Ruby AI eXtensions

What's Raix

Raix (pronounced "ray" because the x is silent) is a library that gives you everything you need to add discrete large-language model (LLM) AI components to your Ruby applications. Raix consists of proven code that has been extracted from Olympia, the world's leading virtual AI team platform, and probably one of the biggest and most successful AI chat projects written completely in Ruby.

Understanding how to use discrete AI components in otherwise normal code is key to productively leveraging Raix, and the subject of a book written by Raix's author Obie Fernandez, titled Patterns of Application Development Using AI. You can easily support the ongoing development of this project by buying the book at Leanpub.

At the moment, Raix natively supports use of either OpenAI or OpenRouter as its underlying AI provider. Eventually you will be able to specify your AI provider via an adapter, kind of like ActiveRecord maps to databases. Note that you can also use Raix to add AI capabilities to non-Rails applications as long as you include ActiveSupport as a dependency. Extracting the base code to its own standalone library without Rails dependencies is on the roadmap, but not a high priority.

Chat Completions

Raix consists of three modules that can be mixed in to Ruby classes to give them AI powers. The first (and mandatory) module is ChatCompletion, which provides transcript and chat_completion methods.

class MeaningOfLife
  include Raix::ChatCompletion
end

>> ai = MeaningOfLife.new
>> ai.transcript << { user: "What is the meaning of life?" }
>> ai.chat_completion

=> "The question of the meaning of life is one of the most profound and enduring inquiries in philosophy, religion, and science.
    Different perspectives offer various answers..."

By default, Raix will automatically add the AI's response to the transcript. This behavior can be controlled with the save_response parameter, which defaults to true. You may want to set it to false when making multiple chat completion calls during the lifecycle of a single object (whether sequentially or in parallel) and want to manage the transcript updates yourself:

>> ai.chat_completion(save_response: false)

Transcript Format

The transcript accepts both abbreviated and standard OpenAI message hash formats. The abbreviated format, suitable for system, assistant, and user messages is simply a mapping of role => content, as shown in the example above.

transcript << { user: "What is the meaning of life?" }

As mentioned, Raix also understands standard OpenAI messages hashes. The previous example could be written as:

transcript << { role: "user", content: "What is the meaning of life?" }

One of the advantages of OpenRouter and the reason that it is used by default by this library is that it handles mapping message formats from the OpenAI standard to whatever other model you're wanting to use (Anthropic, Cohere, etc.)

Note that it's possible to override the current object's transcript by passing a messages array to chat_completion. This allows for multiple threads to share a single conversation context in parallel, by deferring when they write their responses back to the transcript.

chat_completion(openai: "gpt-4.1-nano", messages: [{ user: "What is the meaning of life?" }])

Predicted Outputs

Raix supports Predicted Outputs with the prediction parameter for OpenAI.

>> ai.chat_completion(openai: "gpt-4o", params: { prediction: })

Prompt Caching

Raix supports Anthropic-style prompt caching when using Anthropic's Claude family of models. You can specify a cache_at parameter when doing a chat completion. If the character count for the content of a particular message is longer than the cache_at parameter, it will be sent to Anthropic as a multipart message with a cache control "breakpoint" set to "ephemeral".

Note that there is a limit of four breakpoints, and the cache will expire within five minutes. Therefore, it is recommended to reserve the cache breakpoints for large bodies of text, such as character cards, CSV data, RAG data, book chapters, etc. Raix does not enforce a limit on the number of breakpoints, which means that you might get an error if you try to cache too many messages.

>> my_class.chat_completion(params: { cache_at: 1000 })
=> {
  "messages": [
    {
      "role": "system",
      "content": [
        {
          "type": "text",
          "text": "HUGE TEXT BODY LONGER THAN 1000 CHARACTERS",
          "cache_control": {
            "type": "ephemeral"
          }
        }
      ]
    },

JSON Mode

Raix supports JSON mode for chat completions, which ensures that the AI model's response is valid JSON. This is particularly useful when you need structured data from the model.

When using JSON mode with OpenAI models, Raix will automatically set the response_format parameter on requests accordingly, and attempt to parse the entire response body as JSON. When using JSON mode with other models (e.g. Anthropic) that don't support response_format, Raix will look for JSON content inside of <json> XML tags in the response, before falling back to parsing the entire response body. Make sure you tell the AI to reply with JSON inside of XML tags.

