docs: update BBR guide

mkdocs.yml

@@ -12,6 +12,7 @@ theme:
   logo: images/logo/logo-text-large-horizontal-white.png
   favicon: images/favicon-64.png
   features:
+    - content.code.annotate
     - search.highlight
     - navigation.tabs
     - navigation.top
@@ -55,7 +56,7 @@ nav:
         Design Principles: concepts/design-principles.md
         Conformance: concepts/conformance.md
         Roles and Personas: concepts/roles-and-personas.md
-    - Implementations: 
+    - Implementations:
       - Gateways: implementations/gateways.md
       - Model Servers: implementations/model-servers.md
     - FAQ: faq.md
@@ -70,7 +71,7 @@ nav:
         - InferencePool Rollout: guides/inferencepool-rollout.md
       - Metrics and Observability: guides/metrics-and-observability.md
       - Configuration Guide:
-          - Configuring the plugins via configuration files or text: guides/epp-configuration/config-text.md    
+          - Configuring the plugins via configuration files or text: guides/epp-configuration/config-text.md
           - Prefix Cache Aware Plugin: guides/epp-configuration/prefix-aware.md
       - Troubleshooting Guide: guides/troubleshooting.md
     - Implementer Guides:

site-src/guides/serve-multiple-genai-models.md

@@ -1,18 +1,53 @@
 # Serve multiple generative AI models
-A company wants to deploy multiple large language models (LLMs) to serve different workloads. 
-For example, they might want to deploy a Gemma3 model for a chatbot interface and a Deepseek model for a recommendation application. 
+
+A company wants to deploy multiple large language models (LLMs) to a cluster to serve different workloads.
+For example, they might want to deploy a Gemma3 model for a chatbot interface and a DeepSeek model for a recommendation application.
 The company needs to ensure optimal serving performance for these LLMs.
-By using an Inference Gateway, you can deploy these LLMs on your cluster with your chosen accelerator configuration in an `InferencePool`. 
-You can then route requests based on the model name (such as "chatbot" and "recommender") and the `Criticality` property.
+By using an Inference Gateway, you can deploy these LLMs on your cluster with your chosen accelerator configuration in an `InferencePool`.
+You can then route requests based on the model name (such as `chatbot` and `recommender`) and the `Criticality` property.
 
 ## How
+
 The following diagram illustrates how an Inference Gateway routes requests to different models based on the model name.
-The model name is extracted by [Body-Based routing](https://github.com/kubernetes-sigs/gateway-api-inference-extension/blob/main/pkg/bbr/README.md)
+The model name is extracted by [Body-Based routing](https://github.com/kubernetes-sigs/gateway-api-inference-extension/blob/main/pkg/bbr/README.md) (BBR)
  from the request body to the header. The header is then matched to dispatch
  requests to different `InferencePool` (and their EPPs) instances.
 ![Serving multiple generative AI models](../images/serve-mul-gen-AI-models.png)
 
+### Deploy Body-Based Routing
+
+To enable body-based routing, you need to deploy the Body-Based Routing ExtProc server using Helm. Depending on your Gateway provider, you can use one of the following commands:
+
+=== "GKE"
+
+    ```bash
+    helm install body-based-router \
+      --set provider.name=gke \
+      --version v0.5.1 \
+      oci://registry.k8s.io/gateway-api-inference-extension/charts/body-based-routing
+    ```
+
+=== "Istio"
+
+    ```bash
+    helm install body-based-router \
+      --set provider.name=istio \
+      --version v0.5.1 \
+      oci://registry.k8s.io/gateway-api-inference-extension/charts/body-based-routing
+    ```
+
+=== "Other"
+
+    ```bash
+    helm install body-based-router \
+      --version v0.5.1 \
+      oci://registry.k8s.io/gateway-api-inference-extension/charts/body-based-routing
+    ```
+
+### Configure HTTPRoute
+
 This example illustrates a conceptual example regarding how to use the `HTTPRoute` object to route based on model name like “chatbot” or “recommender” to `InferencePool`.
+
 ```yaml
 apiVersion: gateway.networking.k8s.io/v1
 kind: HTTPRoute
@@ -25,8 +60,7 @@ spec:
   - matches:
     - headers:
       - type: Exact
-        #Body-Based routing(https://github.com/kubernetes-sigs/gateway-api-inference-extension/blob/main/pkg/bbr/README.md) is being used to copy the model name from the request body to the header.
-        name: X-Gateway-Model-Name
+        name: X-Gateway-Model-Name # (1)!
         value: chatbot
       path:
         type: PathPrefix
@@ -37,38 +71,74 @@ spec:
   - matches:
     - headers:
       - type: Exact
-        #Body-Based routing(https://github.com/kubernetes-sigs/gateway-api-inference-extension/blob/main/pkg/bbr/README.md) is being used to copy the model name from the request body to the header.
-        name: X-Gateway-Model-Name
+        name: X-Gateway-Model-Name # (2)!
         value: recommender
       path:
         type: PathPrefix
         value: /
     backendRefs:
     - name: deepseek-r1
-      kind: InferencePool     
+      kind: InferencePool
 ```
 
