Optimierung KI-Scuhe + Ki-Überarbeitungen der Übungen #49

Merged
Lars merged 16 commits from develop into main 2026-05-23 07:54:21 +02:00
Owner
No description provided.
Lars added 16 commits 2026-05-23 07:54:15 +02:00
Integrate Planning AI Features and Update Application Version to 0.8.167
Some checks failed
Deploy Development / deploy (push) Successful in 42s
Test Suite / pytest-backend (push) Failing after 0s
Test Suite / lint-backend (push) Successful in 0s
Test Suite / build-frontend (push) Successful in 13s
Test Suite / k6 /health Baseline (push) Failing after 3m59s
Test Suite / playwright-tests (push) Failing after 3m41s
d7d45a8927
- 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.
Enhance Planning Exercise Suggestion Features and Update Application Version to 0.8.169
All checks were successful
Deploy Development / deploy (push) Successful in 42s
Test Suite / pytest-backend (push) Successful in 39s
Test Suite / lint-backend (push) Successful in 0s
Test Suite / build-frontend (push) Successful in 14s
Test Suite / k6 /health Baseline (push) Successful in 33s
Test Suite / playwright-tests (push) Successful in 1m14s
128a9d752e
- 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.
Enhance Planning Exercise Suggestion with LLM-Rerank and Client Overrides
All checks were successful
Deploy Development / deploy (push) Successful in 43s
Test Suite / pytest-backend (push) Successful in 43s
Test Suite / lint-backend (push) Successful in 0s
Test Suite / build-frontend (push) Successful in 13s
Test Suite / k6 /health Baseline (push) Successful in 47s
Test Suite / playwright-tests (push) Successful in 1m14s
207817376d
- 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.
Implement Phase 1 of Planning Exercise Suggestion with Scenario Pipeline and LLM Intent Overlay
All checks were successful
Deploy Development / deploy (push) Successful in 43s
Test Suite / pytest-backend (push) Successful in 43s
Test Suite / lint-backend (push) Successful in 0s
Test Suite / build-frontend (push) Successful in 13s
Test Suite / k6 /health Baseline (push) Successful in 34s
Test Suite / playwright-tests (push) Successful in 1m14s
45e3b5f4f6
- 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.
Refactor ExercisePickerModal to Utilize Effective Query for AI Suggestions
All checks were successful
Deploy Development / deploy (push) Successful in 40s
Test Suite / pytest-backend (push) Successful in 40s
Test Suite / lint-backend (push) Successful in 0s
Test Suite / build-frontend (push) Successful in 13s
Test Suite / k6 /health Baseline (push) Successful in 33s
Test Suite / playwright-tests (push) Successful in 1m13s
905bce198f
- 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.
Refactor ExercisePickerModal for Enhanced Search Functionality
All checks were successful
Deploy Development / deploy (push) Successful in 41s
Test Suite / pytest-backend (push) Successful in 40s
Test Suite / lint-backend (push) Successful in 0s
Test Suite / build-frontend (push) Successful in 13s
Test Suite / k6 /health Baseline (push) Successful in 33s
Test Suite / playwright-tests (push) Successful in 1m16s
d019c20338
- 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.
Enhance ExercisePickerModal with Improved Planning Context Handling
All checks were successful
Deploy Development / deploy (push) Successful in 44s
Test Suite / pytest-backend (push) Successful in 39s
Test Suite / lint-backend (push) Successful in 0s
Test Suite / build-frontend (push) Successful in 13s
Test Suite / k6 /health Baseline (push) Successful in 33s
Test Suite / playwright-tests (push) Successful in 1m39s
f5c886fc13
- 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.
Enhance Planning Exercise Suggestion with Client Context and Group ID Support
All checks were successful
Deploy Development / deploy (push) Successful in 42s
Test Suite / pytest-backend (push) Successful in 40s
Test Suite / lint-backend (push) Successful in 0s
Test Suite / build-frontend (push) Successful in 13s
Test Suite / k6 /health Baseline (push) Successful in 34s
Test Suite / playwright-tests (push) Successful in 1m16s
614c2dcfaa
- 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.
Update ExercisePickerModal to Enforce Backend Suggestion Limit
All checks were successful
Deploy Development / deploy (push) Successful in 42s
Test Suite / pytest-backend (push) Successful in 39s
