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Merge pull request #35 from vmleon/agentic_rag_automation
Agentic rag automation
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‎agentic_rag/.gitignore‎

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venv/
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env/
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.env
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kubeconfig
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# IDE
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.vscode/
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embeddings/
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chroma_db/
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docs/*.json
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**/.certs
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**/node_modules
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k8s/kustom/demo/config.yaml
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k8s/kustom/demo/wallet/
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**/generated/
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# Terraform
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**/.terraform/*
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*.plan
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*.tfstate
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*.tfstate.*
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crash.log
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crash.*.log
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*.tfvars
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*.tfvars.json
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override.tf
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override.tf.json
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*_override.tf
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*_override.tf.json
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.terraformrc
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terraform.rc
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# Distribution / packaging
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dist/

‎agentic_rag/DEPLOY.md‎

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# Deploy with Terraform and Kustomize
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## TODOS
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- Multiple containers for different functions
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- Gradio
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- Agents / Inference
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- Database Access
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- Hugging face token should be a secret
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- Liveness and Readiness
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- Use Load balancer instead of Gradio Live
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- Autoscaling
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## Deploy Infrastructure
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Install scripts dependencies
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```bash
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cd scripts/ && npm install && cd ..
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```
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Set environment (answer questions) and generate Terraform `tfvars` file.
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```bash
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zx scripts/setenv.mjs
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```
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> Alternative: One liner for the yellow commands (for easy copy paste)
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>
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> ```bash
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> cd tf && terraform init && terraform apply -auto-approve
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> ```
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Come back to root folder
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```bash
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cd ..
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```
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Prepare Kubeconfig and namespace:
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```bash
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zx scripts/kustom.mjs
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```
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## Deploy Application
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Export kubeconfig to get access to the Kubernetes Cluster
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```bash
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export KUBECONFIG="$(pwd)/tf/generated/kubeconfig"
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```
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Check everything works
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```bash
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kubectl cluster-info
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```
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Deploy the production overlay
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```bash
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kubectl apply -k k8s/kustom/overlays/prod
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```
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Check all pods are Ready:
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```bash
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kubectl get po --namespace=agentic-rag
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```
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Get Gradio Live URL:
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```bash
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kubectl logs $(kubectl get po -n agentic-rag -l app=agentic-rag -o name) -n agentic-rag | grep "Running on public URL"
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```
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Open the URL from the command before in your browser.
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Also, you could get the Load Balancer Public IP address:
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```bash
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echo "http://$(kubectl get service \
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-n agentic-rag \
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-o jsonpath='{.items[?(@.spec.type=="LoadBalancer")].status.loadBalancer.ingress[0].ip}')"
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```
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To troubleshoot connect to the container
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```bash
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kubectl exec -it $(kubectl get po -n agentic-rag -l app=agentic-rag -o name) -n agentic-rag -- sh
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```
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## Clean up
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Delete the production overlay
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```bash
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kubectl delete -k k8s/kustom/overlays/prod
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```
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Destroy infrastructure with Terraform.
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```bash
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cd tf
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```
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```bash
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terraform destroy -auto-approve
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```
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```bash
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cd ..
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```
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Clean up the artifacts and config files
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```bash
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zx scripts/clean.mjs
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```

‎agentic_rag/OraDBVectorStore.py‎

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username = credentials.get("ORACLE_DB_USERNAME", "ADMIN")
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password = credentials.get("ORACLE_DB_PASSWORD", "")
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dsn = credentials.get("ORACLE_DB_DSN", "")
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wallet_path = credentials.get("ORACLE_DB_WALLET_LOCATION")
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wallet_password = credentials.get("ORACLE_DB_WALLET_PASSWORD")
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if not password or not dsn:
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raise ValueError("Oracle DB credentials not found in config.yaml. Please set ORACLE_DB_USERNAME, ORACLE_DB_PASSWORD, and ORACLE_DB_DSN.")
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# Connect to the database
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try:
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conn23c = oracledb.connect(user=username, password=password, dsn=dsn)
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if not wallet_path:
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print(f'Connecting (no wallet) to dsn {dsn} and user {username}')
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conn23c = oracledb.connect(user=username, password=password, dsn=dsn)
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else:
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print(f'Connecting (with wallet) to dsn {dsn} and user {username}')
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conn23c = oracledb.connect(user=username, password=password, dsn=dsn,
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config_dir=wallet_path, wallet_location=wallet_path, wallet_password=wallet_password)
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print("Oracle DB Connection successful!")
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except Exception as e:
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print("Oracle DB Connection failed!", e)

‎agentic_rag/k8s/kustom/demo/config.yaml.mustache‎

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HUGGING_FACE_HUB_TOKEN: "{{{ hugging_face_token }}}"
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ORACLE_DB_USERNAME: "{{{ adb_username }}}"
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ORACLE_DB_PASSWORD: "{{{ adb_admin_password }}}"
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ORACLE_DB_DSN: "{{{ adb_service_name }}}"
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ORACLE_DB_WALLET_LOCATION: "{{{ adb_wallet_location }}}"
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ORACLE_DB_WALLET_PASSWORD: "{{{ adb_admin_password }}}"

