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.

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:

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 [signature-template.rb](./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 [core-concepts.md](./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:

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 [module-template.rb](./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:

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 [core-concepts.md](./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

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

ChainOfThought: Adds automatic reasoning for improved accuracy

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

ReAct: Tool-using agents with iterative reasoning

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

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

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 [core-concepts.md](./references/core-concepts.md) section on Predictors.

4. LLM Provider Configuration

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

Quick configuration examples:

# 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-sonnet-5',
    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 [config-template.rb](./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-haiku-4-5, gemini-1.5-flash
• Production complex tasks: gpt-4o, claude-sonnet-5, gemini-1.5-pro

Full documentation: See [providers.md](./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:

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:

# 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 [core-concepts.md](./references/core-concepts.md) section on Multimodal Support.

6. Testing LLM Applications

Write standard RSpec tests for LLM logic.

Quick reference:

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 [optimization.md](./references/optimization.md) section on Testing.

7. Opt