- Introduced a new admin user content management endpoint for superadmins, allowing for moderation of user-generated content.
- Updated the backend to include new API functions for retrieving, patching, and deleting user content items.
- Enhanced the frontend with a new Admin User Content page and navigation link for easy access to user content management.
- Updated access layer documentation to reflect the new endpoint and its exempt status.
- Incremented version to 0.8.191 and updated changelog to document these additions in admin functionality.
- 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.
- 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.
- Implemented a maximum of 3 exercises per preview request to prevent Gateway-504 errors, improving the stability of the exercise enrichment process.
- Adjusted batch sizes for applying exercises and previewing to optimize performance and resource management.
- Updated the frontend to reflect changes in preview handling, including user notifications about chunk sizes and potential timeouts.
- Incremented version to 0.8.180 and updated changelog to document these enhancements and fixes.
- 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.
- Introduced the ExerciseAiQuickCreateTeaser component for a compact entry point in the exercise creation process.
- Updated ExercisePickerModal to integrate the new teaser, allowing users to expand and create exercises directly from the search results.
- Enhanced the quick create functionality with dynamic headlines and hints based on user input and context.
- Refactored conditional rendering logic to improve user experience when no exercises are found.
- 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 a constant `PLANNING_SUGGEST_LIMIT` set to 50 to align with backend constraints for exercise suggestions.
- Updated the API request limit in `ExercisePickerModal` to utilize the new constant, ensuring compliance with backend specifications.
- Made `unit_id` and `group_id` optional in `PlanningExerciseSuggestRequest` to support client context without a saved unit.
- Refactored `_load_group_recent_exercise_ids` to handle cases where `exclude_unit_id` is optional.
- Introduced `build_client_planning_context_pack` for improved context handling in client-free searches.
- Updated `suggest_planning_exercises` to utilize the new client context pack when `unit_id` is not provided.
- Incremented version to 0.8.172 and updated changelog to reflect these enhancements in the planning AI capabilities.
- Introduced `planningUnitId` and `expectPlanningSearch` props to better manage planning context for exercise suggestions.
- Refactored logic to resolve planning unit ID and construct active planning context, enhancing the accuracy of exercise suggestions.
- Implemented checks to block planning search when necessary, providing clearer user feedback in the UI.
- Updated `TrainingUnitEditPage` to pass the correct planning unit ID, ensuring seamless integration with the exercise picker.
- Updated `effectivePickerQuery` logic to improve search handling based on planning context, allowing for a single input field in planning mode.
- Simplified query construction by utilizing `effectivePickerQuery` throughout the component, enhancing clarity and user experience.
- Adjusted UI elements and labels to better reflect the context of the search, providing clearer guidance for users.
- Modified `TrainingUnitEditPage` to ensure proper unit ID resolution, improving integration with the exercise picker.
- Introduced `effectivePickerQuery` to streamline search input handling, combining `debouncedSearch` and `debouncedAi` for improved query accuracy.
- Updated the `useExerciseAiQuickCreateFields` hook to use the new effective query, enhancing the quick create functionality.
- Modified conditional checks to utilize `effectivePickerQuery`, ensuring better user feedback based on search input.
- Improved placeholder text and labels for clarity in the search fields, enhancing user experience during exercise selection.
- 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.
- Implemented Phase 1.1 of the planning exercise suggestion functionality, integrating `ExerciseMatchProfile` and `PlanningTargetProfile` for improved exercise scoring based on profile dimensions.
- Updated the `suggestPlanningExercises` API to include a new `retrieval_phase` and `target_profile_summary`, enhancing the context provided to the frontend.
- Enhanced the `ExercisePickerModal` to display additional information from the planning target profile, including focus areas and top skills, improving user experience during exercise selection.
- Incremented application version to 0.8.169 and updated changelog to reflect the new features and improvements in the planning AI capabilities.
- Added new planning AI functionality with the introduction of the `suggestPlanningExercises` API endpoint for context-based exercise suggestions.
- Enhanced `ExercisePickerModal` to utilize planning context, allowing for a more tailored exercise selection experience.
- Updated `TrainingUnitEditPage` to pass planning context to the exercise picker, improving integration with the new planning features.
- Incremented application version to 0.8.167 and updated changelog to reflect the new planning AI capabilities and related enhancements.
- Introduced a new AI assistant toggle in the Exercise List Page header, allowing users to enable quick exercise creation via AI suggestions.
- Updated the ExerciseListSearchBar component to remove deprecated AI quick create functionality, streamlining the interface.
- Enhanced CSS styles for the AI assistant toggle, improving visual feedback and user interaction.
- Improved overall layout and spacing in the exercises page for better usability.
- Updated ExerciseAiQuickCreateOffer to set showSketchField to true by default and introduced sketchOptional prop for improved flexibility in exercise creation.
- Refactored ExercisePickerModal and ExercisesListPageRoot to leverage useExerciseAiQuickCreateFields hook, simplifying state management for quick create fields.
