[docs/dart] Fix rendering of various lists

src/content/docs/docs/deployment/any-platform.mdx

@@ -463,80 +463,80 @@ You can deploy Genkit flows as web services using any platform that can host a D
 
 1.  Create a directory for the Genkit sample project:
 
-```bash
-mkdir -p ~/tmp/genkit-any-project
-cd ~/tmp/genkit-any-project
-```
+    ```bash
+    mkdir -p ~/tmp/genkit-any-project
+    cd ~/tmp/genkit-any-project
+    ```
 
 2.  Initialize a Dart project:
 
-```bash
-dart create -t console-simple .
-```
+    ```bash
+    dart create -t console-simple .
+    ```
 
 3.  Add Genkit dependencies:
 
-```bash
-dart pub add genkit genkit_shelf shelf shelf_router genkit_google_genai
-```
+    ```bash
+    dart pub add genkit genkit_shelf shelf shelf_router genkit_google_genai
+    ```
 
 4.  Create a sample app using Genkit and Shelf:
 
-```dart title="bin/server.dart"
-import 'dart:io';
-import 'package:genkit/genkit.dart';
-import 'package:genkit_shelf/genkit_shelf.dart';
-import 'package:genkit_google_genai/genkit_google_genai.dart';
-import 'package:shelf/shelf.dart';
-import 'package:shelf/shelf_io.dart' as io;
-import 'package:shelf_router/shelf_router.dart';
-import 'package:schemantic/schemantic.dart';
-
-void main() async {
-  final ai = Genkit(
-    plugins: [googleAI(apiKey: Platform.environment['GOOGLE_API_KEY'])],
-    model: googleAI.gemini('gemini-2.5-flash'),
-  );
-
-  final flow = ai.defineFlow(
-    name: 'flow',
-    fn: (String input, _) async => 'Processed $input',
-    inputSchema: .string(),
-    outputSchema: .string(),
-  );
-
-  final router = Router();
-  // Mount the flow handler
-  router.post('/flow', shelfHandler(flow));
-
-  // Create a handler pipeline (e.g., adding logging)
-  final handler = const Pipeline()
-      .addMiddleware(logRequests())
-      .addHandler(router.call);
-
-  // Start the server
-  final port = int.parse(Platform.environment['PORT'] ?? '8080');
-  final server = await io.serve(handler, InternetAddress.anyIPv4, port);
-  print('Server running on port ${server.port}');
-}
-```
+    ```dart title="bin/server.dart"
+    import 'dart:io';
+    import 'package:genkit/genkit.dart';
+    import 'package:genkit_shelf/genkit_shelf.dart';
+    import 'package:genkit_google_genai/genkit_google_genai.dart';
+    import 'package:shelf/shelf.dart';
+    import 'package:shelf/shelf_io.dart' as io;
+    import 'package:shelf_router/shelf_router.dart';
+    import 'package:schemantic/schemantic.dart';
+
+    void main() async {
+      final ai = Genkit(
+        plugins: [googleAI(apiKey: Platform.environment['GOOGLE_API_KEY'])],
+        model: googleAI.gemini('gemini-2.5-flash'),
+      );
+
+      final flow = ai.defineFlow(
+        name: 'flow',
+        fn: (String input, _) async => 'Processed $input',
+        inputSchema: .string(),
+        outputSchema: .string(),
+      );
+
+      final router = Router();
+      // Mount the flow handler
+      router.post('/flow', shelfHandler(flow));
+
+      // Create a handler pipeline (e.g., adding logging)
+      final handler = const Pipeline()
+          .addMiddleware(logRequests())
+          .addHandler(router.call);
+
+      // Start the server
+      final port = int.parse(Platform.environment['PORT'] ?? '8080');
+      final server = await io.serve(handler, InternetAddress.anyIPv4, port);
+      print('Server running on port ${server.port}');
+    }
+    ```
 
 5.  **Compile for Deployment**:
 
     Dart applications can be compiled into self-contained executables (AOT compilation), which makes them easy to deploy without needing the full Dart SDK on the target server.
 
-```bash
-dart compile exe bin/server.dart -o server
-```
+    ```bash
+    dart compile exe bin/server.dart -o server
+    ```
 
     The resulting `server` file is a standalone executable (on the same architecture).
 
 6.  **Deploy**:
 
-        Upload the `server` executable to your hosting provider and configure it to run. Ensure you set the necessary environment variables:
+    Upload the `server` executable to your hosting provider and configure it to run. Ensure you set the necessary environment variables:
 
-        - `PORT`: The port your server should listen on (defaults to 8080 in the code above).
-        - `GOOGLE_API_KEY`: Your Google GenAI API key.
+    - `PORT`: The port your server should listen on (defaults to 8080 in the code above).
+    - `GOOGLE_API_KEY`: Your Google GenAI API key.
 
 </Lang>
 

src/content/docs/docs/deployment/cloud-run.mdx

@@ -452,9 +452,9 @@ You can easy deploy your Genkit Dart flows to Cloud Run as a containerized servi
 2.  Link the project to a billing account.
 3.  Configure the Google Cloud CLI:
 
-```bash
-gcloud init
-```
+    ```bash
+    gcloud init
+    ```
 
 ## 2. Prepare your Dart project
 
@@ -531,21 +531,21 @@ Most flows require API keys (like `GOOGLE_API_KEY` for Gemini). You should use [
 
 1.  Create a secret for your API key:
 
-```bash
-gcloud secrets create google-api-key --data-file=-
-# (Press Enter, paste your API key, then press Ctrl+D)
-```
+    ```bash
+    gcloud secrets create google-api-key --data-file=-
+    # (Press Enter, paste your API key, then press Ctrl+D)
+    ```
 
