- Added new fields for goal query, user notes, max steps, and search query in the AiPromptPreviewBody to support planning prompts.
- Integrated planning prompt handling in the preview_ai_prompt function, allowing for distinct processing of planning and exercise prompts.
- Introduced LLM usage tracking in openrouter_chat_completion and planning_exercise_suggest functions to monitor AI call metrics.
- Updated frontend components to accommodate new input fields for planning prompts, enhancing user experience and functionality.
- Added a new function `_graph_promotion_transition` to determine the necessary exercise visibility changes during graph promotions.
- Updated the `list_visibility_promotion_candidates` endpoint to utilize the new promotion logic, ensuring accurate exercise visibility handling.
- Enhanced the frontend components to prompt users for exercise visibility adjustments based on graph visibility changes, improving user experience.
- Introduced tests for the new promotion logic to ensure correctness and reliability in visibility transitions.
- Updated `PROJECT_STATUS.md` to reflect the implementation of F15 features, including the unified slot review and handling of `findings_stale`.
- Enhanced `PROGRESSION_GRAPH_SLOT_EDITOR_SPEC.md` with detailed descriptions of new functionalities related to the match dialog and path quality assessments.
- Introduced new functions in `exercise_progression_graphs.py` to validate exercise visibility against progression graph settings, ensuring proper governance.
- Improved frontend components to support new governance parameters (visibility and club_id) in exercise creation workflows.
- Updated documentation in `HANDOVER.md` and `PLANNING_KI_ROADMAP.md` to outline the latest developments and validation results for the F15 features.
- Enhanced utility functions for exercise creation to incorporate governance settings, improving the overall user experience in the path builder and editor.
- Updated the key prop in the ProgressionSlotCard component to use a simpler index-based key, enhancing the stability of component rendering during updates.
- This change aims to prevent potential issues with component re-renders and improve overall performance in the ProgressionGraphEditor.
- Introduced `findings_stale` field in `GraphPlanningRoadmapArtifact` to track the freshness of findings.
- Updated `ProgressionGraphEditor` to manage `findingsStale` state across various functions, ensuring accurate representation of evaluation status.
- Modified related utility functions and tests to accommodate the new state, enhancing overall functionality and user feedback in the progression graph management process.
- Added `evaluationStale` state to `ProgressionGraphEditor` and `ProgressionFindingsPanel` to track the freshness of evaluations.
- Updated UI to display a warning when evaluations are stale, prompting users to re-evaluate the graph.
- Modified loading and evaluation functions to manage the `evaluationStale` state effectively, ensuring accurate user feedback during the evaluation process.
- Improved user notifications regarding the need for re-evaluation after changes to the graph.
- Introduced `_build_off_topic_slot_gap_spec` to generate specifications for off-topic slots, improving the handling of filled but thematically inappropriate slots.
- Added `_build_unified_slot_review_entry` to streamline the review process for slots, incorporating various parameters for better evaluation and suggestions.
- Enhanced existing logic in slot management to improve the robustness of path evaluations and user feedback.
- Added tests for the new off-topic slot gap specification to ensure functionality and correctness.
- Introduced new functions `_off_topic_semantic_scores_by_slot` and `_score_exercise_stage_fit_for_spec` to improve the evaluation of off-topic steps and exercise stage fit, enhancing the quality assessment process.
- Updated `_run_unified_slot_improvement_review` to incorporate off-topic scores and exercise stage fit scoring, refining the decision-making process for slot suggestions.
- Enhanced existing logic to streamline the handling of slot scores and improve the overall robustness of slot management in path evaluations.
- Introduced `_parse_slot_refs_from_text` to extract and convert slot references from text, improving the handling of user input in path evaluations.
- Updated `_problematic_slots_from_path_qa` to utilize the new parsing function, enhancing the identification of problematic slots based on various hints and issues.
- Enhanced `ProgressionGraphEditor` and `ProgressionOptimizeCompareModal` to better display identified problem slots and their associated reasons, improving user feedback during evaluations.
- Added tests for new parsing functionality and its integration with existing slot management processes, ensuring robustness in slot reference handling.
- Introduced `_resolve_hint_major_index` to accurately map hints to major step indices, improving the handling of optimization hints in path evaluations.
- Added `_problematic_slots_from_path_qa` to identify and categorize problematic slots based on baseline QA, enhancing the quality assessment process.
- Updated `_slot_suggestion_accepted` to incorporate new parameters for slot problems and stage specifications, refining the decision-making process for slot suggestions.
- Enhanced `ProgressionGraphEditor` to improve user notifications regarding identified issues and suggestions, ensuring clearer communication of path evaluation results.
- Modified `buildProgressionComparePayload` and `buildUnifiedSlotReviewComparePayload` to support baseline evaluations, streamlining the comparison process for proposed paths.
