feat: align docs with demo code and rename env var

docs/source/message_api.md

@@ -87,37 +87,48 @@ TGI can be deployed on various cloud providers for scalable and robust text gene
 
 ## Amazon SageMaker
 
-Amazon SageMaker allows two routes: `/invocations` and `/ping` (or `/health`) for health checks. By default, we map `/generate` to `/invocations`. However, SageMaker does not allow requests to any other routes. 
+To enable the Messages API in Amazon SageMaker you need to set the environment variable `MESSAGES_API_ENABLED=true`. 
 
-To provide the new feature of Messages API, we have introduced an environment variable `OAI_ENABLED`. If `OAI_ENABLED=true`, the `chat_completions` method is used when `/invocations` is called, otherwise it defaults to `generate`. This allows users to opt in for the OAI format.
+This will modify the `/invocations` route to accept Messages dictonaries consisting out of role and content. See the example below on how to deploy Llama with the new Messages API.
 
-Here's an example of running the router with `OAI_ENABLED` set to `true`:
+```python
+import json
+import sagemaker
+import boto3
+from sagemaker.huggingface import HuggingFaceModel, get_huggingface_llm_image_uri
 
-```bash
-OAI_ENABLED=true text-generation-launcher --model-id <MODEL-ID>
-```
+try:
+	role = sagemaker.get_execution_role()
+except ValueError:
+	iam = boto3.client('iam')
+	role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn']
 
-And here's an example request:
+# Hub Model configuration. https://huggingface.co/models
+hub = {
+	'HF_MODEL_ID':'HuggingFaceH4/zephyr-7b-beta',
+	'SM_NUM_GPUS': json.dumps(1),
+    'MESSAGES_API_ENABLED': True
+}
 
-```bash
-curl <SAGEMAKER-ENDPOINT>/invocations \
-    -X POST \
-    -d '{
-  "model": "tgi",
-  "messages": [
-    {
-      "role": "system",
-      "content": "You are a helpful assistant."
-    },
-    {
-      "role": "user",
-      "content": "What is deep learning?"
-    }
-  ],
-  "stream": true,
-  "max_tokens": 20
-}' \
-    -H 'Content-Type: application/json' | jq 
-```
+# create Hugging Face Model Class
+huggingface_model = HuggingFaceModel(
+	image_uri=get_huggingface_llm_image_uri("huggingface",version="1.4.0"),
+	env=hub,
+	role=role, 
+)
 
-Please let us know if any naming changes are needed or if any other routes need similar functionality.
+# deploy model to SageMaker Inference
+predictor = huggingface_model.deploy(
+	initial_instance_count=1,
+	instance_type="ml.g5.2xlarge",
+	container_startup_health_check_timeout=300,
+  )
+  
+# send request
+predictor.predict({
+"messages": [
+        {"role": "system", "content": "You are a helpful assistant." },
+        {"role": "user", "content": "What is deep learning?"}
+    ]
+})
+```

router/src/main.rs

@@ -72,7 +72,7 @@ struct Args {
     #[clap(long, env)]
     ngrok_edge: Option<String>,
     #[clap(long, env, default_value_t = false)]
-    chat_enabled_api: bool,
+    messages_api_enabled: bool,
 }
 
 #[tokio::main]
@@ -104,7 +104,7 @@ async fn main() -> Result<(), RouterError> {
         ngrok,
         ngrok_authtoken,
         ngrok_edge,
-        chat_enabled_api,
+        messages_api_enabled,
     } = args;
 
     // Launch Tokio runtime
@@ -348,7 +348,7 @@ async fn main() -> Result<(), RouterError> {
         ngrok_authtoken,
         ngrok_edge,
         tokenizer_config,
-        chat_enabled_api,
+        messages_api_enabled,
     )
     .await?;
     Ok(())

router/src/server.rs

@@ -708,7 +708,7 @@ pub async fn run(
     ngrok_authtoken: Option<String>,
     ngrok_edge: Option<String>,
     tokenizer_config: HubTokenizerConfig,
-    chat_enabled_api: bool,
+    messages_api_enabled: bool,
 ) -> Result<(), axum::BoxError> {
     // OpenAPI documentation
     #[derive(OpenApi)]
@@ -872,7 +872,7 @@ pub async fn run(
         .route("/metrics", get(metrics));
 
     // Conditional AWS Sagemaker route
-    let aws_sagemaker_route = if chat_enabled_api {
+    let aws_sagemaker_route = if messages_api_enabled {
         Router::new().route("/invocations", post(chat_completions)) // Use 'chat_completions' for OAI_ENABLED
     } else {
         Router::new().route("/invocations", post(compat_generate)) // Use 'compat_generate' otherwise