 2.  Deploy the service, referencing the secret:
 
-```bash
-gcloud run deploy genkit-server \
-  --source . \
-  --port 3400 \
-  --allow-unauthenticated \
-  --region us-central1 \
-  --set-secrets GOOGLE_API_KEY=google-api-key:latest
-```
+    ```bash
+    gcloud run deploy genkit-server \
+      --source . \
+      --port 3400 \
+      --allow-unauthenticated \
+      --region us-central1 \
+      --set-secrets GOOGLE_API_KEY=google-api-key:latest
+    ```
 
     _Note: Replace `3400` with your default port if different, but Cloud Run defaults to passing 8080 as `PORT` env var, which your code should respect._
 

src/content/docs/docs/frameworks/shelf.mdx

@@ -199,18 +199,18 @@ Since a Genkit Shelf application is just a standard Dart HTTP server, you can de
 
 1.  **Create a Dockerfile**:
 
-```dockerfile
-FROM dart:stable AS build
-WORKDIR /app
-COPY pubspec.* ./
-RUN dart pub get
-COPY . .
-RUN dart compile exe bin/server.dart -o bin/server
-
-FROM scratch
-COPY --from=build /runtime/ /
-COPY --from=build /app/bin/server /app/bin/
-CMD ["/app/bin/server"]
-```
+    ```dockerfile
+    FROM dart:stable AS build
+    WORKDIR /app
+    COPY pubspec.* ./
+    RUN dart pub get
+    COPY . .
+    RUN dart compile exe bin/server.dart -o bin/server
+
+    FROM scratch
+    COPY --from=build /runtime/ /
+    COPY --from=build /app/bin/server /app/bin/
+    CMD ["/app/bin/server"]
+    ```
 
 2.  **Build and Deploy** (e.g., using Cloud Build and Cloud Run).

src/content/docs/docs/integrations/google-genai.mdx

@@ -1112,19 +1112,19 @@ void main() {
 
 Requires a Gemini API Key, which you can get from [Google AI Studio](https://aistudio.google.com/apikey).
 
-1. **Environment variables**: Set `GEMINI_API_KEY`
-2. **Plugin configuration**: Pass `apiKey` when initializing the plugin (shown above)
-3. **Per-request**: Override the API key for specific requests in the config:
-
-```dart
-final response = await ai.generate(
-  model: googleAI.gemini('gemini-2.5-flash'),
-  prompt: 'Your prompt here',
-  config: GeminiOptions(
-    apiKey: 'different-api-key', // Use a different API key for this request
-  ),
-);
-```
+1.  **Environment variables**: Set `GEMINI_API_KEY`
+2.  **Plugin configuration**: Pass `apiKey` when initializing the plugin (shown above)
+3.  **Per-request**: Override the API key for specific requests in the config:
+
+    ```dart
+    final response = await ai.generate(
+      model: googleAI.gemini('gemini-2.5-flash'),
+      prompt: 'Your prompt here',
+      config: GeminiOptions(
+        apiKey: 'different-api-key', // Use a different API key for this request
+      ),
+    );
+    ```
 
 ## Language Models
 

src/content/docs/docs/integrations/vertex-ai.mdx

@@ -42,33 +42,33 @@ The plugin requires you to specify your Google Cloud project ID, the [region](ht
 
   You can also pass this value directly:
 
-```dart
-vertexAI(projectId: 'my-project-id')
-```
+  ```dart
+  vertexAI(projectId: 'my-project-id')
+  ```
 
 - By default, `vertexAI` gets the Vertex AI API location from the `GCLOUD_LOCATION` environment variable.
 
   You can also pass this value directly:
 
-```dart
-vertexAI(location: 'us-central1')
-```
+  ```dart
+  vertexAI(location: 'us-central1')
+  ```
 
 - To provide API credentials, you need to set up Google Cloud Application Default Credentials.
 
-  1. To specify your credentials:
+  1.  To specify your credentials:
 
-     - If you're running your flow from a Google Cloud environment (Cloud Functions, Cloud Run, and so on), this is set automatically.
+      - If you're running your flow from a Google Cloud environment (Cloud Functions, Cloud Run, and so on), this is set automatically.
 
-     - On your local dev environment, do this by running:
+      - On your local dev environment, do this by running:
 
-```shell
-gcloud auth application-default login
-```
+        ```shell
+        gcloud auth application-default login
+        ```
 
-     - For other environments, see the [Application Default Credentials](https://cloud.google.com/docs/authentication/provide-credentials-adc) docs.
+      - For other environments, see the [Application Default Credentials](https://cloud.google.com/docs/authentication/provide-credentials-adc) docs.
 
-2. In addition, make sure the account is granted the Vertex AI User IAM role (`roles/aiplatform.user`). See the Vertex AI [access control](https://cloud.google.com/vertex-ai/generative-ai/docs/access-control) docs.
+  2.  In addition, make sure the account is granted the Vertex AI User IAM role (`roles/aiplatform.user`). See the Vertex AI [access control](https://cloud.google.com/vertex-ai/generative-ai/docs/access-control) docs.
 
 ## Usage
 

src/content/docs/docs/local-observability.mdx

@@ -22,14 +22,14 @@ Logs are automatically correlated with the current trace context when using `Zon
 
 To view traces locally:
 
-1. Start the Genkit Developer UI:
+1.  Start the Genkit Developer UI:
 
-```bash
-genkit start -- dart run
-```
+    ```bash
+    genkit start -- dart run
+    ```
 
-2. Run your flow or action.
-3. Open the Developer UI (typically at `http://localhost:4000`) to view traces and metrics.
+2.  Run your flow or action.
+3.  Open the Developer UI (typically at `http://localhost:4000`) to view traces and metrics.
 
 </Lang>