- Introduced the `consume_club_feature_with_usage` function to standardize feature consumption across endpoints, improving code reusability and clarity.
- Implemented `merge_feature_usage_into_response` to embed feature usage data in API responses, streamlining frontend integration.
- Updated various backend routers to utilize the new consumption logic, ensuring consistent feature usage tracking during AI-related actions.
- Enhanced tests to validate the new consumption and logging behavior.
- Incremented application version to 0.8.199 and updated module version for 'club_features' to 1.6.0 to reflect these changes.
- Replaced the admin club feature exemptions router with a new admin rights router to streamline capability management.
- Added new API endpoints for managing admin rights, including capability grants and quota bypass for portal roles and profiles.
- Updated the frontend to include navigation and lazy loading for the new Admin Rights page.
- Incremented application version to 0.8.197 to reflect these changes and enhancements.
- Added support for club feature quota bypass based on portal roles and profile grants in the capabilities check.
- Introduced new functions to handle quota bypass logic in club feature access and consumption.
- Updated the FeatureUsageBadge component to reflect platform exemptions for features.
- Incremented application version to 0.8.195 and database schema version to 20260606083 to reflect these changes.
- Enhanced backend routers to include new logic for consuming club features during AI-related actions.
- Introduced endpoints for managing club creation requests, including fetching, creating, and withdrawing requests.
- Updated the onboarding page to allow users to submit new club creation requests and view their existing requests.
- Enhanced the admin interface with navigation and routing for club creation requests management.
- Incremented version to 0.8.191 to reflect these new features and updates in the application.
- Revised the status in the Capability Catalog to reflect partial implementation (M3).
- Added a new reference to `MEMBERSHIP_RBAC_DECISIONS_2026-06.md` in both the Capability Catalog and Club Membership documentation.
- Enhanced the Club Membership documentation with details on product decisions and onboarding phases.
- Implemented middleware in the backend to restrict access for unverified users and those pending club membership.
- Updated versioning in `version.py` to reflect changes in account lifecycle management.
- Introduced `email_verified` and `account_state` attributes in the `TenantContext` to improve user state management.
- Updated the `resolve_tenant_context` function to dynamically fetch `email_verified` status from the database and determine `account_state` based on user roles and memberships.
- Implemented `assert_min_account_state` checks across various endpoints to enforce access control based on user account status.
- Incremented version to 1.1.0 in version.py to reflect these enhancements in tenant context management and access control.
- Introduced `probe_club_feature_access` to check club feature limits and log access attempts without blocking by default.
- Added `_live_inventory_count` function to retrieve current counts for specific features, enhancing feature limit management.
- Updated various endpoints to utilize the new probing functionality, ensuring compliance with club feature access rules.
- Incremented version to 1.1.0 in version.py to reflect these enhancements in club feature management.
- Enhanced the ACCESS_LAYER_AND_GOVERNANCE_PLAN.md with new specifications for capability documentation and community features.
- Added references to new documents detailing capability IDs and club membership features.
- Updated MULTI_TENANCY_RBAC_ARCHITECTURE.md to include links to the new specifications.
- Marked certain features as deprecated in backend/auth.py, indicating migration paths for club feature access.
- Incremented DB_SCHEMA_VERSION to 20260606078 in version.py to reflect recent 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.
- Incremented version to 0.8.184, reflecting the implementation of Phase C2 features.
- Added support for displaying variant lists and suggested variant names in exercise suggestions.
- Enhanced the ExercisePickerModal to allow selection of exercise variants and improved handling of variant IDs.
- Updated backend logic to enrich planning hits with variant metadata, ensuring accurate exercise variant selection.
- Documented changes in the changelog to highlight the new capabilities in planning AI functionality.
- Incremented version to 0.8.183, reflecting the implementation of Phase C1 enhancements.
- Added support for progression graph auto-matching and variant-aware successors in exercise suggestions.
- Updated request and response structures to include `anchor_exercise_variant_id`, `progression_graph_name`, and `suggested_variant_id`.
- Enhanced frontend components to integrate planning AI search capabilities, including a new modal for exercise creation and improved context display in the exercise list.
- Updated changelog to document these significant improvements in planning AI functionality.
- Introduced a new function `hybrid_ranking_ambiguous` to determine when to rerank candidates based on score proximity, improving the decision-making process for exercise suggestions.
- Updated `should_run_llm_rank_pipeline` to incorporate the new ranking logic and handle scenarios with ambiguous rankings more effectively.
