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 ✅
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