Gemini (Google)

import { GoogleGenerativeAI } from '@google/generative-ai';

const genAI = new GoogleGenerativeAI(process.env.GOOGLE_API_KEY);

async function chat(prompt) {
  const model = genAI.getGenerativeModel({ model: 'gemini-1.5-pro' });
  const result = await model.generateContent(prompt);
  const usage = result.response.usageMetadata;

  // Send to Fenra
  await fetch('https://ingest.fenra.io/usage/transactions', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'X-Api-Key': process.env.FENRA_API_KEY
    },
    body: JSON.stringify({
      provider: 'gemini',
      model: 'gemini-1.5-pro',
      usage: [{
        type: 'tokens',
        metrics: {
          input_tokens: usage.promptTokenCount,
          output_tokens: usage.candidatesTokenCount,
          total_tokens: usage.totalTokenCount
        }
      }],
      context: {
        billable_customer_id: process.env.BILLABLE_CUSTOMER_ID
      }
    })
  });

  return result.response;
}

import google.generativeai as genai
import requests
import os

genai.configure(api_key=os.getenv('GOOGLE_API_KEY'))

def chat(prompt):
    model = genai.GenerativeModel('gemini-1.5-pro')
    response = model.generate_content(prompt)
    usage = response.usage_metadata

    # Send to Fenra
    requests.post(
        'https://ingest.fenra.io/usage/transactions',
        headers={
            'Content-Type': 'application/json',
            'X-Api-Key': os.getenv('FENRA_API_KEY')
        },
        json={
            'provider': 'gemini',
            'model': 'gemini-1.5-pro',
            'usage': [{
                'type': 'tokens',
                'metrics': {
                    'input_tokens': usage.prompt_token_count,
                    'output_tokens': usage.candidates_token_count,
                    'total_tokens': usage.total_token_count
                }
            }],
            'context': {
                'billable_customer_id': os.getenv('BILLABLE_CUSTOMER_ID')
            }
        }
    )

    return response