Get Started with easy_llm_agents for Complete Beginners

ChatGPT Mar 23 Version

In this article, we'll introduce you to the easy_llm_agents library, a versatile Python package for managing and extending language model-based agents. As a complete beginner, you'll learn how to use Git and navigate the terminal to take advantage of this library.

The Value for the Reader

easy_llm_agents simplifies the process of integrating AI agents into your projects. By following this guide, you'll be able to utilize AI in practical applications, even without prior programming experience. The examples provided are designed to be accessible and easy to understand, allowing you to quickly benefit from the power of AI.

What is Git?

Git is a version control system that helps you manage and track changes in your code. It's essential for collaborating with others and maintaining a clear history of your project's development.

Opening the Terminal

Windows

1. Press Win + R to open the Run dialog.

2. Type cmd and press Enter.

macOS

1. Press Cmd + Space to open Spotlight.

2. Type Terminal and press Enter.

Linux

1. Press Ctrl + Alt + T to open the Terminal.

Basic Terminal Commands

• cd <directory>: Change the current working directory to <directory>.

• ls (macOS/Linux) or dir (Windows): List the contents of the current directory.

• mkdir <directory>: Create a new directory with the name <directory>.

• touch <filename> (macOS/Linux) or echo. > <filename> (Windows): Create a new file with the name <filename>.

Cloning easy_llm_agents Repository

In your terminal, navigate to the folder where you want to save the easy_llm_agents repository and run the following command:

git clone https://github.com/virtualzx-nad/easy_llm_agents.git

This will create a folder called easy_llm_agents with the project files. Change to the new directory:

cd easy_llm_agents

Creating a Python Virtual Environment

Create a virtual environment to manage the library's dependencies:

python3 -m venv venv

python3 -m venv venv

Activate the virtual environment:

• Windows: venv\Scripts\activate

• macOS/Linux: source venv/bin/activate

Install the required packages:

pip install -r requirements.txt

Creating Custom Commands

We'll use two examples to help you get started with easy_llm_agents: GreetingCommand and WeatherCommand.

First, create a new Python file for your custom commands:

touch custom_commands.py

Open the custom_commands.py file in a text editor and add the following code for the GreetingCommand:

from easy_llm_agents.command import BaseCommand

class GreetingCommand(BaseCommand, command='GREET', description='Greet the user'):
    def generate_prompt(self):
        return 'Hello, how can I help you today?'

Next, add the WeatherCommand:

import requests

class WeatherCommand(BaseCommand, command='WEATHER', description='Get the current weather for a given city'):
    def generate_prompt(self, city):
        api_key = 'your_openweathermap_api_key'
        url = f'http://api.openweathermap.org/data/2.5/weather?q={city}&appid={api_key}'
        response = requests.get(url).json()
        if response['cod'] == 200:
            weather = response['weather'][0]['description']
            return f'The current weather in {city} is {weather}.'
        else:
            return f"Sorry, I couldn't get the weather information for {city}."

Make sure to replace `'your_openweathermap_api_key'` with your actual API key, which you can obtain by signing up at https://openweathermap.org/.

Now, you have two custom commands: GreetingCommand and WeatherCommand.

Using ChatGPT to Resolve Errors or Roadblocks

If you encounter any issues or errors while working with easy_llm_agents, you can consult ChatGPT to find solutions or get guidance. Simply ask your question or describe the problem you're facing, and ChatGPT will provide helpful information to assist you in resolving the issue.

Terminal: The Ultimate Interface to LLMs ("Pure Tokens")

As you gain more experience with programming, you'll come to appreciate the terminal as a powerful and efficient interface for interacting with language models and other tools. By mastering the terminal, you'll have direct access to the "pure tokens" that drive language models like GPT-4, giving you greater control and flexibility in your projects.

Conclusion

By following this guide, you've learned how to clone the easy_llm_agents repository using Git, create a Python virtual environment, and add custom commands to the library. This knowledge will enable you to start leveraging AI in your projects, even as a complete beginner to programming. Don't be afraid to ask ChatGPT for help or clarification if you run into any issues, and remember that the terminal is a powerful tool for interacting with language models and other programming tasks.

In the next blog post of this series, we will dive deeper into integrating ChatGPT with easy_llm_agents to build more complex and interactive applications. As you progress through this journey, remember that learning to program takes time and patience. Keep experimenting and building upon your knowledge, and you'll find that working with language models and AI can be both rewarding and enjoyable.

To help you continue learning and growing as a programmer, here are some topics we'll cover in upcoming posts:

1. Creating a simple chatbot application using easy_llm_agents and ChatGPT.

2. Handling user input and managing the flow of conversation.

3. Advanced techniques for improving your AI agent's responses and capabilities.

4. Exploring more powerful LLMs and how to work with them safely and effectively.

5. Using the terminal efficiently and mastering its potential as the ultimate interface to LLMs.

As you explore these topics, always remember that programming is a skill that improves with practice. Don't hesitate to ask for help or consult resources, such as ChatGPT, when you encounter challenges. Embrace the learning process and enjoy the journey towards becoming a skilled programmer and AI developer.