import { OpenAI } from 'openai';

const openai = new OpenAI({
  apiKey: 'your-unreal-api-key',
  baseURL: OPENAI_BASE_URL,
});

async function streamCompletion() {
  const stream = await openai.chat.completions.create({
    model: 'your-preferred-model',
    messages: [
      { role: 'system', content: 'You are a helpful assistant.' },
      { role: 'user', content: 'Write a short story about AI.' }
    ],
    stream: true,
  });

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

streamCompletion();

async function functionCalling() {
  const completion = await openai.chat.completions.create({
    model: 'your-preferred-model',
    messages: [
      { role: 'system', content: 'You are a helpful assistant.' },
      { role: 'user', content: 'What\'s the weather like in San Francisco?' }
    ],
    functions: [
      {
        name: 'get_weather',
        description: 'Get the current weather in a given location',
        parameters: {
          type: 'object',
          properties: {
            location: {
              type: 'string',
              description: 'The city and state, e.g. San Francisco, CA',
            },
            unit: {
              type: 'string',
              enum: ['celsius', 'fahrenheit'],
              description: 'The unit of temperature to use',
            },
          },
          required: ['location'],
        },
      },
    ],
    function_call: 'auto',
  });

  const message = completion.choices[0].message;

  if (message.function_call) {
    const functionName = message.function_call.name;
    const functionArgs = JSON.parse(message.function_call.arguments);
    
    console.log(`Function called: ${functionName}`);
    console.log(`Arguments: ${JSON.stringify(functionArgs, null, 2)}`);
    
    // Here you would implement the actual function call
    // For example:
    // if (functionName === 'get_weather') {
    //   const weather = await getWeather(functionArgs.location, functionArgs.unit);
    //   return weather;
    // }
  }
}

async function batchRequests() {
  const prompts = [
    'Explain quantum computing in simple terms',
    'Write a haiku about programming',
    'List 3 tips for productivity'
  ];
  
  const requests = prompts.map(prompt => 
    openai.chat.completions.create({
      model: 'your-preferred-model',
      messages: [
        { role: 'system', content: 'You are a helpful assistant.' },
        { role: 'user', content: prompt }
      ],
    })
  );
  
  const responses = await Promise.all(requests);
  
  responses.forEach((response, index) => {
    console.log(`Response for prompt "${prompts[index]}":`);
    console.log(response.choices[0].message.content);
    console.log('---');
  });
}

import { OpenAI } from 'openai';
import { setTimeout } from 'timers/promises';

class RateLimitedOpenAI {
  private openai: OpenAI;
  private maxRetries: number;
  private retryDelay: number;

  constructor(apiKey: string, baseURL: string, maxRetries = 3, retryDelay = 1000) {
    this.openai = new OpenAI({
      apiKey,
      baseURL,
    });
    this.maxRetries = maxRetries;
    this.retryDelay = retryDelay;
  }

  async createChatCompletion(params: any) {
    let retries = 0;
    
    while (true) {
      try {
        return await this.openai.chat.completions.create(params);
      } catch (error: any) {
        if (error.status === 429 && retries < this.maxRetries) {
          // Rate limited, wait and retry
          retries++;
          console.log(`Rate limited. Retrying in ${this.retryDelay}ms (${retries}/${this.maxRetries})`);
          await setTimeout(this.retryDelay);
          // Exponential backoff
          this.retryDelay *= 2;
        } else {
          throw error;
        }
      }
    }
  }
}

// Usage
const client = new RateLimitedOpenAI('your-api-key', OPENAI_URL);
const response = await client.createChatCompletion({
  model: 'your-preferred-model',
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'Hello!' }
  ],
});

function selectModel(task: 'general' | 'code' | 'creative' | 'analysis') {
  switch (task) {
    case 'general':
      return 'general-purpose-model';
    case 'code':
      return 'code-specialized-model';
    case 'creative':
      return 'creative-writing-model';
    case 'analysis':
      return 'data-analysis-model';
    default:
      return 'default-model';
  }
}

async function smartCompletion(prompt: string, task: 'general' | 'code' | 'creative' | 'analysis') {
  const model = selectModel(task);
  
  const completion = await openai.chat.completions.create({
    model,
    messages: [
      { role: 'system', content: 'You are a helpful assistant.' },
      { role: 'user', content: prompt }
    ],
  });
  
  return completion.choices[0].message.content;
}