Revise model choice for managed LLM samples

samples/managed-llm-provider/README.md

@@ -14,6 +14,8 @@ You can configure the `MODEL` and `ENDPOINT_URL` for the LLM separately for loca
 
 Ensure you have enabled model access for the model you intend to use. To do this, you can check your [AWS Bedrock model access](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access-modify.html) or [GCP Vertex AI model access](https://cloud.google.com/vertex-ai/generative-ai/docs/control-model-access).
 
+To learn about available LLM models in Defang, please see our [Model Mapping documentation](https://docs.defang.io/docs/concepts/managed-llms/openai-access-gateway#model-mapping). 
+
 For more about Managed LLMs in Defang, please see our [Managed LLMs documentation](https://docs.defang.io/docs/concepts/managed-llms/managed-language-models).
 
 ### Docker Model Provider
@@ -36,18 +38,6 @@ To run the application locally, you can use the following command:
 docker compose -f compose.dev.yaml up --build
 ```
 
-## Configuration
-
-For this sample, you will need to provide the following [configuration](https://docs.defang.io/docs/concepts/configuration): 
-
-> Note that if you are using the 1-click deploy option, you can set these values as secrets in your GitHub repository and the action will automatically deploy them for you.
-
-### `MODEL`
-The Model ID of the LLM you are using for your application. For example, `anthropic.claude-3-haiku-20240307-v1:0`.
-```bash
-defang config set MODEL
-```
-
 ## Deployment
 
 > [!NOTE]

samples/managed-llm-provider/compose.yaml

@@ -8,7 +8,8 @@ services:
     restart: always
     environment:
       - ENDPOINT_URL=http://llm/api/v1/chat/completions   # endpoint to the Provider Service
-      - MODEL=anthropic.claude-3-haiku-20240307-v1:0   # LLM model ID used in the Provider Service
+      - MODEL=us.amazon.nova-micro-v1:0  # LLM model ID used in the Provider Service
+      # For other models, see https://docs.defang.io/docs/concepts/managed-llms/openai-access-gateway#model-mapping
     healthcheck:
       test: ["CMD", "python3", "-c", "import sys, urllib.request; urllib.request.urlopen(sys.argv[1]).read()", "http://localhost:8000/"]
       interval: 30s

samples/managed-llm/README.md

@@ -17,6 +17,8 @@ You can configure the `MODEL` and `ENDPOINT_URL` for the LLM separately for loca
 
 Ensure you have enabled model access for the model you intend to use. To do this, you can check your [AWS Bedrock model access](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access-modify.html) or [GCP Vertex AI model access](https://cloud.google.com/vertex-ai/generative-ai/docs/control-model-access).
 
+To learn about available LLM models in Defang, please see our [Model Mapping documentation](https://docs.defang.io/docs/concepts/managed-llms/openai-access-gateway#model-mapping). 
+
 For more about Managed LLMs in Defang, please see our [Managed LLMs documentation](https://docs.defang.io/docs/concepts/managed-llms/managed-language-models).
 
 ### Defang OpenAI Access Gateway
@@ -39,18 +41,6 @@ To run the application locally, you can use the following command:
 docker compose -f compose.dev.yaml up --build
 ```
 
-## Configuration
-
-For this sample, you will need to provide the following [configuration](https://docs.defang.io/docs/concepts/configuration): 
-
-> Note that if you are using the 1-click deploy option, you can set these values as secrets in your GitHub repository and the action will automatically deploy them for you.
-
-### `MODEL`
-The Model ID of the LLM you are using for your application. For example, `anthropic.claude-3-haiku-20240307-v1:0`.
-```bash
-defang config set MODEL
-```
-
 ## Deployment
 
 > [!NOTE]

samples/managed-llm/compose.yaml

@@ -8,7 +8,8 @@ services:
     restart: always
     environment:
       - ENDPOINT_URL=http://llm/api/v1/chat/completions   # endpoint to the gateway service
-      - MODEL=anthropic.claude-3-haiku-20240307-v1:0  # LLM model ID used for the gateway
+      - MODEL=us.amazon.nova-micro-v1:0  # LLM model ID used for the gateway. 
+       # For other models, see https://docs.defang.io/docs/concepts/managed-llms/openai-access-gateway#model-mapping
       - OPENAI_API_KEY=FAKE_TOKEN   # the actual value will be ignored when using the gateway, but it should match the one in the llm service
     healthcheck:
       test: ["CMD", "python3", "-c", "import sys, urllib.request; urllib.request.urlopen(sys.argv[1]).read()", "http://localhost:8000/"]
@@ -29,8 +30,6 @@ services:
         mode: host
     environment:
       - OPENAI_API_KEY=FAKE_TOKEN # this value must match the one in the app service
-      - USE_MODEL_MAPPING=false
-      - DEBUG=true
       # if using GCP for BYOC deployment, add these environment variables:
       # - GCP_PROJECT_ID=${GCP_PROJECT_ID}
       # - GCP_REGION=${GCP_REGION}