Example code snippets

fern/assistants/personalization.mdx

@@ -105,7 +105,7 @@ This approach is ideal for use cases like customer support, account management,
             name: "Dynamic Customer Support Assistant",
             model: {
               provider: "openai",
-              model: "gpt-4",
+              model: "gpt-4o",
               messages: [{
                 role: "system",
                 content: `You are helping ${customer.name}, a ${customer.tier} member since ${customer.createdAt}.`

fern/customization/custom-llm/tool-calling-integration.mdx

@@ -241,7 +241,7 @@ Once your endpoints are set up, try testing them with these cURL commands.
 curl -X POST https://custom-llm-url/chat/completions \
   -H "Content-Type: application/json" \
   -d '{
-        "model": "gpt-3.5-turbo",
+        "model": "gpt-4o",
         "messages": [
           {"role": "user", "content": "I need a payment link."}
         ],
@@ -268,7 +268,7 @@ Streaming chunks eventually include the result (e.g., a payment link) returned b
 curl -X POST https://custom-llm-url/chat/completions \
   -H "Content-Type: application/json" \
   -d '{
-        "model": "gpt-3.5-turbo",
+        "model": "gpt-4o",
         "messages": [
           {"role": "user", "content": "Please transfer my call."}
         ],
@@ -423,4 +423,4 @@ By building each layer step by step and testing with cURL, you can fine-tune you
 For your convenience, you can find the complete source code for this Custom LLM integration here:
 
 **[Custom LLM with Vapi Integration – Complete Code](https://codesandbox.io/p/devbox/gfwztp)**
-```
+```

fern/customization/custom-transcriber.mdx

@@ -63,8 +63,26 @@ You'll learn how to:
     mkdir vapi-custom-transcriber
     cd vapi-custom-transcriber
     npm init -y
+    ```
+
+    <CodeBlocks>
+    ```bash title="npm"
     npm install ws express dotenv @deepgram/sdk
     ```
+
+    ```bash title="yarn"
+    yarn add ws express dotenv @deepgram/sdk
+    ```
+
+    ```bash title="pnpm"
+    pnpm add ws express dotenv @deepgram/sdk
+    ```
+
+    ```bash title="bun"
+    bun add ws express dotenv @deepgram/sdk
+    ```
+    </CodeBlocks>
+
     Create a `.env` file with the following content:
     ```env
     DEEPGRAM_API_KEY=your_deepgram_api_key

fern/customization/jwt-authentication.mdx

@@ -31,7 +31,7 @@ For example, it can be used to restrict which API endpoints the token can access
 
 <Note>
   As of writing, the only publicly scoped API endpoint is
-  https://api.vapi.ai//call/web, which is used for Web Call creation. All other
+  https://api.vapi.ai/call/web, which is used for Web Call creation. All other
   endpoints are privately scoped.
 </Note>
 
@@ -136,18 +136,7 @@ If you set the scope to `public`, you can use it to make authenticated API reque
 ```
 import Vapi from '@vapi-ai/web';
 
-const vapi = new Vapi({
-  token: 'your-jwt-token',
-});
+const vapi = new Vapi('your-jwt-token');
 
 vapi.start('your-assistant-id');
 ```
-
-## Notes
-
-- With the generated token, you can authenticate API requests to any endpoint requiring authentication. The token will be valid for the duration specified in the options (1 hour in this case).
-- If you don't specify `token` in the JWT payload, the token will be public.
-
-## Conclusion
-
-This documentation covered the basics of generating a JWT token and demonstrated how to use the token to make authenticated API requests. Ensure that your environment variables (e.g., `ORG_ID` and `PRIVATE_KEY`) are correctly set up before running the code.

fern/docs.yml

@@ -746,15 +746,13 @@ redirects:
   - source: "/multilingual/introduction"
     destination: "/multilingual"
   - source: "/outbound_sales"
-    destination: "/examples/outbound-sales"
+    destination: "/examples/lead-qualification"
   - source: "/technical_support"
     destination: "/examples/inbound-support"
   - source: "/pizza_website"
     destination: "/examples/inbound-support"
   - source: "/examples/pizza-website"
     destination: "/examples/inbound-support"
-  - source: "/outbound_call_python"
-    destination: "/examples/outbound-call-python"
   - source: "/voice_widget"
     destination: "/examples/voice-widget"
   - source: "/clients"