Test Suite / lint-backend (push) Successful in 1s
Test Suite / build-frontend (push) Successful in 12s
Test Suite / k6 /health Baseline (push) Successful in 33s
Test Suite / playwright-tests (push) Successful in 1m17s
b0611b9f7f
- 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.
Refactor Planning Exercise Suggestion and Enhance LLM Integration
All checks were successful
Deploy Development / deploy (push) Successful in 45s
Test Suite / pytest-backend (push) Successful in 40s
Test Suite / lint-backend (push) Successful in 0s
Test Suite / build-frontend (push) Successful in 13s
Test Suite / k6 /health Baseline (push) Successful in 34s
Test Suite / playwright-tests (push) Successful in 1m16s
8e68261bc1
- 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.
Enhance Planning Exercise Profiles and Context Handling
All checks were successful
Deploy Development / deploy (push) Successful in 44s
Test Suite / pytest-backend (push) Successful in 43s
Test Suite / lint-backend (push) Successful in 0s
Test Suite / build-frontend (push) Successful in 12s
Test Suite / k6 /health Baseline (push) Successful in 33s
Test Suite / playwright-tests (push) Successful in 1m14s
04cc77d501
- 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.
Enhance Planning Exercise Functionality and LLM Integration
All checks were successful
Deploy Development / deploy (push) Successful in 41s
Test Suite / pytest-backend (push) Successful in 43s
Test Suite / lint-backend (push) Successful in 0s
Test Suite / build-frontend (push) Successful in 13s
Test Suite / k6 /health Baseline (push) Successful in 34s
Test Suite / playwright-tests (push) Successful in 1m15s
5c882985e0
- 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.
Add ExerciseAiQuickCreateTeaser Component and Update ExercisePickerModal
All checks were successful
Deploy Development / deploy (push) Successful in 40s
Test Suite / pytest-backend (push) Successful in 39s
Test Suite / lint-backend (push) Successful in 0s
Test Suite / build-frontend (push) Successful in 13s
Test Suite / k6 /health Baseline (push) Successful in 34s
Test Suite / playwright-tests (push) Successful in 1m17s
a8633235f2
- 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.
Refactor Planning Exercise Retrieval and Suggestion Logic
All checks were successful
Deploy Development / deploy (push) Successful in 39s
Test Suite / pytest-backend (push) Successful in 39s
Test Suite / lint-backend (push) Successful in 1s
Test Suite / build-frontend (push) Successful in 12s
Test Suite / k6 /health Baseline (push) Successful in 33s
Test Suite / playwright-tests (push) Successful in 1m12s
d1d8539b42
- 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.
Add Exercise Enrichment Admin API and Update Documentation
All checks were successful
Deploy Development / deploy (push) Successful in 41s
Test Suite / pytest-backend (push) Successful in 40s
Test Suite / lint-backend (push) Successful in 0s
Test Suite / build-frontend (push) Successful in 13s
Test Suite / k6 /health Baseline (push) Successful in 34s
Test Suite / playwright-tests (push) Successful in 1m17s
f4196c3580
- 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.
Enhance Exercise Enrichment Admin Functionality and Update Documentation
All checks were successful
Deploy Development / deploy (push) Successful in 51s
Test Suite / pytest-backend (push) Successful in 44s
Test Suite / lint-backend (push) Successful in 0s
Test Suite / build-frontend (push) Successful in 13s
Test Suite / k6 /health Baseline (push) Successful in 33s
Test Suite / playwright-tests (push) Successful in 1m16s
Test Suite / pytest-backend (pull_request) Successful in 36s
Test Suite / lint-backend (pull_request) Successful in 0s
Test Suite / build-frontend (pull_request) Successful in 13s
Test Suite / k6 /health Baseline (pull_request) Successful in 33s
Test Suite / playwright-tests (pull_request) Successful in 1m23s
46fae3da33
- 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.
Lars merged commit 9ba35dc022 into main 2026-05-23 07:54:21 +02:00
Sign in to join this conversation.
No reviewers
No Label
No Milestone
No project
No Assignees
1 Participants
Notifications
Due Date
The due date is invalid or out of range. Please use the format 'yyyy-mm-dd'.

No due date set.

Dependencies

No dependencies set.

Reference: Lars/shinkan-jinkendo#49
No description provided.