---
title: A Flexible AI Integration Pattern for Ruby on Rails
slug: a-flexible-ai-integration-pattern-for-ruby-on-rails
published_at: 2024-06-18 21:02:00 +0000
updated_at: 2024-07-27 21:59:30 +0000
summary: In this article, you will learn how to integrate AI into your Ruby on Rails applications using a flexible pattern that allows easy swapping of AI providers like OpenAI and Claude. 🤖✨
tags: [Rails, Ruby, AI]
author: CJ Avilla
url: https://www.cjav.dev/articles/a-flexible-ai-integration-pattern-for-ruby-on-rails
type: article
---

# A Flexible AI Integration Pattern for Ruby on Rails

*Published: June 18, 2024*
*Tags: Rails, Ruby, AI*

You’re likely already sprinkling in some AI features to your rails applications, right? At Craftwork, we’ve added a few power features. For instance, we built an assistant for our customer service and sales messaging platform. It generates responses to customers based on context like ongoing project details, past customer messages, image descriptions, progress reports, and FAQs to provide ([human-in-the-loop](https://en.wikipedia.org/wiki/Human-in-the-loop)) support.

A known challenge is that providers like OpenAI and Anthropic constantly ship new models, and the dust hasn’t really settled on their APIs. We've switched between OpenAI, Claude, and others multiple times in the last year as new, more capable models are released. This makes it crucial to have a flexible approach to AI integration that allows easily swapping providers.

We’ve iterated a few times on a flexible integration pattern that I’m sharing here in hopes you find it useful.

Here's a roadmap of where we're headed:

![image.png](/rails/active_storage/blobs/eyJfcmFpbHMiOnsiZGF0YSI6NzU2LCJwdXIiOiJibG9iX2lkIn19--5ea22189b786254459af8423884f628b4f64e18c/image.png)


## `Ai::Service`

One of the nice things about the [langchain](https://github.com/langchain-ai/langchain) framework in Python is that it provides a suite of provider-agnostic abstractions. While I’ve experimented with [langchain.rb](https://github.com/patterns-ai-core/langchainrb), I felt like our use case didn’t fit well enough with the solutions available in that gem. The `Ai::Service` class is a single entry point for all AI-related actions. It lets us switch between default models and providers without changing the underlying API client wrappers or going to all the different uses. Instead of scattered AI-related code throughout our codebase, we can centralize all AI-related functionality within the `Ai::Service` model, making it easier to reason about, test, and maintain.

To use the `Ai::Service` model, we initialize it with a provider and a user. The provider is optional; without it, we’ll fall back to the current default provider for a given action. The user allows us to generate unique and personalized prompts for employees when they talk with customers. 

```rb
service = Ai::Service.new(user: current_user, provider: :open_ai)
# or
service = Ai::Service.new(user: current_user)
```

The `Ai::Service` model exposes methods for common AI tasks, like image description, text generation, and chat messages. For example, to transcribe an audio file using Deepgram, we can call the `transcribe` method:

```rb
transcription = service.transcribe(audio_url: 'https://example.com/call-recording.mp3')
```

Similarly, to describe an image or generate a message for a chat conversation, we can use the `describe_image` or `chat` methods, respectively:

```rb
image_description = service.describe_image(image_url: 'https://example.com/image.png')
response = service.chat(
  system_prompt: 'You are a helpful assistant', 
  messages: conversation_history
)
```

It's important to note that while the `Ai::Service` model provides a unified interface for AI services, the actual implementation details are encapsulated within provider-specific adapters.

Here’s a simplified example of an `Ai::Service`:

```rb
module Ai
  class Service
    attr_reader :user

    def initialize(user: nil)
      @user = user
    end

    def describe_image(image_url:, provider: :open_ai)
      client = llm_client(provider)
      client.describe_image(image_url: image_url)
    end

    def generate_review(project:, provider: :open_ai)
      client = llm_client(provider)
      client.generate_review(project: project)
    end

    def chat(system_prompt:, messages:, provider: :claude)
      client = llm_client(provider)
      client.chat(system_prompt: system_prompt, messages: messages)
    end

    def transcribe(audio_url:, provider: :deepgram)
      client = llm_client(provider)
      client.transcribe(audio_url: audio_url)
    end

    private

    def llm_client(provider)
      case provider
      when :open_ai
        OpenAiClient.client(user: user)
      when :claude
        ClaudeClient.client(user: user)
      when :deepgram
        DeepgramClient.client(user: user)
      when :gemini
        GeminiClient.client(user: user)
      when :ollama
        OllamaClient.client(user: user)
      when :fake
        FakeClient.new(user: user)
      else
        raise "Unknown provider: #{provider}"
      end
    end
  end
end
```

## `Ai::Clients`

You’ll notice the `llm_client` returns client models like `Ai::OpenAiClient` and `Ai::ClaudeClient`. Again, each client is an adapter that wraps the provider-specific logic and communication protocols, abstracting away the complexities of interacting with various AI providers.

The primary role of these client models is to act as a bridge between our application and the respective AI provider's API.

