AI-Powered Apps with Rails: Leveraging LLMs for Next-Level Development

Implementing AI Agents in Rails with RubyLLM

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In this post, we'll explore how to create custom OpenAI chat agents using the RubyLLM gem. We'll discuss the key concepts, implementation details, and provide practical code examples for building reusable AI assistants.

Introduction to RubyLLM Agents

RubyLLM Agents are a type of augmented LLM (Large Language Model) interface that provides access to a list of predefined tools. These agents can be configured with their own models, runtime context, and prompt conventions, making them reusable AI assistants.

Benefits of Chat-Based Agents

Chat-based agents have several advantages over fully autonomous agents like Claude Code or Codex:

• User Input: They still react to user input, allowing for a more interactive experience.
• Tool Integration: They can access a list of predefined tools, enabling seamless integration with existing workflows.

Prerequisites and Setup

Before diving into implementation details, ensure you have the following prerequisites set up:

Install RubyLLM Gem

gem install rubyllm

Set Up Environment Variables

Create environment variables for your API keys (e.g., OPENAI_API_KEY).

Initialize RubyLLM

In your Rails application's configuration file (config/initializers/rubyllm.rb), add the following code:

require 'rubyllm'

RubyLLM.configure do |config|
  config.api_key = ENV['OPENAI_API_KEY']
end

Creating a Custom AI Agent

Let's create a basic AI agent using RubyLLM. We'll define a ChatAgent class that inherits from RubyLLM::Agent.

# app/models/chat_agent.rb
require 'rubyllm'

class ChatAgent < RubyLLM::Agent
  def initialize
    super('text-davinci-003')
  end

  def respond(message)
    # Implement your custom logic here
    message
  end
end

Configuring the Agent

To configure our agent, we'll create a separate class that will handle initialization and setup.

# app/models/chat_agent_config.rb
class ChatAgentConfig < RubyLLM::Configuration
  def initialize(agent)
    super
    @agent = agent
  end

  def configure(agent)
    # Set your custom configuration here
  end
end

Using the Agent in Your Rails Application

Now that we have our AI agent defined, let's use it in a controller action.

# app/controllers/chats_controller.rb
class ChatsController < ApplicationController
  def create
    chat_agent = ChatAgent.new
    response = chat_agent.respond(params[:message])
    render json: { response: response }
  end
end

Best Practices and Next Steps

When building your custom AI agents, consider the following best practices:

• Keep it simple: Avoid overcomplicating your agent's configuration and logic.
• Use a consistent naming convention: Use meaningful names for your classes, methods, and variables.
• Document your code: Make sure to include comments and documentation for future maintenance.

This post has provided an introduction to creating custom OpenAI chat agents using the RubyLLM gem. With this knowledge, you can start building reusable AI assistants that integrate with your existing workflows.

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By Malik Abualzait