- 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.
- 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 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 `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.
- 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 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 0.8.183, 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.
- 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.
- 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.
- 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.