>> my_class.chat_completion(json: true)
=> { "key": "value" }

When using JSON mode with non-OpenAI providers, Raix automatically sets the require_parameters flag to ensure proper JSON formatting. You can also combine JSON mode with other parameters:

>> my_class.chat_completion(json: true, openai: "gpt-4o")
=> { "key": "value" }

Use of Tools/Functions

The second (optional) module that you can add to your Ruby classes after ChatCompletion is FunctionDispatch. It lets you declare and implement functions to be called at the AI's discretion as part of a chat completion "loop" in a declarative, Rails-like "DSL" fashion.

Most end-user facing AI components that include functions should be invoked using chat_completion(loop: true), so that the results of each function call are added to the transcript and chat completion is triggered again. The looping will continue until the AI generates a plain text response.

class WhatIsTheWeather
  include Raix::ChatCompletion
  include Raix::FunctionDispatch

  function :check_weather, "Check the weather for a location", location: { type: "string" } do |arguments|
    "The weather in #{arguments[:location]} is hot and sunny"
  end
end

RSpec.describe WhatIsTheWeather do
  subject { described_class.new }

  it "can call a function and loop to provide text response" do
    subject.transcript << { user: "What is the weather in Zipolite, Oaxaca?" }
    response = subject.chat_completion(openai: "gpt-4o", loop: true)
    expect(response).to include("hot and sunny")
  end
end

Note that for security reasons, dispatching functions only works with functions implemented using Raix::FunctionDispatch#function or directly on the class.

Tool Filtering

You can control which tools are available to the AI on a given chat completion request using the tools parameter in the chat_completion method:

class WeatherAndTime
  include Raix::ChatCompletion
  include Raix::FunctionDispatch

  function :check_weather, "Check the weather for a location", location: { type: "string" } do |arguments|
    "The weather in #{arguments[:location]} is sunny"
  end

  function :get_time, "Get the current time" do |_arguments|
    "The time is 12:00 PM"
  end
end

weather = WeatherAndTime.new

# Don't pass any tools to the LLM
weather.chat_completion(tools: false)

# Only pass specific tools to the LLM
weather.chat_completion(tools: [:check_weather])

# Pass all declared tools (default behavior)
weather.chat_completion

The tools parameter accepts three types of values:

false: No tools are passed to the LLM
An array of symbols: Only the specified tools are passed (raises Raix::UndeclaredToolError if any tool is not declared)
Not provided: All declared tools are passed (default behavior)

Multiple Tool Calls

Some AI models (like GPT-4) can make multiple tool calls in a single response. When this happens, Raix will automatically handle all the function calls sequentially. If you need to capture the arguments to the function calls, do so in the block passed to function. The response from chat_completion is always the final text response from the assistant, and is not affected by function calls.

class MultipleToolExample
  include Raix::ChatCompletion
  include Raix::FunctionDispatch

  attr_reader :invocations

  function :first_tool do |arguments|
    @invocations << :first
    "Result from first tool"
  end

  function :second_tool do |arguments|
    @invocations << :second
    "Result from second tool"
  end

  def initialize
    @invocations = []
  end
end

example = MultipleToolExample.new
example.transcript << { user: "Please use both tools" }
example.chat_completion(openai: "gpt-4o")
# => "I used both tools, as requested"

example.invocations
# => [:first, :second]

Manually Stopping a Loop

To loop AI components that don't interact with end users, at least one function block should invoke stop_looping! whenever you're ready to stop processing.

class OrderProcessor
  include Raix::ChatCompletion
  include Raix::FunctionDispatch

  SYSTEM_DIRECTIVE = "You are an order processor, tasked with order validation, inventory check,
                      payment processing, and shipping."

  attr_accessor :order

  def initialize(order)
    self.order = order
    transcript << { system: SYSTEM_DIRECTIVE }
    transcript << { user: order.to_json }
  end

  def perform
    # will continue looping until `stop_looping!` is called
    chat_completion(loop: true)
  end


  # implementation of functions that can be called by the AI
  # entirely at its discretion, depending on the needs of the order.
  # The return value of each `perform` method will be added to the
  # transcript of the conversation as a function result.

