API Integration Quick Start

# Replace YOUR_API_KEY with your actual key
curl -X POST https://api.yelinai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "gpt-4o-mini",
    "messages": [
      {"role": "system", "content": "You are a helpful AI assistant"},
      {"role": "user", "content": "What can you help me with?"}
    ]
  }'

# API Base URL
base_url: https://api.yelinai.com/v1

# API Key (starts with sk-)
api_key: sk-xxxxxxxxxxxxxx

# Model Name (plug and play)
model: gpt-4.1-mini  # or claude-sonnet-4-20250514, etc.

# Install: pip install openai
from openai import OpenAI

# Initialize client
client = OpenAI(
    api_key="Your API Key",  # Get from LaoZhang API
    base_url="https://api.yelinai.com/v1"  # Integration URL
)

# Examples of calling different models
def test_models():
    models = [
        "gpt-4.1-mini",  # Best value
        "claude-sonnet-4-20250514",  # Best for coding
        "deepseek-v3"  # Best Chinese model
    ]

    for model in models:
        try:
            response = client.chat.completions.create(
                model=model,
                messages=[
                    {"role": "system", "content": "You are a helpful AI assistant"},
                    {"role": "user", "content": "Introduce yourself in one sentence"}
                ],
                temperature=0.7,
                max_tokens=100
            )
            print(f"{model}: {response.choices[0].message.content}")
            print(f"Tokens used: {response.usage.total_tokens}\n")
        except Exception as e:
            print(f"{model} call failed: {e}\n")

if __name__ == "__main__":
    test_models()

import os
client = OpenAI(
    api_key=os.getenv("LAOZHANG_API_KEY"),
    base_url="https://api.yelinai.com/v1"
)

// Install: npm install openai
import OpenAI from 'openai';

// Initialize client
const client = new OpenAI({
  apiKey: process.env.LAOZHANG_API_KEY || 'Your API Key',
  baseURL: 'https://api.yelinai.com/v1'
});

// Streaming output example (real-time response)
async function streamChat() {
  const stream = await client.chat.completions.create({
    model: 'gpt-4.1-mini',
    messages: [
      { role: 'system', content: 'You are a helpful AI assistant' },
      { role: 'user', content: 'Please write a simple React component' }
    ],
    stream: true,  // Enable streaming
    temperature: 0.7
  });

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

// Concurrent calls to multiple models
async function compareModels(prompt) {
  const models = ['gpt-4.1', 'claude-sonnet-4-20250514', 'gemini-2.5-pro'];

  const promises = models.map(model =>
    client.chat.completions.create({
      model,
      messages: [{ role: 'user', content: prompt }],
      max_tokens: 100
    })
  );

  const results = await Promise.all(promises);
  results.forEach((result, index) => {
    console.log(`\n\`$\{models[index]}:\n\`$\{result.choices[0].message.content}`);
  });
}

// Run examples
streamChat().catch(console.error);

# Basic call
curl -X POST https://api.yelinai.com/v1/chat/completions -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"model": "gpt-4.1-mini", "messages": [{"role": "user", "content": "Hello"}]}'

# Streaming output (SSE)
curl -X POST https://api.yelinai.com/v1/chat/completions -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"model": "gpt-4.1", "messages": [{"role": "user", "content": "Write a Python quicksort"}], "stream": true}'

# Image generation
curl -X POST https://api.yelinai.com/v1/images/generations -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"model": "gpt-4o-image", "prompt": "A cute kitten", "n": 1, "size": "1024x1024"}'