Merge branch 'main' of https://github.com/vercel/ai

content/docs/05-ai-sdk-ui/01-overview.mdx

@@ -5,9 +5,9 @@ description: An overview of Vercel AI SDK UI.
 
 # Vercel AI SDK UI
 
-Vercel AI SDK UI is designed to help you build interactive chat, completion, and assistant applications with ease. It is framework-agnostic toolkit, streamlining the integration of advanced AI functionalities into your applications.
+Vercel AI SDK UI is designed to help you build interactive chat, completion, and assistant applications with ease. It is a framework-agnostic toolkit, streamlining the integration of advanced AI functionalities into your applications.
 
-Vercel AI SDK UI provides robust abstractions that simplify the complex tasks of managing chat streams and UI updates on the frontend, enabling you to develop dynamic AI-driven interfaces more efficiently. With three main hooks — **`useChat`**, **`useCompletion`**, and **`useAssistant`** — you can incorporate real-time chat capabilities, text completions, and interactive assistant features into your app.
+Vercel AI SDK UI provides robust abstractions that simplify the complex tasks of managing chat streams and UI updates on the frontend, enabling you to develop dynamic AI-driven interfaces more efficiently. With four main hooks — **`useChat`**, **`useCompletion`**, **`useObject`**, and **`useAssistant`** — you can incorporate real-time chat capabilities, text completions, streamed JSON, and interactive assistant features into your app.
 
 - **[`useChat`](/docs/reference/ai-sdk-ui/use-chat)** offers real-time streaming of chat messages, abstracting state management for inputs, messages, loading, and errors, allowing for seamless integration into any UI design.
 - **[`useCompletion`](/docs/reference/ai-sdk-ui/use-completion)** enables you to handle text completions in your applications, managing chat input state and automatically updating the UI as new completions are streamed from your AI provider.

content/examples/01-next-app/01-basics/02-streaming-text-generation.mdx

@@ -19,7 +19,7 @@ Text generation can sometimes take a long time to complete, especially when you'
 
 ## Client
 
-Let's create a simple React component that will call the `getAnswer` function when a button is clicked. The `getAnswer` function will call the `streamText` function, which will then generate text based on the input prompt. To consume the stream of text in the client, we will use the `readStreamableValue` function from the `ai/rsc` module.
+Let's create a simple React component that will call the `generate` function when a button is clicked. The `generate` function will call the `streamText` function, which will then generate text based on the input prompt. To consume the stream of text in the client, we will use the `readStreamableValue` function from the `ai/rsc` module.
 
 ```tsx filename="app/page.tsx"
 'use client';
@@ -57,7 +57,7 @@ export default function Home() {
 
 ## Server
 
-On the server side, we need to implement the `getAnswer` function, which will call the `streamText` function. The `streamText` function will generate text based on the input prompt. In order to stream the text generation to the client, we will use `createStreambleValue` that can wrap any changable value and stream it to the client.
+On the server side, we need to implement the `generate` function, which will call the `streamText` function. The `streamText` function will generate text based on the input prompt. In order to stream the text generation to the client, we will use `createStreambleValue` that can wrap any changable value and stream it to the client.
 
 Using DevTools, we can see the text generation being streamed to the client in real-time.
 
@@ -69,8 +69,6 @@ import { openai } from '@ai-sdk/openai';
 import { createStreamableValue } from 'ai/rsc';
 
 export async function generate(input: string) {
-  'use server';
-
   const stream = createStreamableValue('');
 
   (async () => {