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feat (ai/core): add toDataStream to streamText result (#2938)
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‎.changeset/late-monkeys-beam.md‎

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---
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'ai': patch
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---
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feat (ai/core): add toDataStream to streamText result

‎content/docs/07-reference/ai-sdk-core/02-stream-text.mdx‎

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],
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},
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{
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name: 'toDataStreamResponse',
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name: 'toDataStream',
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type: '(options?: ToDataStreamOptions) => Response',
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description: 'Converts the result to a data stream.',
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properties: [
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{
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type: 'ToDataStreamOptions',
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parameters: [
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{
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name: 'data',
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type: 'StreamData',
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optional: true,
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description: 'The stream data object.',
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},
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{
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name: 'getErrorMessage',
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type: '(error: unknown) => string',
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description:
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'A function to get the error message from the error object. By default, all errors are masked as "" for safety reasons.',
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optional: true,
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},
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],
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},
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],
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},
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{
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name: 'toDataStreamResponse',
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type: '(options?: ToDataStreamResponseOptions) => Response',
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description:
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'Converts the result to a streamed response object with a stream data part stream. It can be used with the `useChat` and `useCompletion` hooks.',
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properties: [
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{
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type: 'ToDataStreamOptions',
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type: 'ToDataStreamResponseOptions',
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parameters: [
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{
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name: 'init',

‎content/examples/15-api-servers/10-node-js-http-server.mdx‎

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@@ -5,7 +5,7 @@ description: Example of using Vercel AI SDK in a Node.js HTTP server.
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# Node.js HTTP Server
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You can use the Vercel AI SDK in a Node.js HTTP server to generate and stream text and objects to the client.
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You can use the Vercel AI SDK in a Node.js HTTP server to generate text and stream it to the client.
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## Examples
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set in the `OPENAI_API_KEY` environment variable.
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</Note>
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### Basic
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### Data Stream
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You can use the `pipeDataStreamToResponse` method to pipe the stream data to the server response.
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```ts file='index.ts'
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import { openai } from '@ai-sdk/openai';
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import { StreamData, streamText } from 'ai';
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import { streamText } from 'ai';
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import { createServer } from 'http';
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createServer(async (req, res) => {
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}).listen(8080);
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```
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### With Stream Data
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### Data Stream With Stream Data
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`pipeDataStreamToResponse` can be used with `StreamData` to send additional data to the client.
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```ts file='index.ts' highlight="6-7,12-15,18"
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import { openai } from '@ai-sdk/openai'
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import { StreamData, streamText } from 'ai'
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import { createServer } from 'http'
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createServer(async (req, res) => {
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const data = new StreamData()
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data.append('initialized call')
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const result = await streamText({
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model: openai('gpt-4o'),
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prompt: 'Invent a new holiday and describe its traditions.'
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onFinish() {
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data.append('call completed')
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data.close()
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}
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})
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result.pipeDataStreamToResponse(res, { data })
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}).listen(8080)
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```
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### Text Stream
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You can send a text stream to the client using `pipeTextStreamToResponse`.
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```ts file='index.ts'
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import { openai } from '@ai-sdk/openai';
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import { StreamData, streamText } from 'ai';
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import { streamText } from 'ai';
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import { createServer } from 'http';
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createServer(async (req, res) => {
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const data = new StreamData();
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data.append('initialized call');
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const result = await streamText({
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model: openai('gpt-4o'),
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prompt: 'Invent a new holiday and describe its traditions.',
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onFinish() {
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data.append('call completed');
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data.close();
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},
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});
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result.pipeDataStreamToResponse(res, { data });
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result.pipeTextStreamToResponse(res);
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}).listen(8080);
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```

