LLM completions

JSONLines

LLM completions can be streamed using JSON Lines format, which is a convenient way to send a stream of JSON objects over HTTP. On the back-end streaming is implemented with Generator functions, which allows to yield data as it becomes available. Read more about streaming with Vovk.ts.

src/modules/llm/LlmController.ts


import { type VovkRequest, post, prefix, operation, HttpException, HttpStatus } from 'vovk';
import OpenAI from 'openai';
 
@prefix('openai')
export default class OpenAiController {
  @operation({
    summary: 'Create a chat completion',
    description: 'Create a chat completion using OpenAI and yield the response',
  })
  @post('chat')
  static async *createChatCompletion(
    req: VovkRequest<{ messages: OpenAI.Chat.Completions.ChatCompletionMessageParam[] }>
  ) {
    const { messages } = await req.json();
    const openai = new OpenAI();
 
    yield* await openai.chat.completions.create({
      messages,
      model: 'gpt-5-nano',
      stream: true,
    });
  }
}


// ...
using completion = await OpenAiRPC.createChatCompletion({
  body: { messages: [...messages, userMessage] },
});
// ...

See example for more details.

Vercel AI SDK

The AI SDK is the TypeScript toolkit designed to help developers build AI-powered applications with React, Next.js, Vue, Svelte, Node.js, and more.

Read more about the Vercel AI SDK .

Vovk.ts supports every built-in feature of Next.js and, as a result, it can be used with the Vercel AI SDK returning Response object from toDataStreamResponse function with no additional changes.

src/modules/ai-sdk/AiSdkController.ts


import { post, prefix, operation, type VovkRequest } from 'vovk';
import { streamText, convertToModelMessages, type UIMessage } from 'ai';
import { openai } from '@ai-sdk/openai';
 
@prefix('ai-sdk')
export default class AiSdkController {
  @operation({
    summary: 'Vercel AI SDK',
    description:
      'Uses [@ai-sdk/openai](https://www.npmjs.com/package/@ai-sdk/openai) and ai packages to chat with an AI model',
  })
  @post('chat')
  static async chat(req: VovkRequest<{ messages: UIMessage[] }>) {
    const { messages } = await req.json();
    const LIMIT = 5;
 
    if (messages.filter(({ role }) => role === 'user').length > LIMIT) {
      throw new HttpException(HttpStatus.BAD_REQUEST, `You can only send ${LIMIT} messages at a time`);
    }
 
    return streamText({
      model: openai('gpt-5-nano'),
      system: 'You are a helpful assistant.',
      messages: convertToModelMessages(messages),
    }).toUIMessageStreamResponse();
  }
}

On the client-side, you can @ai-sdk/react package to interact with the endpoint and build a chat interface.


'use client';
import { useChat } from '@ai-sdk/react';
import { DefaultChatTransport } from 'ai';
import { useState } from 'react';
 
export default function Page() {
  const [input, setInput] = useState('');
 
  const { messages, sendMessage, error, status } = useChat({
    transport: new DefaultChatTransport({
      api: '/api/ai-sdk/chat',
    }),
  });
 
  const handleSubmit = (e: React.FormEvent) => {
    e.preventDefault();
    if (input.trim()) {
      sendMessage({ text: input });
      setInput('');
    }
  };
 
  return (
    <form onSubmit={handleSubmit}>
      {messages.map((message) => (
        <div key={message.id}>
          {message.role === 'assistant' ? '🤖' : '👤'}{' '}
          {message.parts.map((part, partIndex) => (
            <span key={partIndex}>{part.type === 'text' ? part.text : ''}</span>
          ))}
        </div>
      ))}
      {error && <div>❌ {error.message}</div>}
      <div className="input-group">
        <input type="text" placeholder="Send a message..." value={input} onChange={(e) => setInput(e.target.value)} />
        <button>Send</button>
      </div>
    </form>
  );
}