JavaScript

import { TinfoilAI } from 'tinfoil';

const client = new TinfoilAI({
  apiKey: process.env.TINFOIL_API_KEY
});

// Example: Multi-step agentic workflow
const response = await client.chat.completions.create({
  model: 'kimi-k2-thinking',
  messages: [
    {
      role: 'user',
      content: 'Write a TypeScript function to validate email addresses using regex, then test it with several examples and debug any issues.'
    }
  ]
});

console.log(response.choices[0]?.message?.content);

import { TinfoilAI } from 'tinfoil';

const client = new TinfoilAI({
  apiKey: process.env.TINFOIL_API_KEY
});

// Example: Complex reasoning task
const response = await client.chat.completions.create({
  model: 'deepseek-r1-0528',
  messages: [
    {
      role: 'user',
      content: 'Solve this step by step: If a train travels 120 miles in 2 hours, and then increases its speed by 25% for the next 3 hours, how far does it travel in total?'
    }
  ]
});

console.log(response.choices[0]?.message?.content);

import { TinfoilAI } from 'tinfoil';
import fs from 'fs';

const client = new TinfoilAI({
  apiKey: process.env.TINFOIL_API_KEY
});

// Example: Image analysis
const imageBuffer = fs.readFileSync('image.jpg');
const base64Image = imageBuffer.toString('base64');

const response = await client.chat.completions.create({
  model: 'qwen3-vl-30b',
  messages: [
    {
      role: 'user',
      content: [
        { type: 'text', text: "What's in this image?" },
        {
          type: 'image_url',
          image_url: {
            url: `data:image/jpeg;base64,${base64Image}`
          }
        }
      ] as any
    }
  ]
});

console.log(response.choices[0]?.message?.content);

import { TinfoilAI } from 'tinfoil';

const client = new TinfoilAI({
  apiKey: process.env.TINFOIL_API_KEY
});

// Example: Conversational AI
const response = await client.chat.completions.create({
  model: 'llama3-3-70b',
  messages: [
    {
      role: 'user',
      content: 'What are the key differences between renewable and non-renewable energy sources?'
    }
  ]
});

console.log(response.choices[0]?.message?.content);

import { TinfoilAI } from 'tinfoil';

const client = new TinfoilAI({
  apiKey: process.env.TINFOIL_API_KEY
});

// Example: Advanced reasoning with configurable effort levels
const response = await client.chat.completions.create({
  model: 'gpt-oss-120b',
  messages: [
    {
      role: 'user',
      content: 'Analyze the trade-offs between different database architectures for a high-traffic e-commerce platform.'
    }
  ]
});

console.log(response.choices[0]?.message?.content);

import { TinfoilAI } from 'tinfoil';

const client = new TinfoilAI({
  apiKey: process.env.TINFOIL_API_KEY
});

// Example: Native multimodal agentic coding
const response = await client.chat.completions.create({
  model: 'kimi-k2-5',
  messages: [
    {
      role: 'user',
      content: 'Review this codebase and suggest architectural improvements for better modularity and testability.'
    }
  ]
});

console.log(response.choices[0]?.message?.content);

import fs from "fs";
import { TinfoilAI } from 'tinfoil';

const client = new TinfoilAI({
  apiKey: process.env.TINFOIL_API_KEY
});

// Example: Audio transcription
const audioFile = fs.createReadStream("meeting_recording.mp3");
const transcription = await client.audio.transcriptions.create({
  model: "whisper-large-v3-turbo",
  file: audioFile,
  language: "en", // Optional: specify language for better accuracy
  prompt: "This is a business meeting discussing quarterly results" // Optional: provide context
});

console.log("Transcription:", transcription.text);

import { TinfoilAI } from 'tinfoil';

const client = new TinfoilAI({
  apiKey: process.env.TINFOIL_API_KEY
});

// Example: Generate embeddings for similarity search
const documents = [
  "Artificial intelligence is transforming modern technology.",
  "Machine learning enables computers to learn from data.",
  "The weather today is sunny and warm.",
  "Deep learning uses neural networks with multiple layers."
];

// Generate embeddings for all documents
const embeddings: number[][] = [];
for (const doc of documents) {
  const response = await client.embeddings.create({
    model: "nomic-embed-text",
    input: doc,
    encoding_format: "float"
  });
  embeddings.push(response.data[0].embedding);
}

// Calculate similarity between first two documents
const dotProduct = (a: number[], b: number[]) => 
  a.reduce((sum, val, i) => sum + val * b[i], 0);
const magnitude = (vec: number[]) => 
  Math.sqrt(vec.reduce((sum, val) => sum + val * val, 0));

const similarity = dotProduct(embeddings[0], embeddings[1]) / 
  (magnitude(embeddings[0]) * magnitude(embeddings[1]));

console.log(`Similarity between first two AI-related documents: ${similarity.toFixed(3)}`);
console.log(`Embedding dimension: ${embeddings[0].length}`);