update docs to include model mapping

Author: nullfunc

docs/concepts/managed-llms/openai-access-gateway.md

@@ -34,6 +34,10 @@ services:
 Under the hood, when you use the `model` provider, Defang will deploy the **OpenAI Access Gateway** in a private network. This allows you to use the same code for both local development and cloud deployment.
 The `x-defang-llm` extension is used to configure the appropriate roles and permissions for your service. See the [Managed Language Models](/docs/concepts/managed-llms/managed-language-models/) page for more details.
 
+## Model Mapping
+
+Defang supports model mapping through the openai-access-gateway on AWS and GCP. This takes a model with a Docker naming convention (e.g. ai/lama3.3) and maps it to the closest matching model name on the target platform. If no such match can be found it can fallback onto a known existing model (e.g. ai/mistral). These environment variables are USE_MODEL_MAPPING (default to true) and FALLBACK_MODEL (no default), respectively.
+
 ## Current Support
 
 | Provider | Managed Language Models |

docs/tutorials/deploying-openai-apps-aws-bedrock-gcp-vertex.mdx

@@ -43,15 +43,16 @@ Add **Defang's [openai-access-gateway](https://github.com/DefangLabs/openai-acce
 +    environment:
 +      - OPENAI_API_KEY
 +      - GCP_PROJECT_ID # if using GCP Vertex AI
-+      - GCP_REGION # if using GCP Vertex AI, AWS_REGION not necessary for Bedrock
++      - REGION
 ```
 
 ### Notes:
 
 - The container image is based on [aws-samples/bedrock-access-gateway](https://github.com/aws-samples/bedrock-access-gateway), with enhancements.
 - `x-defang-llm: true` signals to **Defang** that this service should be configured to use target platform AI services.
 - New environment variables:
-  - `GCP_PROJECT_ID` and `GCP_REGION` are needed if using **Vertex AI**. (e.g.` GCP_PROJECT_ID` = my-project-456789 and `GCP_REGION` = us-central1)
+  - `REGION` is the zone where the services runs (for AWS this is equvilent of AWS_REGION)
+  - `GCP_PROJECT_ID` is needed if using **Vertex AI**. (e.g.` GCP_PROJECT_ID` = my-project-456789 and `REGION` = us-central1)
 
 :::tip
 **OpenAI Key**
@@ -106,6 +107,10 @@ You should configure your application to specify the model you want to use.
 
 Choose the correct `MODEL` depending on which cloud provider you are using.
 
+Alternatively, Defang supports model mapping through the openai-access-gateway. This takes a model with a Docker naming convention (e.g. ai/lama3.3) and maps it to
+the closest matching one on the target platform. If no such match can be found it can fallback onto a known existing model (e.g. ai/mistral). These environment
+variables are USE_MODEL_MAPPING (default to true) and FALLBACK_MODEL (no default), respectively.
+
 :::info
 **Choosing the Right Model**
 
@@ -138,7 +143,7 @@ services:
     environment:
       - OPENAI_API_KEY
       - GCP_PROJECT_ID     # required if using Vertex AI
-      - GCP_REGION         # required if using Vertex AI
+      - REGION
 ```
 
 ---
@@ -148,7 +153,7 @@ services:
 | Variable           | AWS Bedrock | GCP Vertex AI |
 |--------------------|-------------|---------------|
 | `GCP_PROJECT_ID`    | _(not used)_| Required      |
-| `GCP_REGION`        | _(not used)_| Required      |
+| `REGION`            | Required| Required      |
 | `MODEL`             | Bedrock model ID | Vertex model path |
 
 ---
@@ -158,4 +163,5 @@ You now have a single app that can:
 - Talk to **AWS Bedrock** or **GCP Vertex AI**
 - Use the same OpenAI-compatible client code
 - Easily switch cloud providers by changing a few environment variables
+:::