mesheryctl design import -f https://raw.githubusercontent.com/meshery/meshery.io/master/catalog/1a68c515-8612-4e40-a515-1e93726cb265/0.0.3/design.yml
What This Design Does:
A comprehensive cloud-native architecture designed specifically for Retrieval-Augmented Generation (RAG) systems and AI agents. This blueprint seamlessly connects a Next.js client interface with a robust Django backend API. To ensure high performance, it integrates a Redis caching layer for rapid memory retrieval and a Milvus vector database, which is heavily optimized for storing and querying chunked document embeddings. This infrastructure provides a complete, scalable foundation for deploying modern AI applications on Kubernetes.
Design Snapshot
Caveats and Consideration:
torage: This blueprint uses standard Kubernetes deployments for the vector database. For a production environment, you will need to attach Persistent Volume Claims (PVCs) to the Milvus StatefulSet to ensure your embedded data is not lost if the pod restarts. Networking: The Next.js frontend is exposed via a LoadBalancer on port 80, while the Django API, Redis cache, and Milvus DB are secured internally using ClusterIP services. Configuration: Ensure you inject the appropriate environment variables (like database connection strings and AI model API keys) into the Django and Next.js containers before deploying to a live cluster.
Compatibility:
Design Details
CATEGORY
deploymentCREATED BY
CREATED AT
August 04, 2026VERSION
0.0.3-
MODELS
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