Tenzro Cortex

// Generate text with Gemini 2.5 Pro
const response = await fetch('https://api.tenzro.com/cortex/generate', {
  method: 'POST',
  headers: {
    'X-API-Key': 'sk_your_key_here',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    prompt: "Write a Python function to calculate fibonacci numbers",
    model: "gemini-2.5-pro",
    max_tokens: 2048,
    temperature: 0.7,
    thinking_budget: 1000,
    show_thinking: true,
    system_prompt: "You are a helpful coding assistant",
    stream: true // Enable streaming
  })
});

// Handle streaming response
const reader = response.body.getReader();
while (true) {
  const { done, value } = await reader.read();
  if (done) break;
  
  const chunk = new TextDecoder().decode(value);
  const lines = chunk.split('\n').filter(line => line.trim());
  
  for (const line of lines) {
    if (line.startsWith('data: ')) {
      const data = JSON.parse(line.slice(6));
      if (data.content) {
        console.log(data.content); // Stream content to UI
      }
    }
  }
}

// Analyze an image with Gemini 2.5 Pro
const formData = new FormData();
formData.append('image', imageFile);
formData.append('prompt', 'Describe this image in detail and identify all objects');
formData.append('model', 'gemini-2.5-pro');
formData.append('max_tokens', '1000');

const response = await fetch('https://api.tenzro.com/cortex/analyze/image', {
  method: 'POST',
  headers: {
    'X-API-Key': 'sk_your_key_here'
  },
  body: formData
});

const result = await response.json();
console.log(result.description);

// Document OCR with Gemini 2.0 Flash
const ocrResponse = await fetch('https://api.tenzro.com/cortex/analyze/document', {
  method: 'POST',
  headers: {
    'X-API-Key': 'sk_your_key_here',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    image_url: "https://example.com/document.pdf",
    model: "gemini-2.0-flash",
    extract_text: true,
    extract_tables: true
  })
});

const ocrData = await ocrResponse.json();
console.log(ocrData.extracted_text);

// Initialize a live session with Gemini 2.0 Flash
const session = await fetch('https://api.tenzro.com/cortex/live/session', {
  method: 'POST',
  headers: {
    'X-API-Key': 'sk_your_key_here',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    model: "gemini-2.0-flash",
    response_modalities: ["TEXT", "AUDIO"],
    voice: "alloy",
    instructions: "You are a helpful AI assistant."
  })
});

const sessionData = await session.json();

// Connect to WebSocket
const ws = new WebSocket(
  `wss://api.tenzro.com/cortex/live/ws/${sessionData.id}?api_key=sk_your_key_here`
);

ws.onopen = () => {
  console.log('Connected to live session');
  
  // Send a message
  ws.send(JSON.stringify({
    type: 'conversation.item.create',
    item: {
      type: 'message',
      role: 'user',
      content: [{ type: 'input_text', text: 'Hello, how can you help me?' }]
    }
  }));
  
  // Generate response
  ws.send(JSON.stringify({ type: 'response.create' }));
};

ws.onmessage = (event) => {
  const message = JSON.parse(event.data);
  
  switch (message.type) {
    case 'response.text.delta':
      // Handle streaming text
      console.log('Text chunk:', message.delta);
      break;
    case 'response.audio.delta':
      // Handle streaming audio
      playAudioChunk(message.delta);
      break;
    case 'response.done':
      console.log('Response complete');
      break;
  }
};