import { createAgent, Provider, ModelName } from 'llm-abstraction';

// Create an OpenAI agent
const openaiAgent = createAgent({
  name: "MyAssistant",
  basePrompt: "You are a helpful, concise assistant.",
  model: `${Provider.OpenAI}/${ModelName.GPT4oMini}`,
  apiKey: process.env.OPENAI_API_KEY as string
});

// Create a Google Gemini agent
const geminiAgent = createAgent({
  name: "MyGeminiAssistant",
  basePrompt: "You are a helpful, concise assistant.",
  model: `${Provider.Google}/${ModelName.Gemini2Flash}`,
  apiKey: process.env.GOOGLE_API_KEY as string
});

// Send a prompt to your agent
const response = await openaiAgent.prompt([
  { role: 'user', content: 'What is the capital of France?' }
]);

console.log(response.text);  // Paris
console.log(`Tokens used: ${response.usage.totalTokens}`);

// Define a type for structured output
interface CountryInfo {
  name: string;
  capital: string;
  population: number;
  continent: string;
  languages: string[];
}

// Create a sample object to define the structure
const sampleCountry: CountryInfo = {
  name: "",
  capital: "",
  population: 0,
  continent: "",
  languages: [""]
};

// Get structured output from your agent
const structuredResponse = await openaiAgent.prompt<CountryInfo>(
  [{ role: 'user', content: 'Provide information about France.' }],
  { sampleObj: sampleCountry }
);

// Access typed data
console.log(`Country: ${structuredResponse.data.name}`);
console.log(`Capital: ${structuredResponse.data.capital}`);
console.log(`Population: ${structuredResponse.data.population.toLocaleString()}`);
console.log(`Languages: ${structuredResponse.data.languages.join(', ')}`);

// Create a batch of prompts (OpenAI only)
const batchId = await openaiAgent.createBatch([
  [{ role: 'user', content: 'What is the capital of France?' }],
  [{ role: 'user', content: 'What is the capital of Japan?' }],
  [{ role: 'user', content: 'What is the capital of Brazil?' }]
]);

console.log(`Created batch ${batchId}`);

// Check batch status
let status = await openaiAgent.checkBatch(batchId);
console.log(`Batch status: ${status.status}`);

// Poll until batch is complete
while (status.status !== 'completed') {
  await new Promise(resolve => setTimeout(resolve, 5000)); // Wait 5 seconds
  status = await openaiAgent.checkBatch(batchId);
  console.log(`Batch status: ${status.status}`);
}

// Retrieve batch results
const results = await openaiAgent.retrieveBatch(batchId);
results.forEach((result, i) => {
  console.log(`Result ${i+1}: ${result.text}`);
});

// Cancel a batch if needed
await openaiAgent.cancelBatch(batchId);

import { LLMRequestOptions } from 'llm-abstraction';

// Configure request parameters
const options: LLMRequestOptions = {
  temperature: 0.3,           // Lower temperature for more deterministic outputs
  maxTokens: 1000,            // Limit response length
  topP: 0.9,                  // Nucleus sampling parameter
  frequencyPenalty: 0.5,      // Reduce repetition
  presencePenalty: 0.5,       // Encourage topic diversity
  
  // Provider-specific parameters
  providerParams: {
    seed: 12345              // For reproducible results (if supported)
  }
};

const response = await myAgent.prompt(messages, options);

import { BatchRequestOptions } from 'llm-abstraction';

// Configure batch options
const batchOptions: BatchRequestOptions = {
  temperature: 0.5,
  maxTokens: 500,
  batchName: "Capitals Batch",     // Custom name for the batch
  priority: "high",                // Priority level
  timeoutSeconds: 3600,            // 1 hour timeout
  failureThreshold: 10             // Allow up to 10% failures
};

const batchId = await openaiAgent.createBatch(prompts, batchOptions);