import { completion } from '@rocketnew/llm-sdk';

const response = await completion({
  model: 'gpt-5.4',
  messages: [{ role: 'user', content: 'Hello!' }]
});

const stream = await completion({
  model: 'gpt-5.4',
  messages: [{ role: 'user', content: 'Count to 5' }],
  stream: true
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content || '');
}

const response = await completion({
  model: 'gpt-5.4',
  messages: [{ role: 'user', content: 'What is the weather in Paris?' }],
  tools: [{
    type: 'function',
    function: {
      name: 'get_weather',
      description: 'Get weather for a location',
      parameters: {
        type: 'object',
        properties: {
          location: { type: 'string' }
        },
        required: ['location']
      }
    }
  }]
});

const response = await completion({
  model: 'gpt-5.4',
  messages: [{ role: 'user', content: 'List 3 colors in JSON' }],
  response_format: { type: 'json_object' }
});

const response = await completion({
  model: 'gpt-5.4',
  messages: [{
    role: 'user',
    content: [
      { type: 'text', text: 'Describe this image' },
      { type: 'image_url', image_url: 'https://example.com/image.jpg' }
    ]
  }]
});

import { imageGeneration } from '@rocketnew/llm-sdk';

const response = await imageGeneration({
  model: 'gpt-image-1.5',
  prompt: 'A photorealistic sunset over a mountain lake',
  size: '1024x1024',
  quality: 'high',
  n: 1
});

const b64 = response.data[0].b64_json;

import { imageEdit } from '@rocketnew/llm-sdk';
import * as fs from 'fs';

const response = await imageEdit({
  model: 'gpt-image-1.5',
  image: fs.readFileSync('source.png'),
  prompt: 'Add a rainbow over the mountains',
  size: '1024x1024'
});

import { speech } from '@rocketnew/llm-sdk';

const audio = await speech({
  model: 'gpt-4o-mini-tts',
  input: 'Hello from RocketLLM.',
  voice: 'alloy',
  response_format: 'mp3'
});

// Write the audio bytes to disk
await audio.writeToFile('hello.mp3');

import { transcription } from '@rocketnew/llm-sdk';
import * as fs from 'fs';

const response = await transcription({
  model: 'gpt-4o-transcribe',
  file: fs.readFileSync('audio.mp3')
});

console.log(response.text);

const response = await completion({
  model: 'claude-sonnet-4-6',
  messages: [{ role: 'user', content: 'Hello!' }]
});

const stream = await completion({
  model: 'claude-sonnet-4-6',
  messages: [{ role: 'user', content: 'Write a poem' }],
  stream: true
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content || '');
}

const response = await completion({
  model: 'claude-sonnet-4-6',
  messages: [{ role: 'user', content: 'Calculate 25 * 4' }],
  tools: [{
    type: 'function',
    function: {
      name: 'calculate',
      parameters: {
        type: 'object',
        properties: {
          operation: { type: 'string' },
          a: { type: 'number' },
          b: { type: 'number' }
        }
      }
    }
  }]
});

const response = await completion({
  model: 'claude-sonnet-4-6',
  messages: [{ role: 'user', content: 'Solve this complex problem...' }],
  thinking: { type: 'enabled', budget_tokens: 2000 }
});

console.log(response.choices[0].message.thinking_blocks);

const response = await completion({
  model: 'claude-sonnet-4-6',
  messages: [{ role: 'user', content: 'Explain quantum computing' }],
  reasoning_effort: 'high'
});

const response = await completion({
  model: 'claude-sonnet-4-6',
  messages: [{ role: 'user', content: 'What are the latest AI news?' }],
  web_search_options: {
    search_context_size: 'medium'
  }
});

const response = await completion({
  model: 'claude-sonnet-4-6',
  messages: [{ role: 'user', content: 'List 3 colors' }],
  response_format: { type: 'json_object' }
});

const response = await completion({
  model: 'gemini/gemini-2.5-flash',
  messages: [{ role: 'user', content: 'Hello!' }]
});

const stream = await completion({
  model: 'gemini/gemini-2.5-flash',
  messages: [{ role: 'user', content: 'Explain AI' }],
  stream: true
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content || '');
}

const response = await completion({
  model: 'gemini/gemini-2.5-flash',
  messages: [{ role: 'user', content: 'Get weather for Tokyo' }],
  tools: [{
    type: 'function',
    function: {
      name: 'get_weather',
      parameters: {
        type: 'object',
        properties: {
          location: { type: 'string' }
        }
      }
    }
  }]
});

const response = await completion({
  model: 'gemini/gemini-2.5-flash',
  messages: [{
    role: 'user',
    content: [
      { type: 'text', text: 'What is in this image?' },
      { type: 'image_url', image_url: 'https://example.com/photo.jpg' }
    ]
  }]
});

const response = await completion({
  model: 'gemini/gemini-2.5-flash',
  messages: [{ role: 'user', content: 'List 3 countries' }],
  response_format: { type: 'json_object' }
});

import { imageGeneration } from '@rocketnew/llm-sdk';

const response = await imageGeneration({
  model: 'gemini/gemini-2.5-flash-image',
  prompt: 'A peaceful zen garden with cherry blossoms',
  size: '1024x1024',
  n: 1
});

import { imageEdit } from '@rocketnew/llm-sdk';
import * as fs from 'fs';

const response = await imageEdit({
  model: 'gemini/gemini-2.5-flash-image',
  image: fs.readFileSync('source.png'),
  prompt: 'Add falling cherry blossom petals',
  size: '1024x1024'
});

import { speech } from '@rocketnew/llm-sdk';

const audio = await speech({
  model: 'gemini/gemini-2.5-flash-preview-tts',
  input: 'Hello from Gemini.',
  voice: 'Kore'
});

// Gemini returns audio/wav
await audio.writeToFile('hello.wav');

const response = await completion({
  model: 'perplexity/sonar-pro',
  messages: [{ role: 'user', content: 'What is TypeScript?' }]
});

const stream = await completion({
  model: 'perplexity/sonar-pro',
  messages: [{ role: 'user', content: 'Explain React' }],
  stream: true
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content || '');
}

const response = await completion({
  model: 'perplexity/sonar-pro',
  messages: [{ role: 'user', content: 'Latest AI developments?' }]
});

// Response content includes inline citations
console.log(response.choices[0].message.content);