Dspy Ruby

DSPy.rb Expert

Overview

DSPy.rb is a Ruby framework that enables developers to program LLMs, not prompt them. Instead of manually crafting prompts, define application requirements through type-safe, composable modules that can be tested, optimized, and version-controlled like regular code.

Mode Detection

Determine what the user needs, then read ONLY the relevant reference files before proceeding.

All reference files are in ~/.claude/skills/dspy-ruby/references/.

Core Capabilities (Quick Reference)

1. Type-Safe Signatures

class EmailClassificationSignature < DSPy::Signature
  description "Classify customer support emails"

  input do
    const :email_subject, String
    const :email_body, String
  end

  output do
    const :category, T.enum(["Technical", "Billing", "General"])
    const :priority, T.enum(["Low", "Medium", "High"])
  end
end

Best practices: Clear descriptions, enums for constrained outputs, desc: parameter for fields, specific types over generic String. Full docs: core-concepts.md.

2. Composable Modules

class EmailProcessor < DSPy::Module
  def initialize
    super
    @classifier = DSPy::Predict.new(EmailClassificationSignature)
  end

  def forward(email_subject:, email_body:)
    @classifier.forward(email_subject: email_subject, email_body: email_body)
  end
end

Chain modules for complex workflows. Full docs: core-concepts.md.

3. Predictor Types

Full docs: core-concepts.md.

4. LLM Provider Configuration

# OpenAI
DSPy.configure { |c| c.lm = DSPy::LM.new('openai/gpt-4o-mini', api_key: ENV['OPENAI_API_KEY']) }

# Anthropic Claude
DSPy.configure { |c| c.lm = DSPy::LM.new('anthropic/claude-3-5-sonnet-20241022', api_key: ENV['ANTHROPIC_API_KEY']) }

# Google Gemini
DSPy.configure { |c| c.lm = DSPy::LM.new('gemini/gemini-1.5-pro', api_key: ENV['GOOGLE_API_KEY']) }

# Local Ollama (free, private)
DSPy.configure { |c| c.lm = DSPy::LM.new('ollama/llama3.1') }

Provider compatibility matrix:

Cost optimization: Dev = Ollama/gpt-4o-mini, Test = gpt-4o-mini (temp=0), Prod simple = gpt-4o-mini/haiku/flash, Prod complex = gpt-4o/sonnet/pro. Full docs: providers.md.

5. Testing

RSpec.describe EmailClassifier do
  before do
    DSPy.configure { |c| c.lm = DSPy::LM.new('openai/gpt-4o-mini', api_key: ENV['OPENAI_API_KEY']) }
  end

  it 'classifies technical emails correctly' do
    result = EmailClassifier.new.forward(email_subject: "Can't log in", email_body: "Unable to access account")
    expect(result[:category]).to eq('Technical')
  end
end

Full docs: optimization.md.

Reference Files Index

Assets (templates for quick starts)

• assets/signature-template.rb -- Examples of signatures including basic, vision, sentiment, code gen
• assets/module-template.rb -- Module patterns including pipelines, agents, error handling, caching
• assets/config-template.rb -- Configuration for all providers, environments, observability, production

When to Use This Skill

• Implementing LLM-powered features in Ruby applications
• Creating type-safe interfaces for AI operations
• Building agent systems with tool usage
• Setting up or troubleshooting LLM providers
• Optimizing prompts and improving accuracy
• Testing LLM functionality
• Adding observability to AI applications
• Converting from manual prompt engineering to programmatic approach
• Debugging DSPy.rb code or configuration issues