  function :validate_order do
    OrderValidationWorker.perform(@order)
  end

  function :check_inventory do
    InventoryCheckWorker.perform(@order)
  end

  function :process_payment do
    PaymentProcessingWorker.perform(@order)
  end

  function :schedule_shipping do
    ShippingSchedulerWorker.perform(@order)
  end

  function :send_confirmation do
    OrderConfirmationWorker.perform(@order)
  end

  function :finished_processing do
    order.update!(transcript:, processed_at: Time.current)
    stop_looping!
  end
end

Prompt Declarations

The third (also optional) module that you can add mix in along with ChatCompletion is PromptDeclarations. It provides the ability to declare a "Prompt Chain" (series of prompts to be called in a sequence), and also features a declarative, Rails-like "DSL" of its own. Prompts can be defined inline or delegate to callable prompt objects, which themselves implement ChatCompletion.

The following example is a rough excerpt of the main "Conversation Loop" in Olympia, which pre-processes user messages to check for the presence of URLs and scan memory before submitting as a prompt to GPT-4. Note that prompt declarations are executed in the order that they are declared. The FetchUrlCheck callable prompt class is included for instructional purposes. Note that it is passed the an instance of the object that is calling it in its initializer as its context. The passing of context means that you can assemble composite prompt structures of arbitrary depth.

class PromptSubscriber
  include Raix::ChatCompletion
  include Raix::PromptDeclarations

  attr_accessor :conversation, :bot_message, :user_message

  # many other declarations omitted...

  prompt call: FetchUrlCheck

  prompt call: MemoryScan

  prompt text: -> { user_message.content }, stream: -> { ReplyStream.new(self) }, until: -> { bot_message.complete? }

  def initialize(conversation)
    self.conversation = conversation
  end

  def message_created(user_message)
    self.user_message = user_message
    self.bot_message = conversation.bot_message!(responding_to: user_message)

    chat_completion(loop: true, openai: "gpt-4o")
  end

  ...
end

class FetchUrlCheck
  include ChatCompletion
  include FunctionDispatch

  REGEX = %r{\b(?:http(s)?://)?(?:www\.)?[a-zA-Z0-9-]+(\.[a-zA-Z]{2,})+(/[^\s]*)?\b}

  attr_accessor :context, :conversation

  delegate :user_message, to: :context
  delegate :content, to: :user_message

  def initialize(context)
    self.context = context
    self.conversation = context.conversation
    self.model = "anthropic/claude-3-haiku"
  end

  def call
    return unless content&.match?(REGEX)

    transcript << { system: "Call the `fetch` function if the user mentions a website, otherwise say nil" }
    transcript << { user: content }

    chat_completion # TODO: consider looping to fetch more than one URL per user message
  end

  function :fetch, "Gets the plain text contents of a web page", url: { type: "string" } do |arguments|
    Tools::FetchUrl.fetch(arguments[:url]).tap do |result|
      parent = conversation.function_call!("fetch_url", arguments, parent: user_message)
      conversation.function_result!("fetch_url", result, parent:)
    end
  end

Notably, Olympia does not use the FunctionDispatch module in its primary conversation loop because it does not have a fixed set of tools that are included in every single prompt. Functions are made available dynamically based on a number of factors including the user's plan tier and capabilities of the assistant with whom the user is conversing.

Streaming of the AI's response to the end user is handled by the ReplyStream class, passed to the final prompt declaration as its stream parameter. Patterns of Application Development Using AI devotes a whole chapter to describing how to write your own ReplyStream class.

Additional PromptDeclarations Options

The PromptDeclarations module supports several additional options that can be used to customize prompt behavior:

class CustomPromptExample
  include Raix::ChatCompletion
  include Raix::PromptDeclarations

  # Basic prompt with text
  prompt text: "Process this input"

  # Prompt with system directive
  prompt system: "You are a helpful assistant",
        text: "Analyze this text"

  # Prompt with conditions
  prompt text: "Process this input",
        if: -> { some_condition },
        unless: -> { some_other_condition }

  # Prompt with success callback
  prompt text: "Process this input",
        success: ->(response) { handle_response(response) }

  # Prompt with custom parameters
  prompt text: "Process with custom settings",
        params: { temperature: 0.7, max_tokens: 1000 }

  # Prompt with until condition for looping
  prompt text: "Keep processing until complete",
        until: -> { processing_complete? }