- Added `_quick_evaluate_steps_qa` function to streamline path quality assessment without recursive API calls, enhancing performance for slot comparisons.
- Introduced `compute_deterministic_path_quality_score` to provide a heuristic quality score based on gaps and off-topic steps, improving evaluation accuracy.
- Updated `_run_unified_slot_improvement_review` to utilize the new quick evaluation method, optimizing the review process and integrating quality scoring.
- Enhanced `build_path_qa_summary` to include quality score calculations, ensuring comprehensive feedback on path evaluations.
- Refactored related functions for improved clarity and efficiency in handling path quality assessments.
- Introduced new fields in `ProgressionPathSuggestRequest` for baseline evaluation and incremental scoring, improving the assessment of proposed paths.
- Implemented `_apply_slot_diff_to_steps` and `_score_incremental_slot_diffs` functions to manage slot differences and evaluate their impact on quality scores.
- Updated `ProgressionGraphEditor` to streamline the match comparison flow, integrating new evaluation parameters and improving user notifications.
- Enhanced `ProgressionOptimizeCompareModal` to better display proposed path suggestions, including pro/con evaluations and quality delta metrics.
- Refactored utility functions for clearer handling of slot differences and improved overall data management in the progression graph editor.
- Introduced new utility functions for comparing slot differences, including `compareDiffKind`, `annotateCompareDiffKinds`, and various filtering functions to streamline the comparison process.
- Updated `ProgressionGraphEditor` to utilize the new comparison logic, improving the handling of slot differences and user notifications.
- Enhanced `ProgressionOptimizeCompareModal` to better manage proposed path suggestions, including clearer messaging and improved selection handling for optional replacements.
- Adjusted frontend components to reflect changes in comparison logic, ensuring a more intuitive user experience in managing progression paths.
- Introduced `buildProgressionComparePayload` to create a structured comparison response from baseline and proposed evaluation results, enhancing clarity in slot differences.
- Refactored `fetchMatchCompare` to `fetchFullMatch` for improved clarity and functionality in fetching progression paths.
- Updated `runMatchCompareFlow` to streamline the evaluation process, integrating baseline and match results for a comprehensive comparison.
- Enhanced utility functions for managing slot differences and gap fill offers, improving overall data handling in the progression graph editor.
- Adjusted frontend components to reflect these changes, ensuring a more intuitive user experience in managing progression paths.
- Removed the `try_suggest_ai_stage_step` function from `_enrich_roadmap_unfilled_gap_offers`, simplifying the gap fill offer generation process.
- Updated `_run_evaluate_only_path_qa` and `suggest_progression_path` to disable AI calls and proposals, enhancing control over evaluation parameters.
- Adjusted `ProgressionGraphEditor` to reflect changes in API requests, ensuring consistent handling of evaluation data.
- Added a new test to validate the behavior of proposed QA when no slot differences are present, improving test coverage for comparison logic.
- Integrated `try_suggest_ai_stage_step` to suggest AI-generated gap fill steps based on user input, improving the automation of the planning process.
- Updated `_enrich_roadmap_unfilled_gap_offers` to conditionally include AI gap fill proposals, enhancing the offer generation logic.
- Implemented `_merge_gap_fill_offers_from_steps` to consolidate gap fill offers from various steps, ensuring a comprehensive list of available offers.
- Modified `ProgressionGraphEditor` to utilize the new merging logic for gap fill offers, improving the user experience in managing offers.
- Enhanced utility functions to streamline the collection and filtering of gap fill offers from API responses.
- Bumped version to reflect the new features and improvements.
- Updated `suggest_progression_path` to utilize `evaluate_steps` for improved validation, ensuring at least one evaluation step is provided.
- Modified frontend components to enhance user experience in the comparison process, including clearer messaging and improved dialog handling.
- Adjusted `ProgressionGraphEditor` to streamline the comparison flow and integrate new evaluation parameters.
- Enhanced `ProgressionOptimizeCompareModal` to reflect changes in comparison logic, allowing for better user interaction with proposed path suggestions.
- Bumped version to reflect the new features and improvements.
- Introduced `_baseline_slot_accepts_rematch_suggestion` to filter out filled or invalid slots from rematch suggestions, improving the accuracy of rematch logic.
- Updated `_build_rematch_suggestion_diffs` to skip non-eligible baseline slots, streamlining the rematch suggestion process.
- Added `_evaluate_steps_for_compare_qa` to evaluate steps against the current state, enhancing the quality assessment during progression path suggestions.
- Modified `_build_progression_compare_response` to ensure proper handling of slot differences and quality scores, improving response clarity.
- Updated frontend components to reflect changes in rematch handling and evaluation logic.
