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Co-authored-by: Jordan Stephens <[email protected]>

Author: commit111

docs/concepts/managed-llms/managed-language-models.md

@@ -21,7 +21,7 @@ Each cloud provider offers their own managed Large Language Model services. AWS
 
 In order to leverage cloud-native managed language models from your Defang services, all you need to do is add the `x-defang-llm` extension to the service config and Defang will configure the approprate roles and permissions for you.
 
-:::tip
+:::info
 Ensure you have the necessary permissions to access 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).
 :::
 

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

@@ -37,7 +37,11 @@ The `x-defang-llm` extension is used to configure the appropriate roles and perm
 
 ## Model Mapping
 
-Defang supports model mapping through the [openai-access-gateway](https://github.com/DefangLabs/openai-access-gateway) on AWS and GCP. This takes a model with a Docker naming convention (e.g. `ai/llama3.3`) 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.
+Defang supports model mapping through the [openai-access-gateway](https://github.com/DefangLabs/openai-access-gateway) on AWS and GCP. This takes a model with a Docker naming convention (e.g. `ai/llama3.3`) 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`).
+
+This can be configured through the following environment variables:
+* `USE_MODEL_MAPPING` (default to true) - configures whether or not model mapping should be enabled.
+* `FALLBACK_MODEL` (no default) - configure a model which will be used if model mapping fails to find a target model.
 
 ## Current Support
 

docs/tutorials/deploy-openai-apps/aws-bedrock.mdx

@@ -11,8 +11,11 @@ import {useColorMode} from '@docusaurus/theme-common';
 Let's assume you have an app that uses an OpenAI client library and you want to deploy it to the cloud on **AWS Bedrock**.  
 
 This tutorial shows you how **Defang** makes it easy.
+:::info
+You must [configure AWS Bedrock model access](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access-modify.html) for each model you intend to use in your AWS account.
+:::
 
-Suppose you start with a Compose file like this:
+Suppose you start with a `compose.yaml` file with one `app` service, like this:
 
 ```yaml
 services:
@@ -31,9 +34,7 @@ services:
 
 ## Add an LLM Service to Your Compose File
 
-You need to add a new service that acts as a proxy between your app and the backend LLM provider (Bedrock).
-
-Add **Defang's [openai-access-gateway](https://github.com/DefangLabs/openai-access-gateway)** service:
+You can use AWS Bedrock without changing your `app` code by introducing a new [`defangio/openai-access-gateway`](https://github.com/DefangLabs/openai-access-gateway) service. We'll call the new service `llm`. This new service will act as a proxy between your application and AWS Bedrock, and will transparently handle converting your OpenAI requests into AWS Bedrock requests and Bedrock responses into OpenAI responses. This allows you to use AWS Bedrock with your existing OpenAI client SDK.
 
 ```diff
 +  llm:
@@ -161,4 +162,4 @@ You now have a single app that can:
 
 - Talk to **AWS Bedrock**
 - Use the same OpenAI-compatible client code
-- Easily switch cloud providers by changing a few environment variables
+- Easily switch between models or cloud providers by changing a few environment variables

docs/tutorials/deploy-openai-apps/gcp-vertex.mdx

@@ -11,8 +11,11 @@ import {useColorMode} from '@docusaurus/theme-common';
 Let's assume you have an application that uses an OpenAI client library and you want to deploy it to the cloud using **GCP Vertex AI**.
 
 This tutorial shows you how **Defang** makes it easy.
+:::info
+You must [configure GCP Vertex AI model access](https://cloud.google.com/vertex-ai/generative-ai/docs/control-model-access) for each model you intend to use in your GCP account.
+:::
 
-Suppose you start with a Compose file like this:
+Suppose you start with a `compose.yaml` file with one `app` service, like this:
 
 ```yaml
 services:
@@ -31,9 +34,7 @@ services:
 
 ## Add an LLM Service to Your Compose File
 
-You need to add a new service that acts as a proxy between your app and the backend LLM provider (Vertex AI).
-
-Add **Defang's [openai-access-gateway](https://github.com/DefangLabs/openai-access-gateway)** service:
+You can use Vertex AI without changing your `app` code by introducing a new [`defangio/openai-access-gateway`](https://github.com/DefangLabs/openai-access-gateway) service. We'll call the new service `llm`. This new service will act as a proxy between your application and Vertex AI, and will transparently handle converting your OpenAI requests into Vertex AI requests and Vertex AI responses into OpenAI responses. This allows you to use Vertex AI with your existing OpenAI client SDK.
 
 ```diff
 +  llm:
@@ -54,8 +55,8 @@ Add **Defang's [openai-access-gateway](https://github.com/DefangLabs/openai-acce
 - 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:
-  - `REGION` is the zone where the services runs (e.g. us-central1)
-  - `GCP_PROJECT_ID` is your project to deploy to (e.g. my-project-456789)
+  - `REGION` is the zone where the services runs (e.g. `us-central1`)
+  - `GCP_PROJECT_ID` is your project to deploy to (e.g. `my-project-456789`)
 
 :::tip
 **OpenAI Key**
@@ -148,7 +149,7 @@ services:
         mode: host
     environment:
       - OPENAI_API_KEY
-      - GCP_PROJECT_ID     # required if using GCP Vertex AI
+      - GCP_PROJECT_ID
       - REGION
 ```
 
@@ -168,4 +169,4 @@ You now have a single app that can:
 
 - Talk to **GCP Vertex AI**
 - Use the same OpenAI-compatible client code
-- Easily switch cloud providers by changing a few environment variables
+- Easily switch between models or cloud providers by changing a few environment variables