- Adjusted the frontend to always include LLM ranking in requests, ensuring consistent behavior across different query lengths.
- Incremented version to 0.8.182 and updated changelog to reflect these enhancements in planning AI capabilities.
- Added support for section guidance notes and titles in the planning target profile, enabling richer context for exercise suggestions.
- Introduced deterministic text-to-catalog signal mapping, allowing for improved integration of planning text signals into the exercise retrieval process.
- Implemented a partner-related filter in exercise retrieval, enhancing the relevance of suggested exercises based on user intent.
- Updated the retrieval phase to account for text signals, improving the accuracy of exercise recommendations.
- Incremented version to 0.8.181 and updated changelog to reflect these significant enhancements in planning AI capabilities.
- Introduced the `exercise_enrichment_admin` API for batch exercise enrichment, allowing superadmins to filter candidates, preview, and apply skills.
- Updated the access layer documentation to include the new endpoint and its exempt status.
- Enhanced the frontend with a new admin page for exercise enrichment and updated navigation to include this feature.
- Incremented version to 0.8.179 and updated changelog to reflect these additions and improvements.
- Updated the planning exercise retrieval process to implement a multistage approach, ranking the entire visible library deterministically against the expectation profile.
- Removed the previous profile OR pool mechanism, simplifying the retrieval logic and ensuring full-text search is only used as a scoring signal.
- Adjusted the `compose_retrieval_phase` function to accommodate the new full library ranking strategy.
- Incremented version to 0.8.177 and updated changelog to reflect these changes in planning exercise capabilities.
- Added support for the new planning exercise expectation profile slug in the AI prompt runtime.
- Refactored SQL parameter handling in the planning exercise retrieval process to ensure correct binding for full-text search.
- Updated the planning exercise suggestion logic to incorporate LLM expectation handling, improving the accuracy of exercise recommendations.
- Introduced new functions to determine when to run the LLM expectation pipeline, enhancing the decision-making process for exercise suggestions.
- Incremented version to 0.8.176 and updated changelog to reflect these enhancements in planning AI capabilities.
- Introduced new functions to generate skill profiles from exercise IDs, improving the ability to summarize skills for both units and sections.
- Updated the planning target profile to incorporate section-specific exercise IDs, allowing for more granular skill tracking and context.
- Enhanced the ExercisePickerModal and related pages to support section context, including titles, guidance notes, and exercise counts.
- Implemented expectation mode handling in the planning target pipeline to differentiate between planning references and query-only scenarios.
- Incremented version to 0.8.174 and updated changelog to reflect these enhancements in planning AI capabilities.
- Replaced the previous exercise matching logic with a new multistage planning retrieval process, improving the accuracy of exercise suggestions.
- Introduced LLM gates to limit LLM calls based on query length and intent application, optimizing performance and resource usage.
- Updated the `compose_retrieval_phase` function to include profile preselection, enhancing the retrieval process.
- Incremented version to 0.5.0 and updated changelog to reflect these significant enhancements in planning AI capabilities.
- Introduced the Scenario Pipeline for planning exercises, allowing for more nuanced query handling and exercise suggestions based on user intent.
- Enhanced the `suggestPlanningExercises` API to include `include_llm_intent`, `scenario_kind`, and `query_intent_summary`, improving the context provided to the frontend.
- Updated the `ExercisePickerModal` to display new information related to query intent and scenario classification, enhancing user experience during exercise selection.
- Incremented application version to 0.8.171 and updated changelog to document the new features and improvements in the planning AI capabilities.
- Implemented optional LLM-Rerank functionality in the planning exercise suggestion process, allowing for improved exercise ranking based on user-defined criteria.
- Updated the `suggestPlanningExercises` API to accept `planned_exercise_ids` for client-side overrides, enhancing flexibility in exercise selection.
- Enhanced the `ExercisePickerModal` to reflect LLM ranking status and support new planning context features.
- Incremented application version to 0.8.170 and updated changelog to document the new features and improvements in the planning AI capabilities.
- Added new functionality for exporting and importing matrix editor data in JSON and CSV formats within the MaturityMatrixToolsAdmin component.
- Updated the API utility functions to support matrix editor exports and imports, enhancing the backend communication for Superadmin tasks.
- Refactored the client API to streamline request handling and improve code clarity.
- Included new UI elements for file upload and download actions, improving user experience in managing matrix data.
- Added capabilities for weighted skill profiles, allowing trainers to compare training modules, frameworks, and regression paths based on skill contributions.
