- 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
- Rename workflow from "Release Notify (Ali AI Final)" to "Release Notify Workflow" for clarity
- Replace jq multi-line argument construction with printf for better readability
- Simplify payload generation by building content string separately before passing to jq
- Reduce complexity of nested jq arguments while maintaining identical output format
- Add new GitHub Actions workflow to notify WeChat on release branch merges
- Implement HEAD sync check to prevent race conditions with GitHub API
- Add commit validation to ensure PR is the latest merge to release branch
- Fetch PR commits and generate AI summary using Alibaba Qwen API
- Send formatted Markdown notification to WeChat webhook with release details
- Include branch, author, PR title, and AI-generated change summary in notification
- 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
Multiple services were performing individual database queries for FileMetadata when resolving missing file names/sizes. This change batches the queries using `in_()` to reduce database round trips and improve performance.
feat(app): support file metadata in chat messages and DSL app overwrite
- Extended chat message file objects with `name`, `size`, and `file_type` fields across app_chat_service and workflow_service
- Added ability to overwrite existing app configurations via DSL import in app_dsl_service, including type validation and config update logic for AgentConfig, MultiAgentConfig, and WorkflowConfig
- 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.
- Replace storage_services/search with new read_services/memory_search structure
- Implement content_search and perceptual_search strategies
- Add query_preprocessor for search optimization
- Create memory_service as unified interface
- Update celery_app and graph_search for new architecture
- Add enums for memory operations
- Implement base_pipeline and memory_read pipeline patterns
