- Create new `.github/scripts/build_wechat_payload.py` to handle WeChat payload generation
- Replace inline Python string concatenation with dedicated script for better maintainability
- Add checkout step to access the script during workflow execution
- Simplify workflow by delegating payload construction to external script
- Improve code readability and reusability for future notification enhancements
- Replace printf and jq command chain with Python script for payload generation
- Improve readability by using Python string concatenation instead of nested printf format specifiers
- Ensure proper JSON encoding with ensure_ascii=False to preserve Chinese characters
- Simplify environment variable interpolation using os.environ dictionary access
- Add GitHub Actions workflow to notify WeChat on release branch merges
- Implement multi-step pipeline: sync ref, verify latest PR, fetch commits
- Integrate Aliyun Qwen AI for automated Chinese commit message summarization
- Send formatted Markdown notifications to WeChat webhook with release details
- Include branch, author, PR title, AI summary, and PR link in notifications
- Add redbear-mem-benchmark directory to .gitignore
- Prevents benchmark artifacts from being tracked in version control
- Aligns with existing pattern of ignoring redbear-mem-metrics directory
- Add GitHub Actions workflow to notify on merged release branch PRs
- Implement HEAD sync check to ensure branch is up-to-date before notification
- Fetch commit messages from merged PR for AI summarization
- Integrate Alibaba Qwen AI to generate Chinese release summaries for QA team
- Send formatted Markdown notifications to WeChat webhook with PR details and AI summary
- Workflow triggers only on final PR merge to release branches to avoid duplicate notifications
- Replace Statement-based implicit memory count (count/3) with actual
MemorySummary node count filtered by DERIVED_FROM_STATEMENT relationship
- Add minimum threshold of 5 MemorySummary nodes before reporting data
- Add _build_empty_profile() to return structured empty profile when
insufficient data exists, skipping unnecessary LLM calls
Added `get_user_by_id_regardless_active` in user repository to support activation/deactivation workflows, updated `user_service` to use it, and refactored `_enrich_release_config` in `app_dsl_service` to accept `default_model_config_id` as a parameter instead of reading from config dict.
