Gemini Generate Content

import { GoogleGenAI } from '@google/genai';

const ai = new GoogleGenAI({
  apiKey: process.env.GEMINI_API_KEY,
  httpOptions: {
    baseUrl: 'https://www.llmadaptive.uk/api/v1beta'
  }
});

const response = await ai.models.generateContent({
  model: 'gemini-2.5-pro',
  contents: [
    {
      role: 'user',
      parts: [
        { text: 'Explain quantum computing in simple terms' }
      ]
    }
  ],
  config: {
    temperature: 0.7,
    maxOutputTokens: 1024
  }
});

console.log(response.candidates[0].content.parts[0].text);
console.log('Provider:', response.provider);
console.log('Tokens used:', response.usageMetadata.totalTokenCount);

const response = await ai.models.generateContent({
  model: 'gemini-2.5-pro',
  contents: [
    {
      role: 'user',
      parts: [{ text: 'What is the capital of France?' }]
    },
    {
      role: 'model',
      parts: [{ text: 'The capital of France is Paris.' }]
    },
    {
      role: 'user',
      parts: [{ text: 'What is its population?' }]
    }
  ]
});

const response = await ai.models.generateContent({
  model: 'gemini-2.5-pro',
  contents: [
    {
      role: 'user',
      parts: [{ text: 'Write a sorting algorithm in Python' }]
    }
  ],
  config: {
    temperature: 0.3,
    maxOutputTokens: 2048
  },
  // Adaptive-specific features
  semantic_cache: {
    enabled: true,
    similarity_threshold: 0.95
  },
  fallback: {
    enabled: true,
    max_retries: 3
  },
  model_router: {
    cost_optimization: true,
    fallback_models: ['claude-sonnet-4-20250514', 'gpt-4o']
  }
});

console.log('Cache tier:', response.usageMetadata.cache_tier);
console.log('Provider:', response.provider);