Vertex AI

Configure Vertex AI as an LLM provider in agentgateway.

Before you begin

Set up an agentgateway proxy.

Set up access to Vertex AI

1. Set up authentication for Vertex AI. Make sure to have your:

   • Google Cloud Project ID
   • Project location, such as us-central1
   • API key or service account credentials

2. Save your Vertex AI API key as an environment variable.

   export VERTEX_AI_API_KEY=<insert your API key>

3. Create a Kubernetes secret to store your Vertex AI API key.

   kubectl apply -f- <<EOF
   apiVersion: v1
   kind: Secret
   metadata:
     name: vertex-ai-secret
     namespace: kgateway-system
   type: Opaque
   stringData:
     Authorization: $VERTEX_AI_API_KEY
   EOF

4. Create an resource to configure an LLM provider that references the AI API key secret.

   kubectl apply -f- <<EOF
   apiVersion: agentgateway.dev/v1alpha1
   kind: 
   metadata:
     name: vertex-ai
     namespace: kgateway-system
   spec:
     ai:
       provider:
         vertexai:
           model: gemini-pro
           projectId: "my-gcp-project"
           region: "us-central1"
     policies:
       auth:
         secretRef:
           name: vertex-ai-secret
   EOF

5. Create an HTTPRoute resource that routes incoming traffic to the . The following example sets up a route on the /openai path to the that you previously created. The URLRewrite filter rewrites the path from /openai to the path of the API in the LLM provider that you want to use, /v1/chat/completions.

   kubectl apply -f- <<EOF
   apiVersion: gateway.networking.k8s.io/v1
   kind: HTTPRoute
   metadata:
     name: vertex-ai
     namespace: kgateway-system
   spec:
     parentRefs:
       - name: agentgateway
         namespace: kgateway-system
     rules:
     - matches:
       - path:
           type: PathPrefix
           value: /vertex
       backendRefs:
       - name: vertex-ai
         namespace: kgateway-system
         group: agentgateway.dev
         kind: 
   EOF

6. Send a request to the LLM provider API. Verify that the request succeeds and that you get back a response from the API.

   Cloud Provider LoadBalancerPort-forward for local testing

   curl "$INGRESS_GW_ADDRESS/vertex" -H content-type:application/json  -d '{
      "model": "",
      "messages": [
        {
          "role": "user",
          "content": "Write me a short poem about Kubernetes and clouds."
        }
      ]
    }' | jq

   curl "localhost:8080/vertex" -H content-type:application/json  -d '{
      "model": "",
      "messages": [
        {
          "role": "user",
          "content": "Write me a short poem about Kubernetes and clouds."
        }
      ]
    }' | jq

   Example output:

   {
     "id": "chatcmpl-vertex-12345",
     "object": "chat.completion",
     "created": 1727967462,
     "model": "gemini-pro",
     "choices": [
       {
         "index": 0,
         "message": {
           "role": "assistant",
           "content": "In the cloud, Kubernetes reigns,\nOrchestrating pods with great care,\nContainers float like clouds,\nScaling up and down,\nAutomation everywhere."
         },
         "finish_reason": "stop"
       }
     ],
     "usage": {
       "prompt_tokens": 12,
       "completion_tokens": 28,
       "total_tokens": 40
     }
   }

Next steps

• Want to use other endpoints than chat completions, such as embeddings or models? Check out the multiple endpoints guide.

• Explore other guides for LLM consumption, such as function calling, model failover, and prompt guards.