move llm pages into folders

Author: commit111

docs/tutorials/deploy-openai-apps.mdx

@@ -0,0 +1,5 @@
+{
+  "label": "Deploy OpenAI Apps on Managed LLMs",
+  "position": 45,
+  "collapsible": true
+}

docs/tutorials/deploy-openai-apps/_category_.json

@@ -1,15 +1,17 @@
 ---
-title: Deploy OpenAI Apps to AWS Bedrock
+title: AWS Bedrock
 sidebar_position: 50
 ---
+import React from 'react';
+import {useColorMode} from '@docusaurus/theme-common';
 
 # Deploy OpenAI Apps to AWS Bedrock
 
 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.
 
-Suppose you start with a compose file like this:
+Suppose you start with a Compose file like this:
 
 ```yaml
 services:
@@ -107,16 +109,15 @@ Choose the correct `MODEL` depending on which cloud provider you are using.
 :::info
 **Choosing the Right Model**
 
-- For **AWS Bedrock**, use a Bedrock model ID (e.g., `anthropic.claude-3-sonnet-20240229-v1:0`) [See available Bedrock models](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html).
+- For **AWS Bedrock**, use a Bedrock model ID (e.g., `anthropic.claude-3-sonnet-20240229-v1:0`). [See available Bedrock models](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html).
 :::
 
 Alternatively, Defang supports model mapping through the [openai-access-gateway](https://github.com/DefangLabs/openai-access-gateway). This takes a model with a Docker naming convention (e.g. `ai/llama3.3`) and maps it to
 the closest equivalent on the target platform. If no such match can be found ,a fallback can be defined to use 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
-# Complete Example Compose File
+
+## Complete Example Compose File
 
 ```yaml
 services:
@@ -146,7 +147,7 @@ services:
 
 ---
 
-# Environment Variable Matrix
+## Environment Variable Matrix
 
 | Variable           | AWS Bedrock |
 |--------------------|-------------|
@@ -160,5 +161,3 @@ 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
-:::
-

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

@@ -0,0 +1,11 @@
+---
+title: Deploy OpenAI Apps on Managed LLMs
+sidebar_position: 45
+---
+
+# Deploy OpenAI Apps on Managed LLMs
+
+Defang currently supports using Managed LLMs with AWS Bedrock and GCP Vertex AI. Follow the link below for your specific platform.
+
+- [AWS Bedrock](/docs/tutorials/deploy-openai-apps/aws-bedrock/)
+- [GCP Vertex AI](/docs/tutorials/deploy-openai-apps/gcp-vertex/)

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

@@ -1,15 +1,17 @@
 ---
-title: Deploy OpenAI Apps to GCP Vertex AI
+title: GCP Vertex AI
 sidebar_position: 50
 ---
+import React from 'react';
+import {useColorMode} from '@docusaurus/theme-common';
 
 # Deploy OpenAI Apps to GCP Vertex AI
 
 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.
 
-Suppose you start with a compose file like this:
+Suppose you start with a Compose file like this:
 
 ```yaml
 services:
@@ -120,8 +122,7 @@ the closest matching one on the target platform. If no such match can be found,
 variables are `USE_MODEL_MAPPING` (default to true) and `FALLBACK_MODEL` (no default), respectively.
 
 
-:::info
-# Complete Example Compose File
+## Complete Example Compose File
 
 ```yaml
 services:
@@ -152,13 +153,13 @@ services:
 
 ---
 
-# Environment Variable Matrix
+## Environment Variable Matrix
 
 | Variable           | GCP Vertex AI |
 |--------------------|---------------|
 | `GCP_PROJECT_ID`    | Required      |
 | `REGION`            | Required      |
-| `MODEL`             | Vertex model or Docker model name, for example `publishers/meta/models/llama-3.3-70b-instruct-maas` or `ai/llama3.3` |
+| `MODEL`             | Vertex model ID or Docker model name, for example `publishers/meta/models/llama-3.3-70b-instruct-maas` or `ai/llama3.3` |
 
 ---
 
@@ -167,5 +168,3 @@ 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
-:::
-