How to Install and Use Helicone for Your AI Projects 🚀

Saturday, Dec 21, 2024 | 6 minute read

GitHub Trend

🌟 Revolutionize Your AI Development! 🚀 This open-source platform simplifies integration, monitoring, and debugging for large language models. With real-time analytics, flexible deployment, and community support, it empowers developers to excel in their AI projects. 💡✨

In today’s fast-paced technological landscape, Artificial Intelligence and Large Language Models (LLM) have become central to developers’ focus. As the range of application scenarios continues to expand, developers urgently need an efficient platform to help them manage and optimize these innovative tools. 🤔

Helicone has emerged as an open-source platform designed to be the AI assistant for developers. It not only integrates various features to assist users in seamlessly implementing, monitoring, and fine-tuning LLMs but also serves as a powerful tool that enables developers to tackle technical challenges with ease. 🎉

1. What is Helicone? The AI Assistant for Open Source Developers 🤖

Helicone is an exciting all-in-one open-source LLM developer platform aimed at providing comprehensive support to help developers optimize and manage their LLM applications. Specifically designed for LLM applications, Helicone integrates features like integration, observation, analysis, and tuning, empowering developers to effectively address daily challenges. Its goal is to significantly enhance developers’ ability to monitor and debug upcoming LLM applications, fully unleashing the potential of AI applications! ✨

2. The Unique Appeal of Helicone: Key Features at a Glance 🌟

Helicone boasts numerous impressive features. Let’s explore these unique and practical functionalities!

• Quick Integration: With just a single line of code, users can quickly integrate Helicone, creating a seamless monitoring experience that saves significant development time and ensures that every developer can hit the ground running! ⏱️

• Monitoring and Debugging: Helicone’s application request tracking and inspection functionalities allow for deep problem analysis, accelerating the troubleshooting process and helping developers quickly restore normal application operation. 🔍

• Real-Time Analysis: The platform can track multiple key metrics of application performance in real-time, such as costs, latency, and quality, helping developers make data-driven decisions and ensuring efficient application management. 📈

• Experimentation and Adjustment: Helicone enables developers to experiment with and modify prompts, allowing them to swiftly push high-quality changes into production, ensuring ongoing model optimization and high performance. ⚙️

• Enterprise-Level Compliance: Helicone complies with multiple security standards, including SOC 2 and GDPR, ensuring data security and providing peace of mind for enterprises to use it confidently! 🔒

3. Why Developers Love Helicone? Reasons to Choose It Revealed ❤️

Developers rave about Helicone not just for its capabilities, but also for the flexible options and robust support it provides:

• Diverse Deployment Options: Helicone offers both cloud-based and self-hosted deployment methods, greatly catering to diverse developer needs, allowing for flexible startup plans and personalized configurations for convenience! ☁️🏠

• Support for Leading LLM Providers: The platform is compatible with various popular LLM services, such as OpenAI, Anthropic, and Azure OpenAI, allowing developers to easily select and switch models to adapt to different application scenarios. 🔄

• Strong Community Support: Helicone boasts an active user community with a Discord platform where developers can share experiences, collaborate effectively, and even contribute code and participate in community development, collectively advancing the open-source project! 🤝

This robust functionality and flexible configuration options make Helicone an invaluable assistant for developers building and optimizing their AI applications, helping them stand out and succeed in a competitive market! 🌍💡

4. Installing Helicone: Easy to Get Started, Quick to Launch 😄

To successfully install Helicone, make sure you have Docker and Docker Compose installed on your machine. These two tools are essential dependencies for Helicone to operate smoothly, so ensure they are up to date. Now, let’s take a detailed look at how to clone the repository and set up the environment.

1. First, clone Helicone’s GitHub repository, navigate to the docker directory, and copy the example environment variable file:

   git clone https://github.com/Helicone/helicone.git
   cd docker
   cp .env.example .env
   

   The first step of this code downloads the Helicone project source code to your local machine using the git clone command. Next, the cd docker command switches to its Docker directory, where the files are crucial for configuring the Docker container. The last command cp .env.example .env copies the environment variable configuration from the example file, allowing you to modify the settings that meet your needs. This step is very important because the .env file will contain API keys and other configuration options required for using Helicone later.

2. Next, use Docker Compose to start the project:

   docker compose up
   

   The docker compose up command will start all services defined in the Docker Compose file, ensuring Helicone and its dependencies run smoothly. You can view related logs in the terminal that will help confirm whether the services started successfully. If your environment isn’t fully set up yet, follow the prompts to make necessary adjustments, ensuring there are no errors.

5. Creating an OpenAI Instance: Bridging with AI 🚀

In many applications, we need to access OpenAI services through Helicone. Here are the detailed steps to configure an OpenAI instance using Helicone.

import OpenAI from "openai";

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,  
  baseURL: `https://oai.helicone.ai/v1/${process.env.HELICONE_API_KEY}`,  
});

First, we import the OpenAI class with import OpenAI from "openai"; to enable API requests. We then create an instance named openai by calling new OpenAI() and passing the required configurations. Here, the apiKey is retrieved from the .env file using process.env.OPENAI_API_KEY, ensuring access to OpenAI’s API. The baseURL is the address of the Helicone API, created by concatenating with the environment variable HELICONE_API_KEY, which allows us to interact with Helicone.

6. Using Default Request Headers: Ensuring Smooth Requests 🌟

To ensure that requests to OpenAI can be sent successfully, we may need to add some default request headers. Here’s a related code example:

import OpenAI from "openai";

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,  
  baseURL: `https://oai.helicone.ai/v1`,  
  defaultHeaders: {
   "Helicone-Auth": `Bearer ${process.env.HELICONE_API_KEY}`,  
  },
});

In this code snippet, in addition to the previously mentioned apiKey and baseURL, we set up defaultHeaders. This option is an object used to store all default request headers. The Helicone-Auth header includes the Bearer keyword followed by our API key, which is necessary for authentication. Adding this request header helps ensure that each API request carries the required authentication information, preventing unauthorized access.

7. Sending Requests to Retrieve Information: The Moment of Interaction with AI 📬

With the above configurations mastered, it’s now time to send requests through the OpenAI instance to translate and retrieve data. Here’s an example of sending a request:

import OpenAI from "openai";

const openai = new OpenAI({
  apiKey: OPENAI_API_KEY, 
  baseURL: `https://oai.helicone.ai/v1/${HELICONE_API_KEY}/`
});

// Call API to send request
const response = await openai.chat.completions.create({
  messages: [
    {
      role: "system",
      content: "Get..."
    }
  ]
});

In this code snippet, we again create an openai instance, ensuring everything is set correctly. Next, we use the await keyword to wait for the API’s response, indicating that this is an asynchronous operation; the program will pause here until the result is obtained. When calling openai.chat.completions.create(), we pass an object where we set the messages data. The messages array contains objects representing individual messages with role and content fields to build the conversation’s context. Ultimately, Helicone will generate corresponding outputs based on these inputs, marking an essential step in interacting with AI.

With the above steps, you’ve now mastered the basic operations of installing, configuring, and calling Helicone, successfully entering the exciting world of interacting with OpenAI! 🎉