- Replace hardcoded user placeholder name lists in write_tools and
user_memory_service with shared _USER_PLACEHOLDER_NAMES constant
- Filter user placeholder names during alias merging in _merge_attribute
to prevent cross-role alias contamination on non-user entities
- Use toLower() in Cypher query for case-insensitive name matching
- Change PgSQL->Neo4j alias sync condition from 'if pg_aliases' to
'if info is not None' so empty aliases correctly clear stale data
- Skip alias merging for user entities during dedup (_merge_attribute and
_merge_entities_with_aliases) to prevent dirty data from overwriting
PgSQL authoritative aliases
- Add PgSQL→Neo4j alias sync after Neo4j write in write_tools to
ensure Neo4j user entities always reflect the PgSQL source
- Remove deduped_aliases (Neo4j history) from alias sync in
extraction_orchestrator, only append newly extracted aliases to PgSQL
- Guard Neo4j MERGE cypher to preserve existing aliases for user
entities (name IN ['用户','我','User','I'])
- Fix emotion_analytics_service query to use ExtractedEntity label
and entity_type property
- Replace curl with urllib.request for API calls to improve portability
- Move API key to environment variable for better security practices
- Inline Python script using heredoc for cleaner workflow definition
- Add intermediate file (ai_summary.txt) to separate concerns between API call and output handling
- Simplify JSON payload construction using Python's json module
- Improve error handling with fallback message for failed AI generation
- Update prompt instruction to request numbered list format
- Remove title and preamble from AI output for cleaner formatting
- Improve clarity by specifying "要点" (key points) in prompt
- Enhance consistency of release notification messages
- Extract WeChat notification payload construction from inline curl command
- Move environment variables to explicit env section for clarity
- Build JSON payload using Python for better string handling and readability
- Write payload to temporary file and pass to curl via -d @wechat.json
- Improves maintainability and reduces shell string escaping complexity
- Remove build_wechat_payload.py script and consolidate payload construction directly in workflow
- Eliminate intermediate environment variables and file I/O operations for cleaner execution
- Inline AI summary payload generation into curl request
- Inline WeChat notification payload generation into curl request
- Remove unnecessary checkout step since script is no longer needed
- Simplify workflow by reducing file dependencies and improving readability
- 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.
