Node.js

Node.js SDK

Node.js SDK for Tinfoil’s secure AI inference API
GitHub: tinfoil-node

Installation

npm install tinfoil

Migration from OpenAI

Migrating from OpenAI to Tinfoil is straightforward. The client is designed to be compatible with the OpenAI Node.js client:

// Before (OpenAI)
- import OpenAI from 'openai';
- 
- const client = new OpenAI({
-   apiKey: process.env.OPENAI_API_KEY,
- });

// After (Tinfoil)
+ import { TinfoilAI } from 'tinfoil';
+ 
+ const client = new TinfoilAI({
+   apiKey: process.env.TINFOIL_API_KEY
+ });

All method signatures remain the same since TinfoilAI extends the standard OpenAI client with built-in security features.

Running the Chat Example

To run the streaming chat example:

Clone the repository
Install dependencies:

npm install

Create a .env file with your configuration:

TINFOIL_API_KEY=your-api-key

Run the example:

cd examples/chat
npx ts-node main.ts

The example demonstrates streaming chat completions with the Tinfoil API wrapper.

Usage

// 1. Create a client
const client = new TinfoilAI({
  apiKey: process.env.TINFOIL_API_KEY,
});

// 2. Use client as you would OpenAI client 
// see https://github.com/openai/openai-node for API documentation

Model Examples

Below are specific examples for each supported model. Click on any model to see its configuration and usage example.

Chat Models

DeepSeek R1 - deepseek-r1-0528

import { TinfoilAI } from 'tinfoil';

// Configure client for DeepSeek R1
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: 'system',
      content: 'You are an expert at solving complex mathematical problems step by step.'
    },
    {
      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?'
    }
  ],
  temperature: 0.1
});

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

Mistral Small 3.1 24B - mistral-small-3-1-24b

import { TinfoilAI } from 'tinfoil';

// Configure client for Mistral Small 3.1 24B
const client = new TinfoilAI({
  apiKey: process.env.TINFOIL_API_KEY
});

// Example: Multilingual conversation
const response = await client.chat.completions.create({
  model: 'mistral-small-3-1-24b',
  messages: [
    {
      role: 'user',
      content: 'Explain the concept of machine learning in both English and French.'
    }
  ],
  temperature: 0.7
});

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

Llama 3.3 70B - llama3-3-70b

import { TinfoilAI } from 'tinfoil';

// Configure client for Llama 3.3 70B
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: 'system',
      content: 'You are a helpful assistant that provides detailed explanations.'
    },
    {
      role: 'user',
      content: 'What are the key differences between renewable and non-renewable energy sources?'
    }
  ],
  temperature: 0.8
});

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

Qwen 2.5 72B - qwen2-5-72b

import { TinfoilAI } from 'tinfoil';

// Configure client for Qwen 2.5 72B
const client = new TinfoilAI({
  apiKey: process.env.TINFOIL_API_KEY
});

// Example: Code generation and analysis
const response = await client.chat.completions.create({
  model: 'qwen2-5-72b',
  messages: [
    {
      role: 'user',
      content: 'Write a TypeScript function to calculate the Fibonacci sequence up to n terms, then explain how it works.'
    }
  ],
  temperature: 0.3
});

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

Audio Models

Whisper Large V3 Turbo - whisper-large-v3-turbo

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

// Configure client for Whisper Large V3 Turbo
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);

Kokoro - kokoro

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

// Configure client for Kokoro TTS
const client = new TinfoilAI({
  apiKey: process.env.TINFOIL_API_KEY
});

const textToSpeak = "Welcome to Tinfoil's secure AI platform. Your data remains private and protected.";

// Example: Text-to-speech with different voices
// Single voice
const response1 = await client.audio.speech.create({
  model: "kokoro",
  voice: "af_sky",
  input: textToSpeak,
  response_format: "mp3"
});

const buffer1 = Buffer.from(await response1.arrayBuffer());
await fs.promises.writeFile("speech_single.mp3", buffer1);

// Combined voices for richer sound
const response2 = await client.audio.speech.create({
  model: "kokoro",
  voice: "af_sky+af_bella",
  input: textToSpeak,
  response_format: "mp3"
});

const buffer2 = Buffer.from(await response2.arrayBuffer());
await fs.promises.writeFile("speech_combined.mp3", buffer2);

console.log("Speech files generated successfully!");

Embedding Models

Nomic Embed Text - nomic-embed-text

import { TinfoilAI } from 'tinfoil';

// Configure client for Nomic Embed Text
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,
  });
  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}`);

API Documentation

This library is a drop-in replacement for the official OpenAI Node.js client that can be used with Tinfoil. All methods and types are identical. See the OpenAI client for complete API usage and documentation.

Guides

Tool Calling

Learn how to use function calling capabilities with AI models.

Structured Outputs

Use JSON schema validation for reliable data extraction.

Image Processing

Process images with multimodal AI models.

Document Processing

Upload and process documents securely.

Getting Started

Models

SDKs

Guides

Enclave Verification

Tutorials

Resources

Node.js SDK

Installation

Migration from OpenAI

Running the Chat Example

Usage

Model Examples

Chat Models

Audio Models

Embedding Models

API Documentation

Guides

Tool Calling

Structured Outputs

Image Processing

Document Processing

Getting Started

Models

SDKs

Guides

Enclave Verification

Tutorials

Resources

Node.js SDK

​Installation

​Migration from OpenAI

​Running the Chat Example

​Usage

​Model Examples

​Chat Models

​Audio Models

​Embedding Models

​API Documentation

​Guides

Tool Calling

Structured Outputs

Image Processing

Document Processing

Installation

Migration from OpenAI

Running the Chat Example

Usage

Model Examples

Chat Models

Audio Models

Embedding Models

API Documentation

Guides