Kubernetes AI CICD Demo Application

I want a working proof-of-concept application that pulls together a modern MLOps stack on Kubernetes. The build should spin up from a clean repo, flow through a CI/CD pipeline, and deploy an end-to-end demo that highlights three AI pillars I care about: Nvidia AI Enterprise for accelerated inference and training, production-grade LLM functionality (LangChain-powered), and Retrieval-Augmented Generation using a vector store.

Core workflow
• Code is pushed → pipeline (GitHub Actions or similar) runs tests, builds the container image, and promotes it to the cluster.
• Helm or ArgoCD handles drift management so that the desired state remains in sync.
• At runtime, the service must auto-scale both CPU and GPU requests, proving horizontal and vertical elasticity.
• Token or currency budgeting is tracked per request so I can see live cost data and later hook it into FinOps dashboards.

Inside the cluster
• Nvidia AI Enterprise stack (Triton, TensorRT, CUDA) installed via the operator.
• LLM endpoints wrapped with LangChain agents that can call external tools.
• A vector database (FAISS, Milvus, or anything OSS) stores embeddings for RAG.
• One sample agent demonstrates question-answering over the vector store; another shows a multi-tool plan/act loop.

What I need from you
1. Terraform or similar IaC scripts for the base GKE/EKS/AKS cluster.
2. CI/CD definitions, Kubernetes manifests/Helm charts, and any ArgoCD configuration.
3. Demo notebooks or curl scripts that hit the endpoints and display: model output, RAG enrichment, token usage, and cost.


Acceptance criteria
• End-to-end pipeline green on a fresh account.
• Pod autoscaling proves GPU/CPU resizing under load.
• LLM call enriched by RAG returns expected citations.
• FinOps metrics exported (Prometheus/OpenCost acceptable).

Keep everything as open source as possible; licence notes if you must use a proprietary component. I’m happy to iterate quickly, so push early versions and we’ll harden them together.