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 ✅

docker-compose.yml

@@ -201,6 +201,63 @@ services:
       retries: 3
       start_period: 10s
 
+  # Vision Encoder Service - OpenCLIP for text/image embeddings
+  vision-encoder:
+    build:
+      context: ./services/vision-encoder
+      dockerfile: Dockerfile
+    container_name: dagi-vision-encoder
+    ports:
+      - "8001:8001"
+    environment:
+      - DEVICE=${VISION_DEVICE:-cuda}
+      - MODEL_NAME=${VISION_MODEL_NAME:-ViT-L-14}
+      - MODEL_PRETRAINED=${VISION_MODEL_PRETRAINED:-openai}
+      - NORMALIZE_EMBEDDINGS=true
+      - QDRANT_HOST=qdrant
+      - QDRANT_PORT=6333
+      - QDRANT_ENABLED=true
+    volumes:
+      - ./logs:/app/logs
+      - vision-model-cache:/root/.cache/clip
+    depends_on:
+      - qdrant
+    networks:
+      - dagi-network
+    restart: unless-stopped
+    # GPU support - requires nvidia-docker runtime
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8001/health"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 60s
+
+  # Qdrant Vector Database - for image/text embeddings
+  qdrant:
+    image: qdrant/qdrant:v1.7.4
+    container_name: dagi-qdrant
+    ports:
+      - "6333:6333"  # HTTP API
+      - "6334:6334"  # gRPC API
+    volumes:
+      - qdrant-data:/qdrant/storage
+    networks:
+      - dagi-network
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:6333/healthz"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+
 volumes:
   rag-model-cache:
     driver: local
@@ -208,6 +265,10 @@ volumes:
     driver: local
   postgres-data:
     driver: local
+  vision-model-cache:
+    driver: local
+  qdrant-data:
+    driver: local
 
 networks:
   dagi-network: