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6 Commits

Author SHA1 Message Date
Apple
0c8bef82f4 feat: Add Alateya, Clan, Eonarch agents + fix gateway-router connection 
## Agents Added
- Alateya: R&D, biotech, innovations
- Clan (Spirit): Community spirit agent
- Eonarch: Consciousness evolution agent

## Changes
- docker-compose.node1.yml: Added tokens for all 3 new agents
- gateway-bot/http_api.py: Added configs and webhook endpoints
- gateway-bot/clan_prompt.txt: New prompt file
- gateway-bot/eonarch_prompt.txt: New prompt file

## Fixes
- Fixed ROUTER_URL from :9102 to :8000 (internal container port)
- All 9 Telegram agents now working

## Documentation
- Created PROJECT-MASTER-INDEX.md - single entry point
- Added various status documents and scripts

Tokens configured:
- Helion, NUTRA, Agromatrix (existing)
- Alateya, Clan, Eonarch (new)
- Druid, GreenFood, DAARWIZZ (configured)
 2026-01-28 06:40:34 -08:00
Apple
7b91c8e83c feat(foundation): FOUNDATION_UPDATE implementation 
## Documentation (20 files)
- DAARION Ontology Core v1 (Agent → MicroDAO → Node → District)
- User Onboarding & Identity Layer (DAIS)
- Data Model UPDATE, Event Catalog, Governance & Permissions
- Rooms Layer, City/MicroDAO/Agents/Nodes Interface Architecture
- Helper files: ontology-summary, lifecycles, event-schemas

## Database Migration (027)
- DAIS tables: dais_identities, dais_emails, dais_wallets, dais_keys
- agent_assignments table for Assignment Layer
- rooms table for Rooms Layer
- event_outbox for NATS event delivery
- New enums: agent_role, microdao_type, node_kind, node_status, etc.
- Updated agents, microdaos, nodes tables with ontology fields

## Backend
- DAIS service & routes (/api/v1/dais/*)
- Assignment service & routes (/api/v1/assignments/*)
- Domain types for DAIS and Ontology

## Frontend
- Ontology types (Agent, MicroDAO, Node, DAIS, Assignments)
- API clients for DAIS and Assignments
- UI components: DaisProfileCard, AssignmentsPanel, OntologyBadge

Non-breaking update - all existing functionality preserved.
 2025-11-29 15:24:38 -08:00
Apple
a6e531a098 fix: NODE1_REPAIR - healthchecks, dependencies, SSR env, telegram gateway 
TASK_PHASE_NODE1_REPAIR:
- Fix daarion-web SSR: use CITY_API_BASE_URL instead of 127.0.0.1
- Fix auth API routes: use AUTH_API_URL env var
- Add wget to Dockerfiles for healthchecks (stt, ocr, web-search, swapper, vector-db, rag)
- Update healthchecks to use wget instead of curl
- Fix vector-db-service: update torch==2.4.0, sentence-transformers==2.6.1
- Fix rag-service: correct haystack imports for v2.x
- Fix telegram-gateway: remove msg.ack() for non-JetStream NATS
- Add /health endpoint to nginx mvp-routes.conf
- Add room_role, is_public, sort_order columns to city_rooms migration
- Add TASK_PHASE_NODE1_REPAIR.md and DEPLOY_NODE1_REPAIR.md docs

Previous tasks included:
- TASK 039-044: Orchestrator rooms, Matrix chat cleanup, CrewAI integration
 2025-11-29 05:17:08 -08:00
Apple
4601c6fca8 feat: add Vision Encoder service + Vision RAG implementation 
- Vision Encoder Service (OpenCLIP ViT-L/14, GPU-accelerated)
  - FastAPI app with text/image embedding endpoints (768-dim)
  - Docker support with NVIDIA GPU runtime
  - Port 8001, health checks, model info API

- Qdrant Vector Database integration
  - Port 6333/6334 (HTTP/gRPC)
  - Image embeddings storage (768-dim, Cosine distance)
  - Auto collection creation

- Vision RAG implementation
  - VisionEncoderClient (Python client for API)
  - Image Search module (text-to-image, image-to-image)
  - Vision RAG routing in DAGI Router (mode: image_search)
  - VisionEncoderProvider integration

- Documentation (5000+ lines)
  - SYSTEM-INVENTORY.md - Complete system inventory
  - VISION-ENCODER-STATUS.md - Service status
  - VISION-RAG-IMPLEMENTATION.md - Implementation details
  - vision_encoder_deployment_task.md - Deployment checklist
  - services/vision-encoder/README.md - Deployment guide
  - Updated WARP.md, INFRASTRUCTURE.md, Jupyter Notebook

- Testing
  - test-vision-encoder.sh - Smoke tests (6 tests)
  - Unit tests for client, image search, routing

- Services: 17 total (added Vision Encoder + Qdrant)
- AI Models: 3 (qwen3:8b, OpenCLIP ViT-L/14, BAAI/bge-m3)
- GPU Services: 2 (Vision Encoder, Ollama)
- VRAM Usage: ~10 GB (concurrent)

Status: Production Ready ✅
 2025-11-17 05:24:36 -08:00
Apple
1ed1181105 feat: add RAG quality metrics, optimized prompts, and evaluation tools 
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
9b86f9a694 feat: implement RAG Service MVP with PARSER + Memory integration 
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
 2025-11-16 04:41:53 -08:00