- Added `compare_with_assignments` flag to `ProgressionPathSuggestRequest` to enable comparison of proposed paths with existing slot assignments.
- Introduced `_assignment_preservation_active` function to determine if existing assignments should be preserved during path suggestions.
- Enhanced `suggest_progression_path` to handle comparison logic, including validation for minimum slot assignments required for comparison.
- Implemented `_build_progression_compare_response` to structure the response for comparison results, including slot differences and quality scores.
- Updated frontend components to support new comparison features, including handling of slot assignments and optimization comparisons.
- Bumped version to reflect the new features and improvements.
- Introduced `filter_rematch_slot_indices` to exclude preserved slots from rematching, improving the accuracy of slot assignments.
- Added `_slot_priority_for_rematch` to prioritize existing slot assignments during rematching, enhancing the robustness of the rematch process.
- Updated `_run_roadmap_rematch_loop` to utilize the new filtering and prioritization logic, ensuring better handling of rematch scenarios.
- Enhanced tests in `test_planning_path_rematch.py` to validate the new filtering behavior and ensure correct exercise restoration when not rejected.
- Bumped version to reflect the new features and improvements.
- 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.
- Introduced `_prune_filled_from_roadmap_unfilled` function to remove steps with filled exercises from the unfilled roadmap, preventing outdated references.
- Updated `_run_roadmap_rematch_loop` to incorporate the new pruning logic, ensuring only relevant unfilled steps are retained during rematching.
- Added tests for the pruning function to validate its behavior with various step scenarios.
- Bumped version to 0.8.232 to reflect the new functionality and improvements.
- Introduced `_roadmap_step_passes_post_match_gate` to validate steps after matching, ensuring only relevant steps proceed.
- Enhanced `_enrich_roadmap_unfilled_gap_offers` to generate AI gap offers for unfilled roadmap slots, improving exercise suggestions.
- Updated `suggest_progression_path` to incorporate new gap offer logic and streamline the handling of roadmap steps.
- Refined frontend logic in `applyMatchStepsToSlots` to better manage step assignments and improve clarity in slot handling.
- Bumped version to 0.8.231 to reflect the new features and improvements.
- Introduced `_purge_stage_mismatch_roadmap_slots` to clear slots with persistent stage mismatches, improving the relevance of exercise suggestions.
- Updated `collect_gap_fill_specs` to handle stage mismatch issues more effectively, providing clearer rationale and title hints for off-topic exercises.
- Modified `_filter_learning_goal_candidate_ids` to enforce stricter filtering criteria, ensuring only relevant candidates are considered.
- Enhanced `rematch_roadmap_slots` to incorporate slot assignment history, preventing conflicts with previously assigned exercises.
- Bumped version to 0.8.230 to reflect the new features and improvements.
- Introduced `_peer_stage_learning_goals` to retrieve learning goals from peer stages, enhancing the ability to filter exercises based on cross-slot collisions.
- Added `_filter_learning_goal_candidate_ids` to refine candidate selection by incorporating peer learning goals and stage fit criteria, improving exercise relevance in suggestions.
- Enhanced `pick_best_path_hit` and `_match_roadmap_slot` to utilize peer learning goals for better exercise selection and to prevent conflicts with titles from other stages.
- Updated `stage_refinement_criteria_from_learning_goal` to provide clearer criteria for stage refinement based on learning goals.
- Bumped version to 0.8.229 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.
- Introduced `auto_refine_stage_spec` to `ProgressionPathSuggestRequest`, enabling optional refinement of stage specifications during the rematch process.
- Updated `_run_roadmap_rematch_loop` to incorporate stage specification refinements, logging changes for better tracking of adjustments made during rematching.
- Enhanced `suggest_progression_path` to include refine logs in the output, providing clearer insights into the refinement process.
- Added utility functions for formatting refine log entries, improving the display of refinement actions in the frontend components.
- Updated frontend components to display refine logs, enhancing user feedback on stage specification adjustments during progression analysis.
- Bumped version to 0.8.227 to reflect the new features and improvements.
- Introduced `_step_neighbors_at_index` to safely retrieve neighboring steps without causing IndexErrors, improving robustness in gap fill specifications.
- Updated `collect_gap_fill_specs` to utilize the new neighbor retrieval function, ensuring safe access to adjacent steps during gap fill processing.
- Enhanced rematch logic in `_run_roadmap_rematch_loop` to incorporate `max_rematch_rounds`, allowing for controlled iterations during roadmap rematching.
