- Introduced `planning_catalog_context` to `ProgressionPathSuggestRequest` for improved handling of catalog-related data during path suggestions.
- Implemented `_resolve_planning_catalog_context` to retrieve and validate the planning catalog context, enhancing the robustness of the suggestion process.
- Updated `_build_path_target_profile` to incorporate catalog context, enriching target profiles with relevant planning data.
- Enhanced frontend components in `ProgressionGraphEditor` to manage and display planning catalog context, including new selection options for focus areas, style directions, training types, and target groups.
- Added utility functions for parsing and transforming planning catalog context data for API interactions.
- Bumped version to 0.8.233 to reflect the new features and improvements.
- Updated `max_rematch_rounds` in `ProgressionPathSuggestRequest` to allow for a maximum of 3 rounds, improving flexibility in rematch processes.
- Introduced `_track_rejected` function to track rejected exercises by major step index, enhancing the rematch logic to account for previously rejected exercises.
- Enhanced `_run_roadmap_rematch_loop` to utilize the new rejection tracking, ensuring better handling of off-topic steps during rematching.
- Improved `detect_off_topic_steps` to incorporate refined scoring and reasoning for stage fit, enhancing the accuracy of off-topic detection.
- Updated `refine_stage_spec_artifact` to merge stage exclusion phrases more effectively, improving the clarity of anti-pattern handling.
- Bumped version to 0.8.228 to reflect the new features and improvements.
- Updated `_annotate_roadmap_step` to change the condition for setting `slot_status` based on `roadmap_match_source`, improving clarity in slot assignment handling.
- Removed the `_try_reconcile_slot_assignment` function to streamline the slot assignment process, as its logic is now integrated into the main flow.
- Enhanced `_match_roadmap_slot` to conditionally preserve slot assignments based on exercise ID, ensuring better handling of existing assignments.
- Improved the handling of semantic scores in `rank_visible_library_hits` to prioritize the best semantic fit, enhancing exercise retrieval accuracy.
- Added tests to validate the new logic for title equivalence and semantic scoring, ensuring robustness in exercise selection processes.
- Introduced new functions for handling exercise visibility and retrieval based on progression graph context, including `fetch_exercise_rows_by_ids_for_graph`.
- Updated `_load_supplemental_exercise_rows` to incorporate graph visibility rules, improving the accuracy of exercise retrieval.
- Enhanced `_run_path_step_retrieval` to utilize preloaded supplemental exercise rows, optimizing performance and clarity in path step processing.
- Added `exercise_title_equivalent_to_stage_goal` function to improve title matching against learning goals, enhancing exercise relevance.
- Updated tests to validate new retrieval logic and title equivalence functionality, ensuring robustness in exercise selection processes.
- Introduced `slot_assignments` to `ProgressionPathSuggestRequest` for improved handling of existing slot assignments in path building.
- Implemented `_slot_assignments_by_major_index` and `_path_step_from_slot_assignment` functions to facilitate the integration of slot assignments into the path generation process.
- Updated `_build_steps_roadmap_first` to utilize slot assignments, enhancing the accuracy of path steps based on existing exercise slots.
- Enhanced `detect_path_gaps` to skip empty slots, preventing unnecessary errors during gap detection.
- Added tests to validate the new slot assignment handling and ensure robustness in path generation logic.
- Introduced `resolve_path_primary_topic` function to enhance the determination of primary topics from goal queries and semantic briefs, improving exercise relevance.
- Updated `_match_roadmap_slot` and `detect_off_topic_steps` functions to utilize the new primary topic resolution logic, ensuring accurate topic identification.
- Enhanced tests to validate the functionality of primary topic resolution and its impact on exercise selection and off-topic detection.
- Improved handling of primary topics in the `ExerciseProgressionPathBuilder` and related components for better integration with the overall path-building process.
- Improved off-topic step handling by incorporating roadmap major step indices for better indexing and detection.
- Refactored `collect_gap_fill_specs` to streamline the insertion logic for off-topic steps, ensuring correct placement based on major step indices.
- Introduced `_normalize_roadmap_steps_coverage` function to standardize roadmap steps coverage, enhancing the handling of missing slots.
- Added `prune_stripped_after_rematch` function to clean up stripped off-topic steps after rematching, improving the overall rematching process.
- Updated tests to validate new rematching and off-topic handling features, ensuring robustness against edge cases.
- Incremented application version to reflect these updates.
- Introduced `auto_rematch_after_qa` parameter in `ProgressionPathSuggestRequest` to enable automatic rematching after quality assurance checks.
- Refactored roadmap slot matching logic to improve clarity and functionality, renaming `_build_steps_roadmap_first` to `_match_roadmap_slot`.
