ok... ok... I'll add it to git...

stacks/qdrant.yml

@@ -0,0 +1,55 @@
+version: '3.8'
+
+# Qdrant Vector Database
+# Purpose: Efficient vector storage for Open WebUI RAG (conversation memory & documents)
+# Ports: 6333 (HTTP API), 6334 (gRPC)
+# GPU: NO - CPU-based vector operations are efficient
+# Storage: SSD for vector data (performance-critical)
+
+services:
+  qdrant:
+    image: qdrant/qdrant:latest
+    container_name: qdrant
+    restart: unless-stopped
+    ports:
+      - "6333:6333"  # HTTP API
+      - "6334:6334"  # gRPC API
+    volumes:
+      # Vector storage on SSD for performance
+      - /home/jpmschweitzer/docker-data/qdrant/storage:/qdrant/storage
+      # Snapshots for backups
+      - /home/jpmschweitzer/docker-data/qdrant/snapshots:/qdrant/snapshots
+    environment:
+      - TZ=Europe/Amsterdam
+    networks:
+      - ai-dataplane
+
+networks:
+  ai-dataplane:
+    external: true
+
+# Qdrant Performance Notes:
+# - Optimized for high-dimensional vectors (embeddings)
+# - Supports HNSW indexing for fast similarity search
+# - Efficient memory usage (~1-2GB for thousands of documents)
+# - No GPU required (CPU operations are fast enough)
+#
+# Storage Estimates:
+# - ~1KB per conversation turn (with embedding)
+# - 10,000 turns = ~10MB
+# - Very efficient for conversation memory
+#
+# After Deployment:
+# 1. Check logs: docker logs qdrant
+# 2. Access UI: http://localhost:6333/dashboard
+# 3. Verify API: curl http://localhost:6333/collections
+#
+# Integration with Open WebUI:
+# - Set VECTOR_DB=qdrant in Open WebUI
+# - Set QDRANT_URL=http://qdrant:6333
+# - Open WebUI will automatically create collections
+#
+# Collections Created:
+# - Documents: User-uploaded files for RAG
+# - Conversations: Chat history for memory
+# - Web search results: Cached search results