- Removed deprecated parsing logic and streamlined error handling for sketch input, enhancing user experience during exercise creation.
- Improved placeholder text and labels for clarity, ensuring better guidance for users when providing input for AI-generated exercises.
- Updated APP_VERSION to 0.8.166 and modified BUILD_DATE to reflect recent changes.
- Enhanced AI exercise creation process with a new quick create feature, allowing users to generate exercises based on search input.
- Introduced a rich text editor for editing AI-generated drafts, improving user experience in exercise creation.
- Updated ExercisePickerModal and related components to support the new quick create functionality, including error handling and input validation.
- Added new utility functions for parsing search queries and building exercise payloads from drafts.
- Updated the quick create process to include a preview feature for AI-generated exercises, allowing users to review goals, execution, preparation, and trainer notes.
- Introduced new constants for instruction fields and refactored the payload building function to utilize the preview data.
- Improved error handling to ensure at least one of the goal or execution fields is populated.
- Deprecated the previous payload building function in favor of the new preview-based approach, streamlining the exercise creation workflow.
- Updated APP_VERSION to 0.8.164 and added changelog entry for the new version.
- Enhanced ExercisePickerModal to support quick exercise creation using AI, including fields for sketch and focus area.
- Implemented error handling for AI suggestions and improved user prompts for input validation.
- Updated UI elements to reflect changes in exercise creation workflow.
- Added `exercise_instruction_rewrite` functionality to enhance AI-generated instructions, incorporating fields for goal, execution, preparation, and trainer notes.
- Updated `ExerciseFormAiPromptContext` to include new fields and methods for instruction handling.
- Enhanced the `run_exercise_form_ai_suggestion` function to support instruction rewriting and validation.
- Modified API endpoints and frontend components to integrate instruction features, including a new button for AI instruction revision.
- Incremented application version to 0.8.163 and updated changelog to reflect these changes, including migration details and new functionality.
- Added `openrouter_model` field to the `ai_prompts` table, allowing for optional model overrides per prompt.
- Updated the `exercise_ai` module to utilize the effective OpenRouter model based on prompt-specific settings, enhancing flexibility in AI interactions.
- Enhanced the admin interface to support OpenRouter model configuration for prompts, improving usability for Superadmins.
- Incremented application version to 0.8.161 and updated changelog to reflect these changes, including migration details and new functionality.
- 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.
- Incremented version to 1.1 and updated the status to reflect the implementation of core features including `ai_prompts`, `prompt_resolver`, and the Superadmin HTTP API.
- Documented the current API endpoints for managing AI prompts, including CRUD operations and preview functionality.
- Introduced a new placeholder catalog and preview capabilities for the Superadmin interface.
- Enhanced the backend with new functions for handling AI prompt templates and integrated them into the API.
- Updated frontend components to include navigation and routing for the new Admin AI Prompts page.
- Incremented application version to 0.8.158 and updated changelog to reflect these changes.
- Introduced detailed logging for AI operations in the `exercise_ai` and `openrouter_chat` modules, activated by the `SHINKAN_AI_DEBUG` environment variable, to aid in debugging and performance monitoring.
- Updated the `run_exercise_ai_suggestion` function to log prompt lengths, response sizes, and JSON parsing errors, enhancing transparency in AI interactions.
- Improved the `_flatten_message_content` function to handle nested content structures more effectively, ensuring compatibility with various AI response formats.
- Incremented the application version to 0.8.157 and updated the changelog to reflect these enhancements, including new logging features and content handling improvements.
- Added a new function `_first_balanced_json_array` to extract the first complete top-level JSON array from arbitrary text, enhancing robustness in parsing.
- Updated the `run_exercise_ai_suggestion` function to raise clear HTTP exceptions for empty responses from the OpenRouter, ensuring better error handling.
- Introduced `_flatten_message_content` in the `openrouter_chat` module to handle structured message content from OpenAI, improving compatibility with various content formats.
- Incremented the application version to 0.8.156 and updated the changelog to reflect these enhancements, including improved error messages and JSON parsing capabilities.
- Added documentation for the new Superadmin CRUD endpoints for managing AI Skill Retrieval Profiles (`/api/admin/ai-skill-retrieval-profiles*`).
- Updated the ACCESS_LAYER_ENDPOINT_AUDIT.md to include the new Superadmin API and its exempt status.
- Registered the ai_skill_retrieval_admin router in the backend and updated versioning to reflect the changes.
- Enhanced the frontend with a new Admin page for AI Skill Retrieval, including navigation and API integration for profile management.
- Introduced migration 068 for `ai_skill_retrieval_profiles`, enabling configurable weights and quotes for skill catalog prioritization in exercise AI suggestions.
- Updated the `POST /api/exercises/ai/suggest` endpoint to include an optional `focus_areas_context` field, allowing for enhanced context in AI-generated suggestions.
