// OLLM API Example
async function callOLLM(prompt: string) {
  const response = await fetch('https://api.ollm.com/v1/chat/completions', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': 'Bearer your-api-key'
    },
    body: JSON.stringify({
      model: 'near/GLM-4.6',
      messages: [
        {
          role: 'user',
          content: prompt
        }
      ]
    })
  });

  const data = await response.json();
  return data.choices[0].message.content;
}

// Usage
callOLLM('Why is the sky blue?')
  .then(response => console.log(response))
  .catch(error => console.error('Error:', error));

# OLLM API Example
from openai import OpenAI

client = OpenAI(
    base_url="https://api.ollm.com/v1",
    api_key="your-api-key"
)

response = client.chat.completions.create(
    model="near/GLM-4.6",
    messages=[{"role": "user", "content": "Why is the sky blue?"}]
)

print(response.choices[0].message.content)

// OLLM API Example (Rust)
use reqwest::blocking::Client;
use serde_json::json;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let res: serde_json::Value = Client::new()
        .post("https://api.ollm.com/v1/chat/completions")
        .bearer_auth("your-api-key")
        .json(&json!({
            "model": "near/GLM-4.6",
            "messages": [{
                "role": "user",
                "content": "Why is the sky blue?"
            }]
        }))
        .send()?
        .json()?;

    println!("{}", res["choices"][0]["message"]["content"]);
    Ok(())
}