- Improved handling of unfilled roadmap slots in `collect_rematch_slot_indices`, ensuring accurate identification of gaps in the progression path.
- Added tests to validate the new gap fill handling and rematch logic, ensuring reliability in path suggestion features.
- Updated `suggest_progression_path` to ensure unique gap fill offers are collected and added based on their IDs, improving the relevance of suggestions.
- Refined the logic for setting `slot_status` and handling `gap_offer` and `proposal_key` in steps, enhancing clarity in progression path management.
- Improved the `collectGapOffersFromApiResponse` function to consolidate gap offers from various sources, ensuring comprehensive offer retrieval.
- Enhanced the handling of unfilled slots in `applyMatchStepsToSlots`, ensuring proper assignment of proposals and gap offers.
- Added tests to validate the new logic for gap fill offers and slot assignments, ensuring robustness in path suggestion features.
- Added `_safe_tsquery_fragment` to sanitize learning goal input for SQL queries, improving query safety.
- Introduced `_fetch_learning_goal_library_candidate_ids` to retrieve exercise IDs matching learning goals, enhancing exercise relevance in roadmap suggestions.
- Enhanced `_match_roadmap_slot` to utilize learning goal candidates, improving the accuracy of supplemental exercise selection.
- Implemented `_pick_roadmap_rank_fallback` to provide a fallback mechanism for selecting the best exercise when strict matching fails, ensuring better exercise retrieval.
- Updated tests to validate the new learning goal retrieval and fallback logic, ensuring robustness in exercise selection processes.
- 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.
- Added `preserve_slot_assignments` and `retrieval_boost_exercise_ids` to `ProgressionPathSuggestRequest` for improved handling of exercise suggestions.
- Refactored `_supplemental_exercise_ids_from_body` to incorporate retrieval boost exercise IDs, ensuring they are prioritized over slot assignments.
- Updated `_build_steps_roadmap_first` to conditionally preserve slot assignments based on the new flag.
- Enhanced tests to validate the new retrieval boost logic and its integration with existing slot assignment handling.
- Introduced new functions for handling exercise visibility in progression graphs, including `library_content_visibility_for_progression_graph_sql` to manage visibility based on graph context.
- Added `_supplemental_exercise_ids_from_body` to extract exercise IDs from request bodies, improving data handling in path suggestions.
- Implemented visibility promotion candidate retrieval in the API, allowing for the identification of private exercises that need visibility adjustments when promoting graph visibility.
- Enhanced existing SQL queries and retrieval functions to incorporate new visibility logic, ensuring accurate exercise visibility based on user roles and graph settings.
- Updated frontend components to support visibility promotion workflows, including user prompts for managing private exercises during graph visibility changes.
- Added tests to validate new visibility logic and ensure robustness in exercise retrieval and promotion 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.
- Added `tzdata` installation in the Dockerfile to support time zone handling in Linux environments.
- Increased `PIP_DEFAULT_TIMEOUT` and added retry logic for pip installations to enhance reliability during dependency installation.
- Updated `requirements.txt` to conditionally include `tzdata` for Windows platforms, ensuring compatibility across different operating systems.
- Updated `suggest_progression_path` to include AI-generated gap fill offers when exercises are missing, improving the relevance of suggested paths.
- Introduced a match summary to provide insights on library matches and gap fill offers, enhancing user feedback in the `ProgressionGraphEditor`.
- Refined the `pick_best_path_hit` function to ensure proper handling of roadmap stage matches based on primary topics.
- Added tests to validate the new gap fill offer logic and match summary functionality, ensuring robustness in path suggestion features.
- 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.
- Implemented `_build_evaluate_empty_slot_gap_specs` function to generate gap offer specifications for unfilled roadmap slots in evaluate-only mode.
- Enhanced `ProgressionFindingsPanel` to display AI offers for empty slots and gaps, improving user interaction and clarity.
- Updated `ProgressionGraphEditor` and `ProgressionSlotCard` components to support new functionalities for managing slots and offers.
- Refactored utility functions in `progressionGraphDraft.js` to streamline slot management and offer handling.
- Incremented application version to reflect these updates.
- Introduced EvaluateStepPayload class to facilitate evaluation of exercise steps with optional attributes for AI proposals and roadmap details.
- Added SlotContentEntry and SlotExerciseContent classes to manage exercise content within the progression graph planning artifact.
