- 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.
- 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.
- 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.