- Added `_with_roadmap_major_index` utility to streamline off-topic step detection by incorporating roadmap major step indices.
- Enhanced off-topic detection logic to utilize the new utility for improved clarity in identifying mismatches and exclusions.
- Incremented application version to reflect these updates.
- Introduced multistage path quality assurance (QA) functionality to improve exercise relevance and feedback through structured tiers and optimization hints.
- Updated stage specifications to include `start_state` and `target_state` for better contextualization in roadmap matching.
- Enhanced semantic brief construction with technique sibling exclusions to refine exercise selection based on primary topics.
- Improved path retrieval logic to incorporate new parameters for nuanced matching against learning goals.
- Incremented application version to reflect these updates.
- Introduced `resolve_path_anti_patterns` to improve handling of path exclusions based on explicit negations and semantic briefs.
- Updated `enrich_brief_with_path_constraints` to incorporate path-specific exclusions into semantic briefs, enhancing exercise relevance.
- Modified roadmap step annotation to allow for anti-pattern overrides, improving flexibility in exercise selection.
- Enhanced tests to validate new path exclusion features and ensure correct functionality against learning goals.
- Incremented application version to reflect these updates.
- Introduced `build_stage_match_brief` to create stage-specific semantic briefs, improving roadmap matching accuracy.
- Updated path retrieval logic to differentiate between general and stage-specific semantic weights, enhancing exercise relevance.
- Added support for anti-patterns and success criteria in stage matching, allowing for more nuanced exercise selection.
- Enhanced tests to validate new stage matching features and ensure correct functionality against learning goals.
- Incremented application version to reflect these updates.
- Added `semantic_brief_for_stage` function to enhance semantic briefs with stage learning goals for improved roadmap matching.
- Introduced `exercise_passes_stage_learning_goal_gate` to validate exercises against stage learning goals, enhancing relevance checks.
- Updated path retrieval and scoring logic to incorporate stage learning goals, allowing for more nuanced exercise selection.
- Enhanced UI to indicate weak matches with stage learning goals, improving user feedback on exercise relevance.
- Incremented application version to reflect these updates.
- Introduced `roadmap_qa_mode` to manage QA behavior based on roadmap-first logic, improving gap detection between major steps.
- Updated `detect_path_gaps` to skip gaps for roadmap-planned neighbor pairs, enhancing the accuracy of path assessments.
- Added new helper function `is_roadmap_planned_neighbor_pair` to facilitate roadmap neighbor checks.
- Updated relevant tests to validate new functionality and ensure robustness.
- Incremented application version to 0.8.209 to reflect these changes.
- Introduced a roadmap-first approach for the planning AI, allowing for a structured progression graph that aligns with the overall project roadmap.
- Added new functionality to strip off-topic steps from exercise paths, improving the relevance of generated exercise suggestions.
- Implemented a detailed goal text generation for AI proposals, enhancing the context provided for new exercises.
- Updated the ExerciseProgressionPathBuilder component to support new features, including roadmap previews and improved focus area handling.
- Incremented application version to 0.8.205 and updated database schema version to 20260606086 to reflect these changes.
- Updated the AI gap filling logic to include structured offers for unfilled gaps, improving the user experience in the Exercise Progression Path Builder.
- Introduced new functions for detecting off-topic steps and parsing LLM-suggested exercises, enhancing the contextual relevance of exercise suggestions.
- Enhanced the frontend components to support new AI proposal features, including quick creation modals for newly suggested exercises.
- Incremented version to 0.8.190 and updated changelog to reflect these improvements in planning AI functionality.
- Introduced path reordering functionality using LLM with `ordered_step_indices`, allowing for dynamic adjustment of exercise progression paths.
- Added AI gap filling capabilities, enabling the system to propose new exercises when unbridgeable gaps are detected.
- Updated the backend to support new request parameters for path reordering and AI gap filling.
- Enhanced frontend components to reflect these new features, including alerts for AI proposals and adjustments in exercise display.
- Incremented version to 0.8.187 and updated changelog to document these significant enhancements in planning AI functionality.
- Introduced new functions to load exercise goals and variant names in chunks, improving data retrieval efficiency.
- Integrated semantic scoring into the ranking logic, allowing for more nuanced exercise suggestions based on semantic relevance.
- Updated the planning exercise suggestion process to include semantic brief handling, enriching the context for exercise recommendations.
- Adjusted the retrieval phase to incorporate dynamic retrieval weights based on semantic strength, enhancing the overall suggestion accuracy.
- Incremented version to 0.8.186 and updated changelog to reflect these significant enhancements in planning AI functionality.