- Enhanced the `exercise_ai` module to utilize context-based skill selection, incorporating scoring, category caps, and keyword patches for improved AI responses.
- Updated the ExerciseFormPageRoot component to pass focus area context to the AI suggestion API, streamlining user interaction with AI-generated content.
- Incremented version numbers in `backend/version.py` to reflect the latest changes and ensure accurate tracking in the changelog.
- Updated the AI Exercise Implementation Plan to include a detailed description of the new suggestion dialog for AI proposals, allowing users to preview and selectively adopt AI-generated summaries and skills.
- Implemented a new preview feature in the ExerciseFormPageRoot component, enabling users to review AI suggestions before applying them to the form.
- Enhanced the skill management process by normalizing AI-suggested skills and integrating them into the exercise form, improving user interaction and data handling.
- Updated the exercise form to include a tabbed navigation structure, improving user experience with sections for Stammdaten, Anleitung, Einordnung, Varianten, and Medien & Mehr.
- Introduced the concept of **Freigabelevel** (visibility level) in the UI, replacing previous terminology for clarity and consistency across components.
- Implemented new AI endpoints for exercise suggestions and regeneration, allowing for dynamic content generation without direct database writes.
- Removed the legacy `is_primary` flag from exercise skills in the UI, ensuring that intensity levels (`niedrig`, `mittel`, `hoch`) are the primary focus for skill management.
- Enhanced the variant management process with improved saving mechanisms and UI updates to reflect changes more intuitively.
- Replaced hardcoded visibility labels with the new constant EXERCISE_VISIBILITY_FIELD_LABEL in multiple components, ensuring consistent terminology throughout the application.
- Updated UI text to reflect the change from "Sichtbarkeit" to "Freigabelevel" in various contexts, enhancing clarity for users.
- Improved accessibility by standardizing the visibility-related labels in the ExercisePickerModal, ExerciseProgressionGraphPanel, and other related components.
- Introduced a tabbed interface for the exercise form, allowing users to navigate between different sections (Stammdaten, Anleitung, Einordnung, etc.) more intuitively.
- Added new CSS styles for the exercise form, including improved layout and visual differentiation for various sections.
- Implemented dynamic tab management based on exercise type and edit state, enhancing user experience during form interactions.
- Refactored existing components to integrate the new tab structure, ensuring a cohesive design and functionality across the exercise form.
- Introduced snapshot and dirty check functions for variant payloads, enabling better tracking of unsaved changes.
- Implemented synchronization of saved variant snapshots to improve data integrity during edits.
- Enhanced the variant saving process with validation for variant names and automatic saving of changes.
- Updated the UI to reflect changes in variant management, ensuring a smoother user experience when editing exercise variants.
- Added a new meta panel for exercise classification and target groups, improving the organization of exercise attributes.
- Introduced ExerciseCatalogAssocEditor component to manage focus areas, training styles, and target groups, enhancing user interaction.
- Refactored CSS styles for the new meta panel and associated components, ensuring a cohesive design and improved responsiveness.
- Removed the MultiAssocBlock component to streamline the code and improve maintainability.
- Introduced a new constant, VARIANT_DIFFICULTY, to define difficulty options for exercises.
- Improved code organization by separating the import statements for better readability.
- 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.
- Updated the SkillTreeMultiSelect component to support dynamic positioning and improved accessibility through the use of portals.
- Refactored the dropdown panel rendering logic to enhance user experience when selecting skills.
- Added CSS styles for the exercise filter modal to improve layout and responsiveness.
- Introduced new styles for the skill tree multiselect panel, ensuring better visual integration and usability.
- Updated the FrameworkProgramsFilterBlock to include a search input and filter modal, improving user interaction and accessibility.
- Refactored CSS styles for filter components to ensure consistent layout and spacing.
- Removed deprecated panel open state management, streamlining the component logic.
- Integrated new filtering capabilities in the TrainingPlanningFrameworkImportModal and TrainingModulesListPage, enhancing the overall filtering experience.
- Improved the display of active filters and results count, providing clearer feedback to users.
- Introduced new helper functions for managing artifact type corpus, improving code organization and readability.
- Updated the `compute_club_corpus_reference` function to utilize the new corpus handling methods, enhancing clarity and maintainability.
- Refactored skill profile functions to leverage the new corpus structure, ensuring consistent data retrieval across different artifact types.
- Improved the handling of visibility clauses for library content, streamlining database queries for skill profiles.
- Enhanced the batch skill profile summary function to aggregate reference data by artifact type, improving performance and accuracy.
- Modified the `compact_profile_summary` function to allow for dynamic skill and category limits, enhancing flexibility in profile data retrieval.
- Updated frontend components to display skill weights and scores more effectively, improving user interaction with skill metrics.
- Adjusted CSS styles for skill KPI tiles to better differentiate between score and percentage displays, ensuring a clearer visual representation.
- Refactored utility functions to streamline skill summary handling, enhancing overall code maintainability and performance.