- Updated the skill scoring specification to include peer context separation and list filtering, ensuring accurate comparisons among visible artifacts of the same type.
- Enhanced the API to support batch summaries for skill profiles and discovery suggestions, improving data retrieval efficiency.
- Refactored frontend components to display skill metrics, including scores and peer percentages, with improved filtering options for better user experience.
- Updated documentation to reflect the latest changes and enhancements in the skill scoring system.
- Introduced a new function to calculate club-specific skill percentages, ensuring values are capped at 100%.
- Updated skill profile calculations to include indicators for the best club performance per skill.
- Enhanced frontend components to display club best indicators and improved layout for skill profiles.
- Refactored CSS styles for skill profile components, ensuring a more cohesive and user-friendly interface.
- Updated tests to validate new functionality and ensure accurate representation of skill metrics.
- Enhanced the skill scoring system with category grouping and a universal scale for improved comparability across programs.
- Introduced new calculations for artifact share percentage and universal percent, allowing for a more nuanced understanding of skill contributions.
- Updated the API to reflect changes in the skill profile structure, including main category and top skill details.
- Improved frontend components to display skills by main category, enhancing user experience in skill discovery and profile visualization.
- Adjusted tests to validate the new scoring logic and ensure accurate representation of skills and their weights.
- Enhanced the skill scoring formula to incorporate intensity and level range factors, improving the accuracy of skill contributions.
- Removed the use of `is_primary` and `development_contribution` from calculations, streamlining the scoring process.
- Updated documentation to reflect changes in the scoring logic and versioning.
- Adjusted frontend components to align with the new scoring criteria, ensuring consistent user experience across the application.
- Updated the framework program documentation to reflect the completion of Phase 3 v1.0, including new skill scoring and API enhancements.
- Added new API endpoints for skill profile retrieval and suggestions, improving the ability to aggregate and display skills based on training data.
- Introduced new UI components for skill profiles and discovery in the frontend, enhancing user interaction with training frameworks and skills.
- Updated version information to 0.8.151, reflecting the addition of skill profiles and related features.
- Introduced `_normalize_mw_category` function to clean category names for API calls, ensuring consistent handling of category prefixes.
- Updated `SmwClient` methods to utilize normalized category names, improving data retrieval accuracy.
- Added `_wiki_category_or_default` function to provide default categories based on import type, enhancing user experience during imports.
- Integrated new fields `karate_relevance` and `relevance_level` into various admin components, allowing for better skill management.
- Incremented app version to 0.8.145 and updated changelog to reflect these changes.
- Added `karate_relevance` and `relevance_level` fields to the SkillCreate and SkillResponse models, allowing for more detailed skill attributes.
- Updated the SMW property mapping to include these new fields, facilitating their integration during data import.
- Implemented parsing logic for relevance levels from Wiki data, ensuring proper handling of values between 1 and 3.
- Modified the upsert and create skill functions to support the new fields, ensuring they are correctly stored and updated in the database.
- Incremented app version to 0.8.143 and updated changelog to reflect these changes.
- Enhanced phase handling in training unit hydration and insertion processes, ensuring better data integrity.
- Updated frontend components to support phase representation in training framework slots.
- Improved user interface controls for managing parallel phases, optimizing user experience during training program edits.
- Refactored payload building functions to accommodate phase adjustments, enhancing save functionality for training plans.
- Bumped APP_VERSION to 0.8.138 and updated the changelog to reflect recent changes.
- Enhanced training unit planning with support for POST/PUT requests including phases and parallel streams.
- Fixed role assignment validation for stream co-trainers and added integration tests for phase handling.
- Updated the training planning API to improve data structure and retrieval for nested phases and sections.
- Enhanced the pytest workflow in `.gitea/workflows/test.yml` to include `TRAINING_PLANNING_INTEGRATION` for improved testing against the PostgreSQL database.
- Updated `pytest.ini` to clarify integration marker usage, specifying both `ACCESS_LAYER_INTEGRATION` and `TRAINING_PLANNING_INTEGRATION`.
- Revised documentation in `test_training_planning_sections_integration.py` to provide clearer activation instructions for local and CI environments.
- Updated the backend to improve the fetching and insertion of training unit sections, including a new function for handling section items.
- Added documentation notes regarding the unique constraint on `training_unit_sections` and the implications for parallel training streams.
- Updated frontend components and utility functions to reflect changes in the training planning API and to prepare for future enhancements related to parallel streams.
- Bumped APP_VERSION to 0.8.125 and updated the changelog to reflect recent changes.