- Bumped version to reflect the new features and improvements.
- Introduced `_annotate_slot_diffs` to mark trivial ID swaps in slot differences, improving clarity in comparison results.
- Added `_actionable_slot_diffs` to filter out non-actionable differences, streamlining the evaluation process.
- Implemented `_build_rematch_suggestion_diffs` to generate suggestions based on rematch logs, enhancing the path optimization workflow.
- Updated `_build_progression_compare_response` to incorporate actionable slot differences and rematch suggestions, improving the response structure.
- Enhanced frontend components to display rematch suggestions and handle trivial differences more effectively.
- Bumped version to reflect the new features and improvements.
- Introduced `_steps_to_evaluate_payloads` to convert path steps into evaluation payloads for improved quality assessments.
- Updated `_build_progression_compare_response` to include a new `proposed_eval` parameter, allowing for fair quality assessment comparisons.
- Enhanced `ProgressionGraphEditor` to utilize the new pipeline quality assessment data.
- Modified `ProgressionOptimizeCompareModal` to display detailed comparison results, including handling of trivial slot differences and optimization hints.
- Bumped version to reflect the new features and improvements.
- Updated `suggest_progression_path` to include additional evaluation parameters, allowing for more comprehensive path assessments.
- Introduced `PathQaPipelineDetails` component to display detailed quality assessment metrics, including rematch and refine logs, in the frontend.
- Enhanced `ProgressionGraphEditor` to manage proposed path evaluations and integrate quality assessment results into the draft workflow.
- Improved `ProgressionOptimizeCompareModal` to present optimization hints and quality tier information for proposed paths.
- Bumped version to reflect the new features and improvements.
- 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.
- Updated `PROJECT_STATUS.md` to reflect the addition of the Planning AI Progression Graph and its context in the roadmap.
- Enhanced `DOMAIN_MODEL.md` with details on the new `planning_catalog_context` features, allowing trainers to manage curriculum stages and context.
- Added tests in `test_planning_catalog_context.py` to validate the separation of LLM highlights from fix hints during QA processes.
- Updated `HANDOVER.md` and `PLANNING_KI_ROADMAP.md` to reflect the latest app version and improvements in the planning context.
- Enhanced frontend components to support the new planning catalog context, including updates to `ExerciseProgressionPathBuilder` and `ProgressionGraphEditor`.
- Bumped version to 0.8.233 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 `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.
- Added support for displaying optimization hints and rematch logs in the Progression Findings Panel, improving user feedback on potential enhancements.
- Introduced new utility functions for formatting rematch log entries and resolving hint slot indices, enhancing clarity in displayed information.
- Updated the Progression Graph Editor to handle rematch actions and display relevant match summaries, ensuring comprehensive insights during progression analysis.
- Enhanced the utility functions to support the new features, ensuring robust handling of optimization actions and rematch logic.
- 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 `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.
- Refactored `ExerciseProgressionGraphPanel` to support a create dialog for new progression graphs, improving user experience.
- Integrated `ProgressionGraphListCard` for better visualization of existing graphs and streamlined management.
- Updated `ProgressionGraphEditor` to handle start/target analysis and improved draft hydration with AI suggestions.
- Added utility functions for managing structured responses from AI, enhancing the planning process.
- Incremented application version to reflect these updates.
- Introduced skills catalog management in the `ProgressionGraphEditor`, allowing for improved context in AI suggestions.
- Updated the loading mechanism to fetch both focus areas and skills catalog concurrently, enhancing performance.
- Implemented `ensureQuickCreateDraftFromAiSuggestion` utility to streamline the creation of drafts from AI suggestions.
- Enhanced slot management by integrating AI context into the gap fill preparation process, improving user experience.
- Incremented application version to reflect these updates.
- Implemented functions to resolve neighboring steps based on major indices and build AI context for unfilled roadmap stages.
- Enhanced `try_suggest_ai_stage_step` to generate AI proposals for empty roadmap stages, improving user experience in gap filling.
- Updated `build_gap_fill_offer` to utilize major step neighbors for better context in offers related to unfilled slots.
- Added tests to ensure correct functionality of AI suggestion handling in the context of roadmap gaps.
- Incremented application version to reflect these updates.
- Updated the `ProgressionGraphSlotEditorSpec.md` to reflect UI consolidation, removing separate editors and integrating functionalities into `ExerciseProgressionGraphPanel`.
- Refactored `ExerciseProgressionGraphPanel` to streamline the editing experience, removing unused state and logic for better performance.
- Enhanced `ProgressionGraphEditor` to support embedded usage and trigger callbacks on save, improving integration with other components.
- Simplified `ProgressionGraphEditPage` to redirect users to the exercises list with deep-linking support for selected graphs.
- 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.
