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ratacat/claude-skills/dspy-ruby

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version: "1.0.1" name: dspy-ruby description: This skill should be used when working with DSPy.rb, a Ruby framework for building type-safe, composable LLM applications. Use this when implementing predictable AI features, creating LLM signatures and modules, configuring language model providers (OpenAI, Anthropic, Gemini, Ollama), building agent systems with tools, optimizing prompts, or testing LLM-powered functionality in Ruby applications.

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.

This skill provides comprehensive guidance on:

Creating type-safe signatures for LLM operations
Building composable modules and workflows
Configuring multiple LLM providers
Implementing agents with tools
Testing and optimizing LLM applications
Production deployment patterns

Core Capabilities

1. Type-Safe Signatures

Create input/output contracts for LLM operations with runtime type checking.

When to use: Defining any LLM task, from simple classification to complex analysis.

Quick reference:

ruby

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

Templates: See assets/signature-template.rb for comprehensive examples including:

Basic signatures with multiple field types
Vision signatures for multimodal tasks
Sentiment analysis signatures
Code generation signatures

Best practices:

Always provide clear, specific descriptions
Use enums for constrained outputs
Include field descriptions with desc: parameter
Prefer specific types over generic String when possible

Full documentation: See references/core-concepts.md sections on Signatures and Type Safety.

2. Composable Modules

Build reusable, chainable modules that encapsulate LLM operations.

When to use: Implementing any LLM-powered feature, especially complex multi-step workflows.

Quick reference:

ruby

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

Templates: See assets/module-template.rb for comprehensive examples including:

Basic modules with single predictors
Multi-step pipelines that chain modules
Modules with conditional logic
Error handling and retry patterns
Stateful modules with history
Caching implementations

Module composition: Chain modules together to create complex workflows:

ruby

class Pipeline < DSPy::Module
  def initialize
    super
    @step1 = Classifier.new
    @step2 = Analyzer.new
    @step3 = Responder.new
  end
 
  def forward(input)
    result1 = @step1.forward(input)
    result2 = @step2.forward(result1)
    @step3.forward(result2)
  end
end

Full documentation: See references/core-concepts.md sections on Modules and Module Composition.

3. Multiple Predictor Types

Choose the right predictor for your task:

Predict: Basic LLM inference with type-safe inputs/outputs

ruby

predictor = DSPy::Predict.new(TaskSignature)
result = predictor.forward(input: "data")

ChainOfThought: Adds automatic reasoning for improved accuracy

ruby

predictor = DSPy::ChainOfThought.new(TaskSignature)
result = predictor.forward(input: "data")
# Returns: { reasoning: "...", output: "..." }

ReAct: Tool-using agents with iterative reasoning

ruby

predictor = DSPy::ReAct.new(
  TaskSignature,
  tools: [SearchTool.new, CalculatorTool.new],
  max_iterations: 5
)

CodeAct: Dynamic code generation (requires dspy-code_act gem)

ruby

predictor = DSPy::CodeAct.new(TaskSignature)
result = predictor.forward(task: "Calculate factorial of 5")

When to use each:

Predict: Simple tasks, classification, extraction
ChainOfThought: Complex reasoning, analysis, multi-step thinking
ReAct: Tasks requiring external tools (search, calculation, API calls)
CodeAct: Tasks best solved with generated code

Full documentation: See references/core-concepts.md section on Predictors.

4. LLM Provider Configuration

Support for OpenAI, Anthropic Claude, Google Gemini, Ollama, and OpenRouter.

Quick configuration examples:

ruby

# OpenAI
DSPy.configure do |c|
  c.lm = DSPy::LM.new('openai/gpt-4o-mini',
    api_key: ENV['OPENAI_API_KEY'])
end
 
# Anthropic Claude
DSPy.configure do |c|
  c.lm = DSPy::LM.new('anthropic/claude-3-5-sonnet-20241022',
    api_key: ENV['ANTHROPIC_API_KEY'])
end
 
# Google Gemini
DSPy.configure do |c|
  c.lm = DSPy::LM.new('gemini/gemini-1.5-pro',
    api_key: ENV['GOOGLE_API_KEY'])
end
 
# Local Ollama (free, private)
DSPy.configure do |c|
  c.lm = DSPy::LM.new('ollama/llama3.1')
end

Templates: See assets/config-template.rb for comprehensive examples including:

Environment-based configuration
Multi-model setups for different tasks
Configuration with observability (OpenTelemetry, Langfuse)
Retry logic and fallback strategies
Budget tracking
Rails initializer patterns

Provider compatibility matrix:

Feature	OpenAI	Anthropic	Gemini	Ollama
Structured Output	✅	✅	✅	✅
Vision (Images)	✅	✅	✅	⚠️ Limited
Image URLs	✅	❌	❌	❌
Tool Calling	✅	✅	✅	Varies

Cost optimization strategy:

Development: Ollama (free) or gpt-4o-mini (cheap)
Testing: gpt-4o-mini with temperature=0.0
Production simple tasks: gpt-4o-mini, claude-3-haiku, gemini-1.5-flash
Production complex tasks: gpt-4o, claude-3-5-sonnet, gemini-1.5-pro

Full documentation: See references/providers.md for all configuration options, provider-specific features, and troubleshooting.

