Apple
134c044c21
feat: Behavior Policy v1 - Silent-by-default + Short-first + Media-no-comment
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NODA1 agents now:
- Don't respond to broadcasts/posters/announcements without direct mention
- Don't respond to media (photo/link) without explicit question
- Keep responses short (1-2 sentences by default)
- No emoji, no "ready to help", no self-promotion
Added:
- behavior_policy.py: detect_directed_to_agent(), detect_broadcast_intent(), should_respond()
- behavior_policy_v1.txt: unified policy block for all prompts
- Pre-LLM check in http_api.py: skip Router call if should_respond=False
- NO_OUTPUT handling: don't send to Telegram if LLM returns empty
- Updated all 9 agent prompts with Behavior Policy v1
- Unit and E2E tests for 5 acceptance cases
2026-02-04 09:03:14 -08:00
Apple
bca81dc719
feat: Node Self-Healing, DAGI Audit, Agent Prompts, Infra Invariants
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### Backend (city-service)
- Node Registry + Self-Healing API (migration 039)
- Improved get_all_nodes() with robust fallback for node_registry/node_cache
- Agent Prompts Runtime API for DAGI Router integration
- DAGI Router Audit endpoints (phantom/stale detection)
- Node Agents API (Guardian/Steward)
- Node metrics extended (CPU/GPU/RAM/Disk)
### Frontend (apps/web)
- Node Directory with improved error handling
- Node Cabinet with metrics cards
- DAGI Router Card component
- Node Metrics Card component
- useDAGIAudit hook
### Scripts
- check-invariants.py - deploy verification
- node-bootstrap.sh - node self-registration
- node-guardian-loop.py - continuous self-healing
- dagi_agent_audit.py - DAGI audit utility
### Migrations
- 034: Agent prompts seed
- 035: Agent DAGI audit
- 036: Node metrics extended
- 037: Node agents complete
- 038: Agent prompts full coverage
- 039: Node registry self-healing
### Tests
- test_infra_smoke.py
- test_agent_prompts_runtime.py
- test_dagi_router_api.py
### Documentation
- DEPLOY_CHECKLIST_2024_11_30.md
- Multiple TASK_PHASE docs
2025-11-30 13:52:01 -08:00
Apple
1ed1181105
feat: add RAG quality metrics, optimized prompts, and evaluation tools
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Optimized Prompts:
- Create utils/rag_prompt_builder.py with citation-optimized prompts
- Specialized for DAO tokenomics and technical documentation
- Proper citation format [1], [2] with doc_id, page, section
- Memory context integration (facts, events, summaries)
- Token count estimation
RAG Service Metrics:
- Add comprehensive logging in query_pipeline.py
- Log: question, doc_ids, scores, retrieval method, timing
- Track: retrieval_time, total_query_time, documents_found, citations_count
- Add metrics in ingest_pipeline.py: pages_processed, blocks_processed, pipeline_time
Router Improvements:
- Use optimized prompt builder in _handle_rag_query()
- Add graceful fallback: if RAG unavailable, use Memory only
- Log prompt token count, RAG usage, Memory usage
- Return detailed metadata (rag_used, memory_used, citations_count, metrics)
Evaluation Tools:
- Create tests/rag_eval.py for systematic quality testing
- Test fixed questions with expected doc_ids
- Save results to JSON and CSV
- Compare RAG Service vs Router results
- Track: citations, expected docs found, query times
Documentation:
- Create docs/RAG_METRICS_PLAN.md
- Plan for Prometheus metrics collection
- Grafana dashboard panels and alerts
- Implementation guide for metrics
2025-11-16 05:12:19 -08:00
Apple
382e661f1f
feat: complete RAG pipeline integration (ingest + query + Memory)
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Parser Service:
- Add /ocr/ingest endpoint (PARSER → RAG in one call)
- Add RAG_BASE_URL and RAG_TIMEOUT to config
- Add OcrIngestResponse schema
- Create file_converter utility for PDF/image → PNG bytes
- Endpoint accepts file, dao_id, doc_id, user_id
- Automatically parses with dots.ocr and sends to RAG Service
Router Integration:
- Add _handle_rag_query() method in RouterApp
- Combines Memory + RAG → LLM pipeline
- Get Memory context (facts, events, summaries)
- Query RAG Service for documents
- Build prompt with Memory + RAG documents
- Call LLM provider with combined context
- Return answer with citations
Clients:
- Create rag_client.py for Router (query RAG Service)
- Create memory_client.py for Router (get Memory context)
E2E Tests:
- Create e2e_rag_pipeline.sh script for full pipeline test
- Test ingest → query → router query flow
- Add E2E_RAG_README.md with usage examples
Docker:
- Add RAG_SERVICE_URL and MEMORY_SERVICE_URL to router environment
2025-11-16 05:02:14 -08:00
