Restore original global config
|
||||
if 'original_config' in locals():
|
||||
config.config.config = original_config
|
||||
|
||||
async def main():
|
||||
"""Main function to run tests."""
|
||||
logger.info("Starting report synthesis tests...")
|
||||
await test_model_provider_selection()
|
||||
await test_provider_selection_stability()
|
||||
await test_provider_selection_after_config_reload()
|
||||
logger.info("All tests completed.")
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
|
|||
Loading…
Reference in New Issue