TASK 037A: Backend Consistency
- Added db/sql/037_microdao_agent_audit.sql
- Added services/city-service/tools/fix_microdao_agent_consistency.py
- Updated repo_city.get_public_citizens with stricter filtering (node_id, microdao_membership)
- Updated PublicCitizenSummary model to include home_microdao and primary_city_room
- Updated NodeProfile model and get_node_by_id to include microdaos list
TASK 037B: UI Polish
- Updated MicrodaoRoomsSection with role-based colors/icons and mini-map
- Updated /microdao/[slug] with new Hero Block (badges, stats, orchestrator)
- Updated /citizens/[slug] with MicroDAO cross-link in DAIS profile
- Updated /nodes/[nodeId] with MicroDAO Presence section
- Add HomeNodeView model to city-service
- Update get_public_citizens and get_public_citizen_by_slug to JOIN node_cache
- Add HomeNode interface to frontend types
- Display node badge on citizen cards
- Show full home_node info on citizen profile page
- Add migration 013_city_map_coordinates.sql with map coordinates, zones, and agents table
- Add /city/map API endpoint in city-service
- Add /city/agents and /city/agents/online endpoints
- Extend presence aggregator to include agents[] in snapshot
- Add AgentsSource for fetching agent data from DB
- Create CityMap component with interactive room tiles
- Add useCityMap hook for fetching map data
- Update useGlobalPresence to include agents
- Add map/list view toggle on /city page
- Add agent badges to room cards and map tiles
- matrix-gateway: POST /internal/matrix/presence/online endpoint
- usePresenceHeartbeat hook with activity tracking
- Auto away after 5 min inactivity
- Offline on page close/visibility change
- Integrated in MatrixChatRoom component
- matrix-presence-aggregator v2 with SSE endpoint
- Created @presence_daemon Matrix user
- SSE proxy in Next.js /api/presence/stream
- Updated frontend to use SSE instead of WebSocket
- Real-time city online count and room presence
- matrix_provisioning.py: Create Matrix users via Synapse admin API
- Auto-create Matrix account when user registers in DAARION
- Return matrix_user_id in registration response
RAG Service Implementation:
- Create rag-service/ with full structure (config, document_store, embedding, pipelines)
- Document Store: PostgreSQL + pgvector via Haystack
- Embedding: BAAI/bge-m3 (multilingual, 1024 dim)
- Ingest Pipeline: Convert ParsedDocument to Haystack Documents, embed, index
- Query Pipeline: Retrieve documents, generate answers via DAGI Router
- FastAPI endpoints: /ingest, /query, /health
Tests:
- Unit tests for ingest and query pipelines
- E2E test with example parsed JSON
- Test fixtures with real PARSER output example
Router Integration:
- Add mode='rag_query' routing rule in router-config.yml
- Priority 7, uses local_qwen3_8b for RAG queries
Docker:
- Add rag-service to docker-compose.yml
- Configure dependencies (router, city-db)
- Add model cache volume
Documentation:
- Complete README with API examples
- Integration guides for PARSER and Router
Router Configuration:
- Add mode='qa_build' routing rule in router-config.yml
- Priority 8, uses local_qwen3_8b for Q&A generation
2-Stage Q&A Pipeline Tests:
- Create test_qa_pipeline.py with comprehensive tests
- Test prompt building, JSON parsing, router integration
- Mock DAGI Router responses for testing
Region Mode (Grounding OCR):
- Add region_bbox and region_page parameters to ParseRequest
- Support region mode in local_runtime with bbox in prompt
- Update endpoints to accept region parameters (x, y, width, height, page)
- Validate region parameters and filter pages for region mode
- Pass region_bbox through inference pipeline
Updates:
- Update local_runtime to support region_bbox in prompts
- Update inference.py to pass region_bbox to local_runtime
- Update endpoints.py to handle region mode parameters
Prompt Modes Integration:
- Create local_runtime.py with DOTS_PROMPT_MAP
- Map OutputMode to native dots.ocr prompt modes (prompt_layout_all_en, prompt_ocr, etc.)
- Support dict_promptmode_to_prompt from dots.ocr with fallback prompts
- Add layout_only and region modes to OutputMode enum
2-Stage Q&A Pipeline:
- Create qa_builder.py for 2-stage qa_pairs generation
- Stage 1: PARSER (dots.ocr) → raw JSON via prompt_layout_all_en
- Stage 2: LLM (DAGI Router) → Q&A pairs via mode=qa_build
- Update endpoints.py to use 2-stage pipeline for qa_pairs mode
- Add ROUTER_BASE_URL and ROUTER_TIMEOUT to config
Updates:
- Update inference.py to use local_runtime with native prompts
- Update ollama_client.py to use same prompt map
- Add PROMPT_MODES.md documentation
Model Loader:
- Update model_loader.py with complete dots.ocr loading code
- Proper device detection (CUDA/CPU/MPS) with fallback
- Memory optimization (low_cpu_mem_usage)
- Better error handling and logging
- Support for local model paths and HF Hub
Docker:
- Multi-stage Dockerfile (CPU/CUDA builds)
- docker-compose.yml for parser-service
- .dockerignore for clean builds
- Model cache volume for persistence
Configuration:
- Support DOTS_OCR_MODEL_ID and DEVICE env vars (backward compatible)
- Better defaults and environment variable handling
Deployment:
- Add DEPLOYMENT.md with detailed instructions
- Local deployment (venv)
- Docker Compose deployment
- Ollama runtime setup
- Troubleshooting guide
Integration:
- Add parser-service to main docker-compose.yml
- Configure volumes and networks
- Health checks and dependencies