Here's an example of the `Ai::ClaudeClient`. Notice the `generate_review` method, which generates example customer reviews unique to each project that we use to inspire customers when asking them to write one:

```rb
module Ai
  class ClaudeClient < ApplicationClient
    BASE_URI = "https://api.anthropic.com".freeze

    def generate_review(project:, prompt_manager_class: PromptManager)
      @project = project
      prompt_manager = prompt_manager_class.new(binding)
      system_prompt = prompt_manager.render("generate_review/system")
      user_prompt = prompt_manager.render("generate_review/user")

      result = chat(
        model: "claude-3-opus-20240229",
        system_prompt: system_prompt,
        messages: [{
          role: "user",
          content: user_prompt
        }],
        max_tokens: 512
      )

      {
        review: result.dig(:content, 0, :text)
      }
    end

    # Continued...
  end
end
```

In this example, the `generate_review` method utilizes the `chat` method provided by the Claude API to generate example reviews based on the provided system prompt, user prompt, and project information. If you’re wondering about `PromptManager`, sit tight; I’ll explain briefly!

## Prompt Management

Prompt engineering was hyped about a year ago.

![prompt eng trends](/rails/active_storage/blobs/eyJfcmFpbHMiOnsiZGF0YSI6NzU1LCJwdXIiOiJibG9iX2lkIn19--d5d2db7f9dc26a4d3da3d6c340587492871a7d30/image.png)

The quality and structure of prompts can significantly impact performance and accuracy.

The `Ai::PromptManager` class and `Ai::Promptable` module are little utilities for rendering prompts, similar to Rails application views from template files. I wanted to maintain the high-level structure of prompts and easily inject context from data with ActiveRecord.

Prompts are stored as ERB (Embedded Ruby) templates. For example, the system and user prompts for the "generate_review" task might be stored in the following files:

```
app/clients/ai/prompts/generate_review/system.txt.erb
app/clients/ai/prompts/generate_review/user.txt.erb
```

Here's an example of how the `Ai::PromptManager` can be used to render a prompt from a template file:

```rb
module Ai
  class ClaudeClient < ApplicationClient
    def generate_review(project:, prompt_manager_class: PromptManager)
      @project = project
      prompt_manager = prompt_manager_class.new(binding)
      system_prompt = prompt_manager.render("generate_review/system")
      user_prompt = prompt_manager.render("generate_review/user")

      # ... (rest of the method implementation)
    end
  end
end
```

In this example, the `generate_review` method initializes an instance of the `PromptManager` and uses the `render` method to retrieve the system and user prompts from their respective template files. The `binding` passed to the `PromptManager` allows for an experience similar to working with rails views where methods and instance variables are available in the prompts.

Here’s an example of a prompt for generating customer message responses. Notice how you can write it similar to a rails view, but we’re spitting out text instead of html:

```
<%= preamble %>

Frequently asked questions:

<% Faq.kept.each do |faq| %>
Question: <%= faq.question %>
Answer: <%= faq.answer %>
<% end %>

Customer Information:

<%= render "customer", partial: true, locals: { customer: conversation.account } %>

<% if progress_reports.any? %>
Today's date is <%= Date.today.strftime("%A, %B %d, %Y") %>.

The paint crew submits daily progress reports from the field at the end of each day's work.

Include details from the most recent progress report to keep the customer informed and engaged if possible.

<% progress_reports.each do |report| %>
---
Report about project "<%= report.project.title %>" from <%= report.created_at.strftime("%A, %B %d, %Y") %>:

<%= report.report_display %>
<% end %>
---
<% end %>
# continued...
```


## Integrating with Background Jobs

Many tasks, like image analysis or text generation, introduce latency. To ensure a relatively smooth experience, I like to offload these tasks to background jobs.

Here's an example of how we might use the `Ai::Service` within a background job to generate image descriptions:

```rb
class AddImageDescriptionJob
  include Sidekiq::Worker
  sidekiq_options queue: :low, retry: false

  def perform(blob_id)
    blob = ActiveStorage::Blob.find(blob_id)
    return unless blob.image?
    return if blob.image_data_record.present?

    response = Ai::Service.new.describe_image(image_url: blob.url)
    description = response[:description]

    ImageDataRecord.create!(
      description: description,
      blob_id: blob_id,
      human_reviewed: false,
    )
  end
end
```

## Future Improvements and Considerations

While our pattern provides a solid foundation for sprinkling in AI features, there are several ideas. Here are some examples I’d love to add:

 - A/B testing and experimentation for prompts
 - Function calling and tools (e.g. fetch the price from our model for “painting the walls in a large room”)
 - Provider-specific prompts

Leveraging a modular and decoupled architecture, this pattern empowers you to harness the power of different providers while maintaining a high degree of flexibility and adaptability.

While our flexible AI integration pattern provides a solid foundation for adding bits of AI to a web application, I’m not sold on this approach as the best approach to building a full-on agent or product where AI is the core of the business. 

I’d love to hear about your approach! How are you solving these problems?


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