- Added new tests for the dashboard API to ensure proper HTTP 200 responses when inner lists are mocked.
- Enhanced the ExerciseListBulkToolbar component with a data-testid for improved testing capabilities.
- Refactored the TrainingPlanningPage by extracting utility functions to trainingPlanningPageHelpers for better code organization.
- Bumped APP_VERSION to 0.8.123 and updated the changelog to reflect recent changes.
- Fixed internal calls in GET /api/dashboard/kpis to use unwrap_query_default, preventing 500 errors due to FastAPI query defaults.
- Enhanced list_exercises and list_training_units functions to utilize unwrap_query_default for improved query handling.
- Added unit tests for unwrap_query_default to ensure correct behavior in various scenarios.
- Bumped APP_VERSION to 0.8.117 and updated DB_SCHEMA_VERSION to 20260514061.
- Enhanced the training units API with optional keyset pagination, allowing for more efficient data retrieval.
- Updated the changelog to reflect the new features and improvements, including changes to the frontend API integration for training units.
- Adjusted documentation to align with the new app version and its corresponding changes.
- Bumped APP_VERSION to 0.8.115 and updated the changelog to reflect changes, including the introduction of keyset pagination for the GET /api/exercises endpoint.
- Enhanced the exercises router to support cursor-based pagination using cursor_updated_at and cursor_id, improving performance and user experience.
- Updated frontend components to utilize the new pagination method, removing offset-based loading logic.
- Implemented a new API endpoint for retrieving dashboard KPIs, providing a consolidated overview of drafts, personal exercises, and year-to-date completed units.
- Updated the Dashboard component to utilize the new endpoint, enhancing data retrieval efficiency and user experience.
- Added a helper function in the exercises router for programmatic access to exercise listings.
- Updated versioning and changelog to reflect the addition of the dashboard feature.
- Add early 403 in set_legal_hold_from_report for plain admin (before DB
call), fixing test_legal_hold_from_report_requires_superadmin
- Update test_list_inbox_requires_platform_admin to mock DB COUNT query
(returns cnt=0) so it exercises the club_admin code path correctly
- Extend test_patch_report_under_review mock row with target_type,
target_id, resolution_note fields now required by the audit-log path
version: 0.8.94
module: content_reports 1.5.1
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Added new API endpoints for content reporting, including submission, retrieval, and status updates.
- Created database migration for `content_reports` table to store report data.
- Integrated content reports into the existing admin inbox for better management.
- Implemented validation for report submissions, including required fields and email format.
- Added tests for content reporting functionality, covering various scenarios and edge cases.
- Updated frontend API utility to include new content report methods.
- Bumped app version to 0.8.87 and updated relevant page versions.
- Added backend support for Legal Hold with new endpoints to set and release holds on media assets.
- Introduced new database columns for managing Legal Hold status and reasons.
- Updated frontend to include UI elements for setting and releasing Legal Holds, including a confirmation dialog.
- Enhanced Media Library page to display Legal Hold status and actions for superadmins.
- Implemented comprehensive backend tests covering all aspects of Legal Hold functionality.
- Updated documentation to reflect changes in the upload rights specification and interface models.
- Bumped version to 0.8.84 and updated MediaLibraryPage version to 1.6.0.
- check_rights_coverage: rights_status='declared' gibt immer 'ok' zurück
(P-06-Erklärung gilt inhaltlich, nicht sichtbarkeitsabhängig)
- assert_rights_for_promotion: 'insufficient'-Pfad entfernt
- Tests: test_declared_private_insufficient_for_club → test_declared_covers_any_visibility
version: 0.8.81
module: media_rights
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Adjusted retention policy to align with compliance requirements:
- Changed HIDDEN_TO_PURGE_DAYS from 90 to 30 days.
- Enhanced password reset functionality to enforce a minimum password length of 8 characters.
- Updated tests to validate new password requirements and retention logic.
- Corrected umlaut in copyright error messages for clarity.
- Added compliance implementation report detailing the status of various packages (P-03, P-04, P-05, P-07, P-23, P-24) and their technical changes, tests, and notes.
- Introduced a new workspace configuration file for the project to streamline development setup.
- Updated visibility logic for exercises, media assets, and training programs to ensure access is correctly managed based on active club memberships.
- Refactored SQL queries to streamline visibility checks for platform admins and club members, ensuring only relevant content is displayed.
- Improved user interface elements to reflect the status of club memberships, including visual indicators for inactive memberships.
- Enhanced test cases to validate the new visibility logic and ensure proper access control across various components.