  # Prompt with raw response
  prompt text: "Get raw response",
        raw: true

  # Prompt using OpenAI directly
  prompt text: "Use OpenAI",
        openai: true
end

The available options include:

system: Set a system directive for the prompt
if/unless: Control prompt execution with conditions
success: Handle prompt responses with callbacks
params: Customize API parameters per prompt
until: Control prompt looping
raw: Get raw API responses
openai: Use OpenAI directly
stream: Control response streaming
call: Delegate to callable prompt objects

You can also access the current prompt context and previous responses:

class ContextAwarePrompt
  include Raix::ChatCompletion
  include Raix::PromptDeclarations

  def process_with_context
    # Access current prompt
    current_prompt.params[:temperature]

    # Access previous response
    last_response

    chat_completion
  end
end

Predicate Module

The Raix::Predicate module provides a simple way to handle yes/no/maybe questions using AI chat completion. It allows you to define blocks that handle different types of responses with their explanations. It is one of the concrete patterns described in the "Discrete Components" chapter of Patterns of Application Development Using AI.

Usage

Include the Raix::Predicate module in your class and define handlers using block syntax:

class Question
  include Raix::Predicate

  yes? do |explanation|
    puts "Affirmative: #{explanation}"
  end

  no? do |explanation|
    puts "Negative: #{explanation}"
  end

  maybe? do |explanation|
    puts "Uncertain: #{explanation}"
  end
end

question = Question.new
question.ask("Is Ruby a programming language?")
# => Affirmative: Yes, Ruby is a dynamic, object-oriented programming language...

Features

Define handlers for yes, no, and/or maybe responses using the declarative class level block syntax.
At least one handler (yes, no, or maybe) must be defined.
Handlers receive the full AI response including explanation as an argument.
Responses always start with "Yes, ", "No, ", or "Maybe, " followed by an explanation.
Make sure to ask a question that can be answered with yes, no, or maybe (otherwise the results are indeterminate).

Example with Single Handler

You can define only the handlers you need:

class SimpleQuestion
  include Raix::Predicate

  # Only handle positive responses
  yes? do |explanation|
    puts "✅ #{explanation}"
  end
end

question = SimpleQuestion.new
question.ask("Is 2 + 2 = 4?")
# => ✅ Yes, 2 + 2 equals 4, this is a fundamental mathematical fact.

Error Handling

The module will raise a RuntimeError if you attempt to ask a question without defining any response handlers:

class InvalidQuestion
  include Raix::Predicate
end

question = InvalidQuestion.new
question.ask("Any question")
# => RuntimeError: Please define a yes and/or no block

Model Context Protocol (Experimental)

The Raix::MCP module provides integration with the Model Context Protocol, allowing you to connect your Raix-powered application to remote MCP servers. This feature is currently experimental.

Usage

Include the Raix::MCP module in your class and declare MCP servers using the mcp DSL:

class McpConsumer
  include Raix::ChatCompletion
  include Raix::FunctionDispatch
  include Raix::MCP

  mcp "https://your-mcp-server.example.com/sse"
end

Features

Automatically fetches available tools from the remote MCP server using tools/list
Registers remote tools as OpenAI-compatible function schemas
Defines proxy methods that forward requests to the remote server via tools/call
Seamlessly integrates with the existing FunctionDispatch workflow
Handles transcript recording to maintain consistent conversation history

Filtering Tools

You can filter which remote tools to include:

class FilteredMcpConsumer
  include Raix::ChatCompletion
  include Raix::FunctionDispatch
  include Raix::MCP

  # Only include specific tools
  mcp "https://server.example.com/sse", only: [:tool_one, :tool_two]

  # Or exclude specific tools
  mcp "https://server.example.com/sse", except: [:tool_to_exclude]
end

Response Format (Experimental)

The ResponseFormat class provides a way to declare a JSON schema for the response format of an AI chat completion. It's particularly useful when you need structured responses from AI models, ensuring the output conforms to your application's requirements.

