Commit Graph

3 Commits

Author SHA1 Message Date
d1d8539b42 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
- 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.
2026-05-23 06:35:45 +02:00
5c882985e0 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
- 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.
2026-05-22 23:08:53 +02:00
8e68261bc1 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
- 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.
2026-05-22 22:56:28 +02:00