MCP Codebase Insight

# Use Docker for Qdrant Vector Database ## Status Accepted ## Context We need a vector database to store and search through code patterns and documentation embeddings. Qdrant is chosen as our vector database solution, and we need to determine the best way to deploy and manage it. ## Decision Drivers * Ease of deployment and setup * Development environment consistency * Production readiness * Resource management * Scalability * Maintainability ## Considered Options ### Option 1: Docker Container * Use official Qdrant Docker image * Run as containerized service * Manage with Docker Compose for local development * Use Kubernetes for production deployment ### Option 2: Native Installation * Install Qdrant directly on host system * Manage as system service * Configure through system files * Handle updates through package manager ### Option 3: Cloud-Hosted Solution * Use managed Qdrant Cloud service * Pay per usage * Managed infrastructure * Automatic updates and maintenance ## Decision We will use Docker for running Qdrant. This decision is based on several factors: 1. **Development Environment**: Docker provides consistent environment across all developer machines 2. **Easy Setup**: Simple `docker run` command to get started 3. **Resource Isolation**: Container ensures clean resource management 4. **Version Control**: Easy version management through Docker tags 5. **Production Ready**: Same container can be used in production 6. **Scaling**: Can be deployed to Kubernetes when needed ## Expected Consequences ### Positive Consequences * Consistent environment across development and production * Easy setup process for new developers * Clean isolation from other system components * Simple version management * Clear resource boundaries * Easy backup and restore procedures * Portable across different platforms ### Negative Consequences * Additional Docker knowledge required * Small performance overhead from containerization * Need to manage container resources carefully * Additional complexity in monitoring setup ## Pros and Cons of the Options ### Docker Container * ✅ Consistent environment * ✅ Easy setup and teardown * ✅ Good isolation * ✅ Version control * ✅ Production ready * ❌ Container overhead * ❌ Requires Docker knowledge ### Native Installation * ✅ Direct system access * ✅ No containerization overhead * ✅ Full control over configuration * ❌ System-dependent setup * ❌ Potential conflicts with system packages * ❌ More complex version management ### Cloud-Hosted Solution * ✅ No infrastructure management * ✅ Automatic scaling * ✅ Managed backups * ❌ Higher cost * ❌ Less control * ❌ Internet dependency * ❌ Potential latency issues ## Implementation ### Docker Run Command ```bash docker run -d -p 6333:6333 -p 6334:6334 \ -v $(pwd)/qdrant_storage:/qdrant/storage \ qdrant/qdrant ``` ### Docker Compose Configuration ```yaml version: '3.8' services: qdrant: image: qdrant/qdrant ports: - "6333:6333" - "6334:6334" volumes: - qdrant_storage:/qdrant/storage environment: - RUST_LOG=info volumes: qdrant_storage: ``` ## Notes * Monitor container resource usage in production * Set up proper backup procedures for the storage volume * Consider implementing health checks * Document recovery procedures ## Metadata * Created: 2025-03-19 * Last Modified: 2025-03-19 * Author: Development Team * Approvers: Technical Lead, Infrastructure Team * Status: Accepted * Tags: infrastructure, database, docker, vector-search * References: * [Qdrant Docker Documentation](https://qdrant.tech/documentation/guides/installation/#docker) * [Docker Best Practices](https://docs.docker.com/develop/develop-images/dockerfile_best-practices/)