+1. [BBR](https://github.com/kubernetes-sigs/gateway-api-inference-extension/blob/main/pkg/bbr/README.md) is being used to copy the model name from the request body to the header with key `X-Gateway-Model-Name`. The header can then be used in the `HTTPRoute` to route requests to different `InferencePool` instances.
+2. [BBR](https://github.com/kubernetes-sigs/gateway-api-inference-extension/blob/main/pkg/bbr/README.md) is being used to copy the model name from the request body to the header with key `X-Gateway-Model-Name`. The header can then be used in the `HTTPRoute` to route requests to different `InferencePool` instances.
+
 ## Try it out
 
 1. Get the gateway IP:
 ```bash
 IP=$(kubectl get gateway/inference-gateway -o jsonpath='{.status.addresses[0].value}'); PORT=80
 ```
-2. Send a few requests to model "chatbot" as follows:
-```bash
-curl -i ${IP}:${PORT}/v1/completions -H 'Content-Type: application/json' -d '{
-"model": "chatbot",
-"prompt": "What is the color of the sky",
-"max_tokens": 100,
-"temperature": 0
-}'
-```
-3. Send a few requests to model "recommender" as follows:
-```bash
-curl -i ${IP}:${PORT}/v1/completions -H 'Content-Type: application/json' -d '{
-"model": "recommender",
-"prompt": "Give me restaurant recommendations in Paris",
-"max_tokens": 100,
-"temperature": 0
-}'
-```
+
+=== "Chat Completions API"
+
+      1. Send a few requests to model `chatbot` as follows:
+      ```bash
+      curl -X POST -i ${IP}:${PORT}/v1/chat/completions \
+        -H "Content-Type: application/json" \
+        -d '{
+          "model": "chatbot",
+          "messages": [{"role": "user", "content": "What is the color of the sky?"}],
+          "max_tokens": 100,
+          "temperature": 0
+        }'
+      ```
+
+      2. Send a few requests to model `recommender` as follows:
+      ```bash
+      curl -X POST -i ${IP}:${PORT}/v1/chat/completions \
+        -H "Content-Type: application/json" \
+        -d '{
+          "model": "recommender",
+          "messages": [{"role": "user", "content": "Give me restaurant recommendations in Paris"}],
+          "max_tokens": 100,
+          "temperature": 0
+        }'
+      ```
+
+=== "Completions API"
+
+      1. Send a few requests to model `chatbot` as follows:
+      ```bash
+      curl -X POST -i ${IP}:${PORT}/v1/completions \
+        -H 'Content-Type: application/json' \
+        -d '{
+          "model": "chatbot",
+          "prompt": "What is the color of the sky",
+          "max_tokens": 100,
+          "temperature": 0
+        }'
+      ```
+
+      2. Send a few requests to model `recommender` as follows:
+      ```bash
+      curl -X POST -i ${IP}:${PORT}/v1/completions \
+        -H 'Content-Type: application/json' \
+        -d '{
+          "model": "recommender",
+          "prompt": "Give me restaurant recommendations in Paris",
+          "max_tokens": 100,
+          "temperature": 0
+        }'
+      ```