5. Multimodal & Vision Support

Process images alongside text using the unified DSPy::Image interface.

Quick reference:

ruby

class VisionSignature < DSPy::Signature
  description "Analyze image and answer questions"
 
  input do
    const :image, DSPy::Image
    const :question, String
  end
 
  output do
    const :answer, String
  end
end
 
predictor = DSPy::Predict.new(VisionSignature)
result = predictor.forward(
  image: DSPy::Image.from_file("path/to/image.jpg"),
  question: "What objects are visible?"
)

Image loading methods:

ruby

# From file
DSPy::Image.from_file("path/to/image.jpg")
 
# From URL (OpenAI only)
DSPy::Image.from_url("https://example.com/image.jpg")
 
# From base64
DSPy::Image.from_base64(base64_data, mime_type: "image/jpeg")

Provider support:

OpenAI: Full support including URLs
Anthropic, Gemini: Base64 or file loading only
Ollama: Limited multimodal depending on model

Full documentation: See references/core-concepts.md section on Multimodal Support.

6. Testing LLM Applications

Write standard RSpec tests for LLM logic.

Quick reference:

ruby

RSpec.describe EmailClassifier do
  before do
    DSPy.configure do |c|
      c.lm = DSPy::LM.new('openai/gpt-4o-mini',
        api_key: ENV['OPENAI_API_KEY'])
    end
  end
 
  it 'classifies technical emails correctly' do
    classifier = EmailClassifier.new
    result = classifier.forward(
      email_subject: "Can't log in",
      email_body: "Unable to access account"
    )
 
    expect(result[:category]).to eq('Technical')
    expect(result[:priority]).to be_in(['High', 'Medium', 'Low'])
  end
end

Testing patterns:

Mock LLM responses for unit tests
Use VCR for deterministic API testing
Test type safety and validation
Test edge cases (empty inputs, special characters, long texts)
Integration test complete workflows

Full documentation: See references/optimization.md section on Testing.

7. Optimization & Improvement

Automatically improve prompts and modules using optimization techniques.

MIPROv2 optimization:

ruby

require 'dspy/mipro'
 
# Define evaluation metric
def accuracy_metric(example, prediction)
  example[:expected_output][:category] == prediction[:category] ? 1.0 : 0.0
end
 
# Prepare training data
training_examples = [
  {
    input: { email_subject: "...", email_body: "..." },
    expected_output: { category: 'Technical' }
  },
  # More examples...
]
 
# Run optimization
optimizer = DSPy::MIPROv2.new(
  metric: method(:accuracy_metric),
  num_candidates: 10
)
 
optimized_module = optimizer.compile(
  EmailClassifier.new,
  trainset: training_examples
)

A/B testing different approaches:

ruby

# Test ChainOfThought vs ReAct
approach_a_score = evaluate_approach(ChainOfThoughtModule, test_set)
approach_b_score = evaluate_approach(ReActModule, test_set)

Full documentation: See references/optimization.md section on Optimization.

8. Observability & Monitoring

Track performance, token usage, and behavior in production.

OpenTelemetry integration:

ruby

require 'opentelemetry/sdk'
 
OpenTelemetry::SDK.configure do |c|
  c.service_name = 'my-dspy-app'
  c.use_all
end
 
# DSPy automatically creates traces

Langfuse tracing:

ruby

DSPy.configure do |c|
  c.lm = DSPy::LM.new('openai/gpt-4o-mini',
    api_key: ENV['OPENAI_API_KEY'])
 
  c.langfuse = {
    public_key: ENV['LANGFUSE_PUBLIC_KEY'],
    secret_key: ENV['LANGFUSE_SECRET_KEY']
  }
end

Custom monitoring:

Token tracking
Performance monitoring
Error rate tracking
Custom logging

Full documentation: See references/optimization.md section on Observability.

