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Current Focus: FastAPI Implementation, API Testing, and Progressive Report Generation

Active Work

FastAPI Implementation

  • ✅ Created directory structure for FastAPI application following the implementation plan
  • ✅ Implemented core FastAPI application with configuration and security
  • ✅ Created database models for users, searches, and reports
  • ✅ Implemented API routes for authentication, query processing, search execution, and report generation
  • ✅ Created service layer to bridge between API and existing sim-search functionality
  • ✅ Set up database migrations with Alembic
  • ✅ Added comprehensive documentation for the API
  • ✅ Created environment variable configuration
  • ✅ Implemented JWT-based authentication
  • ✅ Added OpenAPI documentation endpoints

API Testing

  • ✅ Created comprehensive test suite for the API using pytest
  • ✅ Implemented test fixtures for database initialization and user authentication
  • ✅ Added tests for authentication, query processing, search execution, and report generation
  • ✅ Created a test runner script with options for verbosity, coverage reporting, and test selection
  • ✅ Implemented a manual testing script using curl commands
  • ✅ Added test documentation with instructions for running tests and troubleshooting
  • ✅ Set up test database isolation to avoid affecting production data
  • ✅ Fixed deprecated Pydantic features to ensure tests run correctly
  • ✅ Replaced dict() with model_dump() in API routes
  • ✅ Updated orm_mode to from_attributes in schema classes
  • ✅ Changed schema_extra to json_schema_extra in schema classes

LLM-Based Query Domain Classification

  • ✅ Implemented LLM-based query domain classification to replace keyword-based approach
  • ✅ Added classify_query_domain method to LLMInterface class
  • ✅ Created _structure_query_with_llm method in QueryProcessor to use LLM classification results
  • ✅ Added fallback to keyword-based classification for resilience
  • ✅ Enhanced structured query with domain, confidence, and reasoning fields
  • ✅ Added comprehensive test script to verify functionality
  • ✅ Added detailed documentation about the new implementation
  • ✅ Updated configuration to support the new classification method
  • ✅ Improved logging for better monitoring of classification results

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
  • ✅ Consolidated redundant progress indicators in the UI to use only Gradio's built-in progress tracking
  • ✅ Fixed model selection issue in report generation to ensure the model selected in the UI is properly used throughout the report generation process
  • ✅ Fixed model provider selection to correctly use the provider specified in the config.yaml file (e.g., ensuring Gemini models use the Gemini provider)
  • ✅ Added detailed logging for model and provider selection to aid in debugging
  • ✅ Implemented comprehensive tests for provider selection stability across multiple initializations, model switches, and configuration changes
  • ✅ Enhanced provider selection stability tests to include fallback mechanisms, edge cases with invalid providers, and provider selection consistency between singleton and new instances
  • ✅ Added test for provider selection stability after config reload
  • ✅ Committed changes with message "Enhanced provider selection stability tests with additional scenarios and edge cases"

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

Recent Changes

API Testing Fixes

  • Fixed deprecated Pydantic features to ensure tests run correctly
  • Replaced dict() with model_dump() in API routes
  • Updated orm_mode to from_attributes in schema classes
  • Changed schema_extra to json_schema_extra in schema classes
  • Made test scripts executable for easier running
  • Committed changes with message "Fix deprecated Pydantic features: replace dict() with model_dump(), orm_mode with from_attributes, and schema_extra with json_schema_extra"

API Testing Implementation

  • Created comprehensive test suite for the API using pytest
  • Implemented test fixtures for database initialization and user authentication
  • Added tests for authentication, query processing, search execution, and report generation
  • Created a test runner script with options for verbosity, coverage reporting, and test selection
  • Implemented a manual testing script using curl commands
  • Added test documentation with instructions for running tests and troubleshooting
  • Set up test database isolation to avoid affecting production data

FastAPI Implementation

  • Created a new sim-search-api directory for the FastAPI application
  • Implemented a layered architecture with API, service, and data layers
  • Created database models for users, searches, and reports
  • Implemented API routes for all functionality
  • Created service layer to bridge between API and existing sim-search functionality
  • Set up database migrations with Alembic
  • Added JWT-based authentication
  • Created comprehensive documentation for the API
  • Added environment variable configuration
  • Implemented OpenAPI documentation endpoints

Directory Structure Reorganization

  • Created a dedicated utils/ directory for utility scripts
    • Moved jina_similarity.py to utils/
    • Added __init__.py to make it a proper Python package
  • 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
  • Added a dedicated scripts/ directory for utility scripts
    • Moved query_to_report.py to scripts/

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 the GradioInterface 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 the ReportGenerator 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

  1. Continue testing the API to ensure all endpoints work correctly
  2. Fix any remaining issues found during testing
  3. Add more specific tests for edge cases and error handling
  4. Integrate the tests into a CI/CD pipeline
  5. Create a React frontend to consume the FastAPI backend
  6. Implement user management in the frontend
  7. Add search history and report management in the frontend
  8. Implement real-time progress tracking for report generation in the frontend
  9. Add visualization components for reports in the frontend
  10. Consider adding more API endpoints for additional functionality

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:

      • ✅ Added a set_progress_callback method to the ReportGenerator 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

    • ✅ 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

  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

  1. API Testing:

    • Continue testing the API to ensure all endpoints work correctly
    • Fix any remaining issues found during testing
    • Add more specific tests for edge cases and error handling
    • Integrate the tests into a CI/CD pipeline

  2. 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

  3. 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

  4. 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

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
  • Implemented FastAPI backend for the sim-search system:
    • Created a layered architecture with API, service, and data layers
    • Implemented JWT-based authentication
    • Created database models for users, searches, and reports
    • Added service layer to bridge between API and existing sim-search functionality
    • Set up database migrations with Alembic
    • Added comprehensive documentation for the API
    • Implemented OpenAPI documentation endpoints
  • Created comprehensive testing framework for the API:
    • Implemented automated tests with pytest for all API endpoints
    • Created a test runner script with options for verbosity and coverage reporting
    • Implemented a manual testing script using curl commands
    • Added test documentation with instructions for running tests and troubleshooting
    • Set up test database isolation to avoid affecting production data
    • Fixed deprecated Pydantic features to ensure tests run correctly