Features

Converts Ruby hashes and arrays into JSON schema format
Supports nested structures and arrays
Enforces strict validation with additionalProperties: false
Automatically marks all top-level properties as required
Handles both simple type definitions and complex nested schemas

Basic Usage

# Simple schema with basic types
format = Raix::ResponseFormat.new("PersonInfo", {
  name: { type: "string" },
  age: { type: "integer" }
})

# Use in chat completion
my_ai.chat_completion(response_format: format)

Complex Structures

# Nested structure with arrays
format = Raix::ResponseFormat.new("CompanyInfo", {
  company: {
    name: { type: "string" },
    employees: [
      {
        name: { type: "string" },
        role: { type: "string" },
        skills: ["string"]
      }
    ],
    locations: ["string"]
  }
})

Generated Schema

The ResponseFormat class generates a schema that follows this structure:

{
  "type": "json_schema",
  "json_schema": {
    "name": "SchemaName",
    "schema": {
      "type": "object",
      "properties": {
        "property1": { "type": "string" },
        "property2": { "type": "integer" }
      },
      "required": ["property1", "property2"],
      "additionalProperties": false
    },
    "strict": true
  }
}

Using with Chat Completion

When used with chat completion, the AI model will format its response according to your schema:

class StructuredResponse
  include Raix::ChatCompletion

  def analyze_person(name)
    format = Raix::ResponseFormat.new("PersonAnalysis", {
      full_name: { type: "string" },
      age_estimate: { type: "integer" },
      personality_traits: ["string"]
    })

    transcript << { user: "Analyze the person named #{name}" }
    chat_completion(params: { response_format: format })
  end
end

response = StructuredResponse.new.analyze_person("Alice")
# Returns a hash matching the defined schema

Installation

Install the gem and add to the application's Gemfile by executing:

$ bundle add raix

If bundler is not being used to manage dependencies, install the gem by executing:

$ gem install raix

If you are using the default OpenRouter API, Raix expects Raix.configuration.openrouter_client to initialized with the OpenRouter API client instance.

You can add an initializer to your application's config/initializers directory:

  # config/initializers/raix.rb
  Raix.configure do |config|
    config.openrouter_client = OpenRouter::Client.new
  end

You will also need to configure the OpenRouter API access token as per the instructions here: https://github.com/OlympiaAI/open_router?tab=readme-ov-file#quickstart

Development

After checking out the repo, run bin/setup to install dependencies. Then, run rake spec to run the tests. You can also run bin/console for an interactive prompt that will allow you to experiment.

Specs require OR_ACCESS_TOKEN and OAI_ACCESS_TOKEN environment variables, for access to OpenRouter and OpenAI, respectively. You can add those keys to a local unversioned .env file and they will be picked up by the dotenv gem.

To install this gem onto your local machine, run bundle exec rake install. To release a new version, update the version number in version.rb, and then run bundle exec rake release, which will create a git tag for the version, push git commits and the created tag, and push the .gem file to rubygems.org.

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/OlympiaAI/raix. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the code of conduct.

License

The gem is available as open source under the terms of the MIT License.

Code of Conduct

Everyone interacting in the Raix project's codebases, issue trackers, chat rooms and mailing lists is expected to follow the code of conduct.

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README.md

README.md

Ruby AI eXtensions

What's Raix

Chat Completions

Transcript Format

Predicted Outputs

Prompt Caching

JSON Mode

Use of Tools/Functions

Tool Filtering

Multiple Tool Calls

Manually Stopping a Loop

Prompt Declarations

Additional PromptDeclarations Options

Predicate Module

Usage

Features

Example with Single Handler

Error Handling

Model Context Protocol (Experimental)

Usage

Features

Filtering Tools

Response Format (Experimental)

Features

Basic Usage

Complex Structures

Generated Schema

Using with Chat Completion

Installation

Development

Contributing

License

Code of Conduct

Files

README.md

Latest commit

History

README.md

File metadata and controls

Ruby AI eXtensions

What's Raix

Chat Completions

Transcript Format

Predicted Outputs

Prompt Caching

JSON Mode

Use of Tools/Functions

Tool Filtering

Multiple Tool Calls

Manually Stopping a Loop

Prompt Declarations

Additional PromptDeclarations Options

Predicate Module

Usage

Features

Example with Single Handler

Error Handling

Model Context Protocol (Experimental)

Usage

Features

Filtering Tools

Response Format (Experimental)

Features

Basic Usage

Complex Structures

Generated Schema

Using with Chat Completion

Installation

Development

Contributing

License

Code of Conduct