AI Inference

import { Tenzro } from 'tenzro';

const tenzro = new Tenzro();

// Text generation
const response = await tenzro.ai.generate({
  model: 'gemini-2.5-flash',
  prompt: 'Explain quantum computing in simple terms',
  maxTokens: 500,
});

console.log(response.text);

const response = await tenzro.ai.chat({
  model: 'gemini-3-pro-preview',
  messages: [
    { role: 'system', content: 'You are a helpful coding assistant.' },
    { role: 'user', content: 'Write a TypeScript function to validate email addresses' },
  ],
  temperature: 0.7,
  maxTokens: 1000,
});

console.log(response.message.content);

const stream = await tenzro.ai.stream({
  model: 'gemini-2.5-flash',
  messages: [
    { role: 'user', content: 'Write a short story about AI' },
  ],
});

for await (const chunk of stream) {
  process.stdout.write(chunk.text);
}

// Get final response with usage stats
const finalResponse = await stream.finalResponse();
console.log('Tokens used:', finalResponse.usage);

const response = await tenzro.ai.chat({
  model: 'gemini-3-pro-preview',
  messages: [
    { role: 'user', content: 'What is the weather in San Francisco?' },
  ],
  tools: [
    {
      name: 'get_weather',
      description: 'Get current weather for a location',
      parameters: {
        type: 'object',
        properties: {
          location: { type: 'string', description: 'City name' },
          unit: { type: 'string', enum: ['celsius', 'fahrenheit'] },
        },
        required: ['location'],
      },
    },
  ],
  toolChoice: 'auto',
});

if (response.toolCalls) {
  for (const call of response.toolCalls) {
    console.log('Function:', call.name);
    console.log('Arguments:', call.arguments);
  }
}

// Single embedding
const embedding = await tenzro.ai.embed({
  model: 'text-embedding-3-small',
  input: 'Machine learning is fascinating',
});

console.log(embedding.vector); // [0.123, -0.456, ...]
console.log(embedding.dimensions); // 1536

// Batch embeddings
const embeddings = await tenzro.ai.embedBatch({
  model: 'text-embedding-3-small',
  inputs: [
    'First document',
    'Second document',
    'Third document',
  ],
});

console.log(embeddings.vectors.length); // 3

// Analyze an image
const response = await tenzro.ai.chat({
  model: 'gemini-3-pro-preview',
  messages: [
    {
      role: 'user',
      content: [
        { type: 'text', text: 'What do you see in this image?' },
        { type: 'image_url', image_url: { url: 'https://example.com/image.jpg' } },
      ],
    },
  ],
});

// Or with base64
const response = await tenzro.ai.chat({
  model: 'gpt-5',
  messages: [
    {
      role: 'user',
      content: [
        { type: 'text', text: 'Describe this diagram' },
        { type: 'image_url', image_url: { url: `data:image/png;base64,${base64Image}` } },
      ],
    },
  ],
});

import { TenzroError, RateLimitError, ModelError } from 'tenzro';

try {
  const response = await tenzro.ai.generate({
    model: 'gemini-2.5-flash',
    prompt: 'Hello',
  });
} catch (error) {
  if (error instanceof RateLimitError) {
    console.log('Rate limited, retry after:', error.retryAfter);
  } else if (error instanceof ModelError) {
    console.log('Model error:', error.message);
  } else {
    throw error;
  }
}