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Commit History
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feat: Implement validation for canonical fixture and add training suite
- Added `validate_canonical_fixture.py` to check required files and patterns in the sample project. - Created unit tests in `test_canonical_fixture.py` to ensure fixture validation works as expected. - Developed `test_phase8_training_api.py` to test training API endpoints and their responses. - Introduced `TrainingRunManager` and `WeightSafetyManager` in `run_manager.py` and `weights.py` for managing training runs and model weights. - Established a new directory structure under `training/` for organizing training-related code. - Documented the major refactor plan for transitioning to a deterministic ground truth and training suite in `phase-07-major-refactor-static-ground-truth-training-suite-plan.md`. - Outlined the completion plan for deterministic ground truth and agent training integration in `phase-08-deterministic-ground-truth-agent-training-completion-plan.md`.babc153
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feat: Enhance module resolution and grading configuration
- Added new environment variables for remote SQLite configuration. - Improved module ID resolution logic in GraphManager. - Updated grading system to include new parameters and fallback mechanisms. - Enhanced observation and state models to include module summaries and review details. - Modified task run response to track module progress during execution.bc02c39 -
feat: Implement task registry and grading system
- Added task specifications for easy, medium, and hard tasks with detailed configurations. - Introduced a task registry to manage task specifications and retrieval. - Developed grading logic for easy, medium, and hard tasks, ensuring deterministic behavior. - Created action and reward models to facilitate grading and action validation. - Established a review runner to execute grading and persist results. - Implemented environment state management for tracking module reviews and episode states. - Added tests for graders and environment interactions to ensure correctness and determinism. - Set up FastAPI server endpoints for health checks and task retrieval. - Documented phase objectives and design principles for clarity and future reference.b196357 -
feat: initialize CodeReviewEnv with foundational components
- Add Dockerfile for containerized environment setup. - Create README.md with quickstart instructions. - Implement database package with migrations and schema definitions. - Develop store module for database interactions and data management. - Introduce parser module for AST parsing and code analysis. - Establish environment and graph management for dependency tracking. - Set up grading and task management placeholders for future phases. - Include sample codebase and ground truth for testing and validation. - Add tests for environment, parser, and graph functionalities.cf05092