‎content/examples/15-api-servers/20-hono.mdx‎

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---
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title: Hono
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description: Example of using Vercel AI SDK in a Hono server.
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---
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# Hono
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You can use the Vercel AI SDK in a Hono server to generate and stream text and objects to the client.
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## Examples
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The examples start a simple HTTP server that listens on port 8080. You can e.g. test it using `curl`:
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```bash
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curl -X POST http://localhost:8080
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```
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<Note>
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The examples use the OpenAI `gpt-4o` model. Ensure that the OpenAI API key is
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set in the `OPENAI_API_KEY` environment variable.
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</Note>
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### Data Stream
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You can use the `toDataStream` method to get a data stream from the result and then pipe it to the response.
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```ts file='index.ts'
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import { openai } from '@ai-sdk/openai';
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import { serve } from '@hono/node-server';
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import { streamText } from 'ai';
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import { Hono } from 'hono';
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import { stream } from 'hono/streaming';
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const app = new Hono();
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app.post('/', async c =>
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stream(c, async stream => {
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const result = await streamText({
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model: openai('gpt-4o'),
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prompt: 'Invent a new holiday and describe its traditions.',
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});
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// Mark the response as a v1 data stream:
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c.header('X-Vercel-AI-Data-Stream', 'v1');
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c.header('Content-Type', 'text/plain; charset=utf-8');
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await stream.pipe(result.toDataStream());
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}),
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);
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serve({ fetch: app.fetch, port: 8080 });
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```
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### Data Stream With Stream Data
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`toDataStream` can be used with `StreamData` to send additional data to the client.
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```ts file='index.ts' highlight="11-13,18-21,28"
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import { openai } from '@ai-sdk/openai';
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import { serve } from '@hono/node-server';
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import { StreamData, streamText } from 'ai';
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import { Hono } from 'hono';
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import { stream } from 'hono/streaming';
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const app = new Hono();
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app.post('/', async c =>
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stream(c, async stream => {
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// use stream data (optional):
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const data = new StreamData();
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data.append('initialized call');
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const result = await streamText({
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model: openai('gpt-4o'),
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prompt: 'Invent a new holiday and describe its traditions.',
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onFinish() {
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data.append('call completed');
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data.close();
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},
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});
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// Mark the response as a v1 data stream:
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c.header('X-Vercel-AI-Data-Stream', 'v1');
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c.header('Content-Type', 'text/plain; charset=utf-8');
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await stream.pipe(result.toDataStream({ data }));
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}),
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);
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serve({ fetch: app.fetch, port: 8080 });
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```
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### Text Stream
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You can use the `toTextStream` method to get a text stream from the result and then pipe it to the response.
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```ts file='index.ts'
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import { openai } from '@ai-sdk/openai';
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import { serve } from '@hono/node-server';
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import { streamText } from 'ai';
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import { Hono } from 'hono';
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import { stream } from 'hono/streaming';
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const app = new Hono();
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app.post('/', async c =>
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stream(c, async stream => {
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const result = await streamText({
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model: openai('gpt-4o'),
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prompt: 'Invent a new holiday and describe its traditions.',
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});
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c.header('Content-Type', 'text/plain; charset=utf-8');
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await stream.pipe(result.toTextStream());
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}),
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);
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serve({ fetch: app.fetch, port: 8080 });
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```

‎content/examples/15-api-servers/index.mdx‎

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description: 'Stream text to a Node.js HTTP server.',
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href: '/examples/api-servers/node-js-http-server',
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},
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{
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title: 'Hono',
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description: 'Stream text to a Hono server.',
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href: '/examples/api-servers/hono',
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},
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]}
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/>

‎examples/hono/.env.example‎

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ANTHROPIC_API_KEY=""
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OPENAI_API_KEY=""
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MISTRAL_API_KEY=""
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GOOGLE_GENERATIVE_AI_API_KEY=""
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FIREWORKS_API_KEY=""
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GROQ_API_KEY=""
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PERPLEXITY_API_KEY=""

‎examples/hono/README.md‎

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# Hono + Vercel AI SDK Example
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## Usage
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1. Create .env file with the following content (and more settings, depending on the providers you want to use):
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```sh
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OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
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```
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2. Run the following commands from the root directory of the AI SDK repo:
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```sh
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pnpm install
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pnpm build
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```
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3. Run the following command:
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```sh
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pnpm tsx src/server.ts
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```
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4. Test the endpoint with Curl:
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```sh
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curl -X POST http://localhost:8080
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```

‎examples/hono/package.json‎

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{
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"name": "ai-sdk-hono-example",
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"version": "0.0.0",
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"private": true,
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"dependencies": {
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"@ai-sdk/openai": "latest",
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"@hono/node-server": "1.12.2",
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"ai": "latest",
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"dotenv": "16.4.5",
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"hono": "4.5.11"
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},
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"scripts": {
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"type-check": "tsc --noEmit"
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},
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"devDependencies": {
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"@types/node": "20.11.20",
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"tsx": "4.7.1",
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"typescript": "5.5.4"
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}
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}

‎examples/hono/src/server.ts‎

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import { openai } from '@ai-sdk/openai';
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import { serve } from '@hono/node-server';
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import { StreamData, streamText } from 'ai';
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import 'dotenv/config';
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import { Hono } from 'hono';
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import { stream } from 'hono/streaming';
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const app = new Hono();
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app.post('/', async c =>
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stream(c, async stream => {
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// use stream data (optional):
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const data = new StreamData();
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data.append('initialized call');
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const result = await streamText({
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model: openai('gpt-4o'),
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prompt: 'Invent a new holiday and describe its traditions.',
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onFinish() {
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data.append('call completed');
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data.close();
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},
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});
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// Mark the response as a v1 data stream:
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c.header('X-Vercel-AI-Data-Stream', 'v1');
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c.header('Content-Type', 'text/plain; charset=utf-8');
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await stream.pipe(result.toDataStream({ data }));
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}),
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);
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serve({ fetch: app.fetch, port: 8080 });

‎examples/hono/tsconfig.json‎

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{
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"compilerOptions": {
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"strict": true,
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"declaration": true,
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"sourceMap": true,
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"target": "es2022",
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"lib": ["es2022", "dom"],
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"module": "esnext",
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"types": ["node"],
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"esModuleInterop": true,
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"allowSyntheticDefaultImports": true,
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"moduleResolution": "node",
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"rootDir": "./src",
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"outDir": "./build",
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"skipLibCheck": true
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},
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"include": ["src/**/*.ts"]
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}

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