Quick Start Workflow

For New Projects

Install DSPy.rb and provider gems:

bash

gem install dspy dspy-openai  # or dspy-anthropic, dspy-gemini

Configure LLM provider (see assets/config-template.rb):

ruby

require 'dspy'
 
DSPy.configure do |c|
  c.lm = DSPy::LM.new('openai/gpt-4o-mini',
    api_key: ENV['OPENAI_API_KEY'])
end

Create a signature (see assets/signature-template.rb):

ruby

class MySignature < DSPy::Signature
  description "Clear description of task"
 
  input do
    const :input_field, String, desc: "Description"
  end
 
  output do
    const :output_field, String, desc: "Description"
  end
end

Create a module (see assets/module-template.rb):

ruby

class MyModule < DSPy::Module
  def initialize
    super
    @predictor = DSPy::Predict.new(MySignature)
  end
 
  def forward(input_field:)
    @predictor.forward(input_field: input_field)
  end
end

Use the module:

ruby

module_instance = MyModule.new
result = module_instance.forward(input_field: "test")
puts result[:output_field]

Add tests (see references/optimization.md):

ruby

RSpec.describe MyModule do
  it 'produces expected output' do
    result = MyModule.new.forward(input_field: "test")
    expect(result[:output_field]).to be_a(String)
  end
end

For Rails Applications

Add to Gemfile:

ruby

gem 'dspy'
gem 'dspy-openai'  # or other provider

Create initializer at config/initializers/dspy.rb (see assets/config-template.rb for full example):

ruby

require 'dspy'
 
DSPy.configure do |c|
  c.lm = DSPy::LM.new('openai/gpt-4o-mini',
    api_key: ENV['OPENAI_API_KEY'])
end

Create modules in app/llm/ directory:

ruby

# app/llm/email_classifier.rb
class EmailClassifier < DSPy::Module
  # Implementation here
end

Use in controllers/services:

ruby

class EmailsController < ApplicationController
  def classify
    classifier = EmailClassifier.new
    result = classifier.forward(
      email_subject: params[:subject],
      email_body: params[:body]
    )
    render json: result
  end
end

Common Patterns

Pattern: Multi-Step Analysis Pipeline

ruby

class AnalysisPipeline < DSPy::Module
  def initialize
    super
    @extract = DSPy::Predict.new(ExtractSignature)
    @analyze = DSPy::ChainOfThought.new(AnalyzeSignature)
    @summarize = DSPy::Predict.new(SummarizeSignature)
  end
 
  def forward(text:)
    extracted = @extract.forward(text: text)
    analyzed = @analyze.forward(data: extracted[:data])
    @summarize.forward(analysis: analyzed[:result])
  end
end

Pattern: Agent with Tools

ruby

class ResearchAgent < DSPy::Module
  def initialize
    super
    @agent = DSPy::ReAct.new(
      ResearchSignature,
      tools: [
        WebSearchTool.new,
        DatabaseQueryTool.new,
        SummarizerTool.new
      ],
      max_iterations: 10
    )
  end
 
  def forward(question:)
    @agent.forward(question: question)
  end
end
 
class WebSearchTool < DSPy::Tool
  def call(query:)
    results = perform_search(query)
    { results: results }
  end
end

Pattern: Conditional Routing

ruby

class SmartRouter < DSPy::Module
  def initialize
    super
    @classifier = DSPy::Predict.new(ClassifySignature)
    @simple_handler = SimpleModule.new
    @complex_handler = ComplexModule.new
  end
 
  def forward(input:)
    classification = @classifier.forward(text: input)
 
    if classification[:complexity] == 'Simple'
      @simple_handler.forward(input: input)
    else
      @complex_handler.forward(input: input)
    end
  end
end

Pattern: Retry with Fallback

ruby

class RobustModule < DSPy::Module
  MAX_RETRIES = 3
 
  def forward(input, retry_count: 0)
    begin
      @predictor.forward(input)
    rescue DSPy::ValidationError => e
      if retry_count < MAX_RETRIES
        sleep(2 ** retry_count)
        forward(input, retry_count: retry_count + 1)
      else
        # Fallback to default or raise
        raise
      end
    end
  end
end

Resources

This skill includes comprehensive reference materials and templates:

References (load as needed for detailed information)

core-concepts.md: Complete guide to signatures, modules, predictors, multimodal support, and best practices
providers.md: All LLM provider configurations, compatibility matrix, cost optimization, and troubleshooting
optimization.md: Testing patterns, optimization techniques, observability setup, and monitoring

Assets (templates for quick starts)

signature-template.rb: Examples of signatures including basic, vision, sentiment analysis, and code generation
module-template.rb: Module patterns including pipelines, agents, error handling, caching, and state management
config-template.rb: Configuration examples for all providers, environments, observability, and production patterns

When to Use This Skill

Trigger this skill when:

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

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