@helicone/async

@helicone/async

A Node.js wrapper for logging LLM traces directly to Helicone, bypassing the proxy, with OpenLLMetry. This package enables you to monitor and analyze your OpenAI API usage without requiring a proxy server.

Features

• Direct logging to Helicone without proxy
• OpenLLMetry support for standardized LLM telemetry
• Custom property tracking
• Environment variable configuration
• TypeScript support

Installation

Stable Version

npm install @helicone/async

Quick Start

• Create a Helicone account and get your API key from helicone.ai/developer

• Set up your environment variables:

export HELICONE_API_KEY=<your API key>
export OPENAI_API_KEY=<your OpenAI API key>

• Basic usage:

const { HeliconeAsyncOpenAI } = require("helicone");

const openai = new HeliconeAsyncOpenAI({
  apiKey: process.env.OPENAI_API_KEY,
  heliconeMeta: {
    apiKey: process.env.HELICONE_API_KEY,
  },
});

const chatCompletion = await openai.chat.completion.create({
  model: "gpt-3.5-turbo",
  messages: [{ role: "user", content: "Hello world" }],
});

console.log(chatCompletion.data.choices[0].message);

Configuration Options

HeliconeMeta Options

The heliconeMeta object supports several configuration options:

interface HeliconeMeta {
  apiKey?: string; // Your Helicone API key
  custom_properties?: Record<string, any>; // Custom properties to track
  cache?: boolean; // Enable/disable caching
  retry?: boolean; // Enable/disable retries
  user_id?: string; // Track requests by user
}

Example with Custom Properties

const openai = new HeliconeAsyncOpenAI({
  apiKey: process.env.OPENAI_API_KEY,
  heliconeMeta: {
    apiKey: process.env.HELICONE_API_KEY,
    custom_properties: {
      project: "my-project",
      environment: "production",
    },
    user_id: "user-123",
  },
});

Advanced Usage

With Async/Await

async function generateResponse() {
  try {
    const response = await openai.chat.completion.create({
      model: "gpt-4",
      messages: [
        { role: "system", content: "You are a helpful assistant." },
        { role: "user", content: "What is the capital of France?" },
      ],
      max_tokens: 150,
    });

    return response.data.choices[0].message;
  } catch (error) {
    console.error("Error:", error);
  }
}

Error Handling

try {
  const completion = await openai.chat.completion.create({
    model: "gpt-3.5-turbo",
    messages: [{ role: "user", content: "Hello" }],
  });
} catch (error) {
  if (error.response) {
    console.error(error.response.status);
    console.error(error.response.data);
  } else {
    console.error(error.message);
  }
}

Best Practices

• Always store API keys in environment variables
• Implement proper error handling
• Use custom properties to track important metadata
• Set appropriate timeout values for your use case
• Consider implementing retry logic for production environments

Contributing

We welcome contributions! Please see our contributing guidelines for details.

License

Apache-2.0

Support

• Documentation: https://docs.helicone.ai
• Issues: GitHub Issues
• Discord: Join our community