‎agentic_rag/k8s/kustom/demo/deployment.yaml‎

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apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: agentic-rag
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labels:
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app: agentic-rag
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spec:
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replicas: 1
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selector:
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matchLabels:
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app: agentic-rag
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template:
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metadata:
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labels:
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app: agentic-rag
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spec:
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tolerations:
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- key: "nvidia.com/gpu"
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operator: "Equal"
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value: "present"
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effect: "NoSchedule"
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initContainers:
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- name: unzip
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image: busybox
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command: ["unzip", "/app/walletzip/wallet.zip", "-d", "/app/wallet"]
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volumeMounts:
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- name: wallet-config
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mountPath: /app/walletzip
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- name: wallet-volume
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mountPath: /app/wallet
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containers:
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- name: agentic-rag
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image: python:3.10-slim
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resources:
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requests:
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memory: "8Gi"
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cpu: "2"
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ephemeral-storage: "50Gi" # Add this
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limits:
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memory: "16Gi"
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cpu: "4"
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ephemeral-storage: "100Gi" # Add this
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ports:
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- containerPort: 7860
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name: gradio
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- containerPort: 11434
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name: ollama-api
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volumeMounts:
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- name: config-volume
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mountPath: /app/config.yaml
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subPath: config.yaml
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- name: wallet-config
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mountPath: /app/walletzip
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- name: wallet-volume
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mountPath: /app/wallet
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- name: data-volume
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mountPath: /app/embeddings
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- name: chroma-volume
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mountPath: /app/chroma_db
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- name: ollama-models
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mountPath: /root/.ollama
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command: ["/bin/bash", "-c"]
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args:
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- |
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apt-get update && apt-get install -y git curl gnupg
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# Install NVIDIA drivers and CUDA
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echo "Installing NVIDIA drivers and CUDA..."
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curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
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curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
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sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
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tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
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apt-get update && apt-get install -y nvidia-container-toolkit
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# Verify GPU is available
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echo "Verifying GPU availability..."
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nvidia-smi || echo "WARNING: nvidia-smi command failed. GPU might not be properly configured."
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# Install Ollama
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echo "Installing Ollama..."
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curl -fsSL https://ollama.com/install.sh | sh
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# Configure Ollama to use GPU
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echo "Configuring Ollama for GPU usage..."
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mkdir -p /root/.ollama
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echo '{"gpu": {"enable": true}}' > /root/.ollama/config.json
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# Start Ollama in the background with GPU support
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echo "Starting Ollama service with GPU support..."
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ollama serve &
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# Wait for Ollama to be ready
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echo "Waiting for Ollama to be ready..."
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until curl -s http://localhost:11434/api/tags >/dev/null; do
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sleep 5
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done
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# Verify models are using GPU
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echo "Verifying models are using GPU..."
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curl -s http://localhost:11434/api/tags | grep -q "llama3" && echo "llama3 model is available"
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# Clone and set up the application
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cd /app
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git clone -b agentic_rag_automation https://github.com/vmleon/devrel-labs.git
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cd devrel-labs/agentic_rag
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pip install -r requirements.txt
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# Move config.yaml file to agentic-rag folder
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echo "Copying config.yaml to /app/devrel-labs/agentic_rag/config.yaml"
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cp /app/config.yaml /app/devrel-labs/agentic_rag/config.yaml
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# Start the Gradio app
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echo "Starting Gradio application..."
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python gradio_app.py
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env:
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- name: PYTHONUNBUFFERED
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value: "1"
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- name: OLLAMA_HOST
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value: "http://localhost:11434"
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- name: NVIDIA_VISIBLE_DEVICES
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value: "all"
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- name: NVIDIA_DRIVER_CAPABILITIES
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value: "compute,utility"
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- name: TORCH_CUDA_ARCH_LIST
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value: "7.0;7.5;8.0;8.6"
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volumes:
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- name: config-volume
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configMap:
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name: agentic-rag-config
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- name: wallet-config
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configMap:
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name: wallet-zip
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- name: wallet-volume
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emptyDir:
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sizeLimit: 50Mi
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- name: data-volume
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persistentVolumeClaim:
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claimName: agentic-rag-data-pvc
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- name: chroma-volume
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persistentVolumeClaim:
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claimName: agentic-rag-chroma-pvc
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- name: ollama-models
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persistentVolumeClaim:
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claimName: ollama-models-pvc

‎agentic_rag/k8s/kustom/demo/kustomization.yaml‎

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resources:
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- pvcs.yaml
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- deployment.yaml
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- service.yaml
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configMapGenerator:
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- name: agentic-rag-config
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files:
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- config.yaml
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- name: wallet-zip
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files:
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- wallet/wallet.zip
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namespace: agentic-rag

‎agentic_rag/k8s/kustom/demo/pvcs.yaml‎

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apiVersion: v1
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kind: PersistentVolumeClaim
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metadata:
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name: agentic-rag-data-pvc
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spec:
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accessModes:
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- ReadWriteOnce
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resources:
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requests:
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storage: 50Gi
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---
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apiVersion: v1
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kind: PersistentVolumeClaim
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metadata:
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name: agentic-rag-chroma-pvc
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spec:
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accessModes:
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- ReadWriteOnce
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resources:
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requests:
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storage: 50Gi
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---
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apiVersion: v1
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kind: PersistentVolumeClaim
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metadata:
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name: ollama-models-pvc
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spec:
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accessModes:
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- ReadWriteOnce
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resources:
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requests:
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storage: 50Gi # Larger storage for model files

‎agentic_rag/k8s/kustom/demo/service.yaml‎

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apiVersion: v1
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kind: Service
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metadata:
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name: agentic-rag
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labels:
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app: agentic-rag
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spec:
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type: LoadBalancer # Use NodePort if LoadBalancer is not available
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ports:
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- port: 80
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targetPort: 7860
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protocol: TCP
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name: http
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selector:
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app: agentic-rag

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