Selecting and Switching AI Models

// GPT-4
const gptResponse = await openai.createCompletion({
  model: "gpt-4",
  prompt: "Write a function to sort an array."
});

// Claude 3
const claudeResponse = await anthropic.messages.create({
  model: "claude-3-opus",
  max_tokens: 1024,
  system: "You are a helpful assistant.",
  messages: [{ role: "user", content: "Write a function to sort an array." }]
});

// Gemini Pro
const geminiResponse = await googleAI.generateContent({
  model: "gemini-pro",
  contents: [{ role: "user", parts: [{ text: "Write a function to sort an array." }] }]
});

const aiClient = new AIAssistant({ model: 'gpt-4-turbo' });

// Perform initial code review
const review = await aiClient.reviewCode(code);

// Switch to a more powerful model for complex refactoring
aiClient.setModel('claude-3-opus');
const refactor = await aiClient.refactorCode(code, review);

// Use gemini for quick syntax checks
aiClient.setModel('gemini-pro');
const syntaxCheck = await aiClient.checkSyntax(code);

{
  "modelRouting": {
    "strategy": "context-aware",
    "rules": [
      {
        "condition": "codeGeneration",
        "model": "gpt-4-turbo"
      },
      {
        "condition": "documentation",
        "model": "claude-3-opus"
      }
    ]
  }
}

const costEffectiveModel = (task) => {
  if (task.complexity < 5) return "GPT-3.5 Turbo";
  if (task.complexity >= 5 && task.complexity < 8) return "Gemini Pro";
  return "GPT-4 Turbo";
};

const qualityModel = (task) => {
  if (task.complexity >= 8) return "GPT-4 Turbo";
  if (task.complexity >= 5) return "Claude 3 Opus";
  return "GPT-3.5 Turbo";
};

{
  "ai": {
    "primaryModel": "gpt-4-turbo",
    "fallbackModel": "gpt-3.5-turbo",
    "retryAttempts": 3
  }
}

ollama run llama3
> What is the capital of France?
> The capital of France is Paris.

const modelA = new AIModel("gpt-4-turbo");
const modelB = new AIModel("claude-3-opus");

const input = "Explain quantum computing in simple terms";

const outputA = modelA.generate(input);
const outputB = modelB.generate(input);

console.log("Model A output:", outputA);
console.log("Model B output:", outputB);

const response = await fetch("https://api.openai.com/v1/chat/completions", {
  method: "POST",
  headers: {
    "Authorization": "Bearer sk-...",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "gpt-4-turbo-2024-04-01",
    messages: [{ role: "user", content: "What is AI?" }]
  })
});

import openai
from datasets import load_dataset

# Load benchmark dataset
dataset = load_dataset("mmlu", "computer_science")

# Evaluate model performance
response = openai.ChatCompletion.create(
  model="gpt-4-turbo",
  messages=[{"role": "user", "content": "What is the capital of France?"}],
  max_tokens=10
)

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