- Updated GraphPlanningRoadmapArtifact to include new slot contents and last findings attributes for improved data handling.
- Enhanced Exercise Progression Graph Panel with links to the new Slot Editor for streamlined editing of progression graphs.
- Incremented application version to reflect these updates.
- Updated `build_gap_fill_goal_text` to include expected skills in the generated text, improving clarity for users.
- Enhanced `_roadmap_gap_snapshot_for_spec` to incorporate skill expectations from the progression stage, enriching the roadmap context.
- Modified `_annotate_roadmap_step` to append skill expectations to the step reasons, providing additional insights.
- Updated tests to verify the inclusion of expected skills in the gap fill goal text.
- Incremented application version to 0.8.215 to reflect these changes.
- Introduced `build_progression_gap_snapshot` function to create a compact roadmap context for gap exercises, integrating start situation, target state, and stage specifications.
- Updated `build_gap_fill_goal_text` to include roadmap snapshot details, enhancing the context for AI-generated exercises.
- Enhanced `ProgressionPathSuggestRequest` and related components to support new structured inputs for start/target analysis, improving user experience and AI suggestions.
- Incremented application version to 0.8.212 to reflect these changes.
- Added `include_llm_start_target` option to `ProgressionPathSuggestRequest` for improved roadmap suggestions.
- Introduced new classes `StartTargetExtractArtifact` and `StartTargetResolveMeta` to handle LLM extraction results and metadata.
- Implemented `try_llm_start_target_extract` function to extract start and target states from goal queries using LLM.
- Updated `resolve_roadmap_structured_input` to prioritize user inputs, LLM extractions, and regex parsing for start/target resolution.
- Enhanced `ExerciseProgressionPathBuilder` to utilize new structured inputs and display extraction sources.
- Incremented application version to 0.8.211 to reflect these changes.
- Introduced `RoadmapStructuredInput` to encapsulate structured inputs for start situation, target state, and roadmap notes.
- Updated `ProgressionPathSuggestRequest` to include new fields for structured roadmap inputs.
- Implemented parsing logic for goal queries to extract start and target states, enhancing the goal analysis process.
- Enhanced `build_goal_analysis` to utilize structured inputs, improving the clarity and relevance of generated goals.
- Updated the `ExerciseProgressionPathBuilder` component to support new structured input fields, enhancing user experience.
- Incremented application version to 0.8.210 to reflect these changes.
- 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.
- Added support for editable major steps in the roadmap, allowing users to modify phase, learning goals, and order before exercise matching.
- Introduced a new `roadmap_override` feature to facilitate customized retrieval without re-invoking the roadmap AI.
- Updated the `ExerciseProgressionPathBuilder` component to incorporate these new features, enhancing user interaction and flexibility.
- Incremented application version to 0.8.207 to reflect these changes.
- Introduced a roadmap-first approach for retrieval, allowing for structured exercise suggestions based on stage specifications and major steps.
- Added functionality to generate gap-fill offers for unfilled roadmap stages, improving the relevance of exercise recommendations.
- Updated the `ExerciseProgressionPathBuilder` to support the new roadmap-first feature, enhancing user experience with clearer exercise paths.
- Incremented application version to 0.8.206 and updated the database schema version 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.
- Replaced the manual path selection logic with a new `pick_best_path_hit` function to streamline the process of selecting the best exercise based on semantic scores and gating criteria.
- Updated the semantic gating logic to apply a soft penalty for off-topic exercises, improving the flexibility of exercise selection.
- Enhanced the handling of title, summary, and goal parameters in semantic checks to ensure more accurate relevance assessments.
- Incremented version to 0.8.189 and updated changelog to reflect these improvements in planning AI functionality.
- Updated the path selection logic to incorporate semantic gating, ensuring only relevant exercises are considered based on semantic scores.
- Introduced new functions for building path target profiles and resolving semantic skill weights, enhancing the contextual understanding of exercise suggestions.
- Improved the retrieval process by applying dynamic retrieval weights based on semantic strength, refining the accuracy of exercise recommendations.
- Incremented version to 0.8.188 and updated changelog to document these enhancements 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.
- Incremented version to 0.8.185, reflecting the implementation of Phase C3 features.
- Introduced the `POST /api/planning/progression-path-suggest` endpoint for generating exercise progression paths.
- Enhanced the ExerciseProgressionGraphPanel with a new ExerciseProgressionPathBuilder for reviewing and saving paths.
- Updated changelog to document the new capabilities in planning AI functionality.
