[2.9.0] Rename plugin to compound-engineering

BREAKING: Plugin renamed from compounding-engineering to compound-engineering. Users will need to reinstall with the new name: claude /plugin install compound-engineering Changes: - Renamed plugin directory and all references - Updated documentation counts (24 agents, 19 commands) - Added julik-frontend-races-reviewer to docs 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-12-02 17:32:04 -08:00
parent 4b49e5344d
commit 6c5b3e40db
121 changed files with 136 additions and 117 deletions
--- a/plugins/compound-engineering/skills/dspy-ruby/assets/config-template.rb
+++ b/plugins/compound-engineering/skills/dspy-ruby/assets/config-template.rb
@@ -0,0 +1,359 @@
+# frozen_string_literal: true
+
+# DSPy.rb Configuration Examples
+# This file demonstrates various configuration patterns for different use cases
+
+require 'dspy'
+
+# ============================================================================
+# Basic Configuration
+# ============================================================================
+
+# Simple OpenAI configuration
+DSPy.configure do |c|
+  c.lm = DSPy::LM.new('openai/gpt-4o-mini',
+    api_key: ENV['OPENAI_API_KEY'])
+end
+
+# ============================================================================
+# Multi-Provider Configuration
+# ============================================================================
+
+# 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
+DSPy.configure do |c|
+  c.lm = DSPy::LM.new('ollama/llama3.1',
+    base_url: 'http://localhost:11434')
+end
+
+# OpenRouter (access to 200+ models)
+DSPy.configure do |c|
+  c.lm = DSPy::LM.new('openrouter/anthropic/claude-3.5-sonnet',
+    api_key: ENV['OPENROUTER_API_KEY'],
+    base_url: 'https://openrouter.ai/api/v1')
+end
+
+# ============================================================================
+# Environment-Based Configuration
+# ============================================================================
+
+# Different models for different environments
+if Rails.env.development?
+  # Use local Ollama for development (free, private)
+  DSPy.configure do |c|
+    c.lm = DSPy::LM.new('ollama/llama3.1')
+  end
+elsif Rails.env.test?
+  # Use cheap model for testing
+  DSPy.configure do |c|
+    c.lm = DSPy::LM.new('openai/gpt-4o-mini',
+      api_key: ENV['OPENAI_API_KEY'])
+  end
+else
+  # Use powerful model for production
+  DSPy.configure do |c|
+    c.lm = DSPy::LM.new('anthropic/claude-3-5-sonnet-20241022',
+      api_key: ENV['ANTHROPIC_API_KEY'])
+  end
+end
+
+# ============================================================================
+# Configuration with Custom Parameters
+# ============================================================================
+
+DSPy.configure do |c|
+  c.lm = DSPy::LM.new('openai/gpt-4o',
+    api_key: ENV['OPENAI_API_KEY'],
+    temperature: 0.7,        # Creativity (0.0-2.0, default: 1.0)
+    max_tokens: 2000,        # Maximum response length
+    top_p: 0.9,              # Nucleus sampling
+    frequency_penalty: 0.0,  # Reduce repetition (-2.0 to 2.0)
+    presence_penalty: 0.0    # Encourage new topics (-2.0 to 2.0)
+  )
+end
+
+# ============================================================================
+# Multiple Model Configuration (Task-Specific)
+# ============================================================================
+
+# Create different language models for different tasks
+module MyApp
+  # Fast model for simple tasks
+  FAST_LM = DSPy::LM.new('openai/gpt-4o-mini',
+    api_key: ENV['OPENAI_API_KEY'],
+    temperature: 0.3  # More deterministic
+  )
+
+  # Powerful model for complex tasks
+  POWERFUL_LM = DSPy::LM.new('anthropic/claude-3-5-sonnet-20241022',
+    api_key: ENV['ANTHROPIC_API_KEY'],
+    temperature: 0.7
+  )
+
+  # Creative model for content generation
+  CREATIVE_LM = DSPy::LM.new('openai/gpt-4o',
+    api_key: ENV['OPENAI_API_KEY'],
+    temperature: 1.2,  # More creative
+    top_p: 0.95
+  )
+
+  # Vision-capable model
+  VISION_LM = DSPy::LM.new('openai/gpt-4o',
+    api_key: ENV['OPENAI_API_KEY'])
+end
+
+# Use in modules
+class SimpleClassifier < DSPy::Module
+  def initialize
+    super
+    DSPy.configure { |c| c.lm = MyApp::FAST_LM }
+    @predictor = DSPy::Predict.new(SimpleSignature)
+  end
+end
+
+class ComplexAnalyzer < DSPy::Module
+  def initialize
+    super
+    DSPy.configure { |c| c.lm = MyApp::POWERFUL_LM }
+    @predictor = DSPy::ChainOfThought.new(ComplexSignature)
+  end
+end
+
+# ============================================================================
+# Configuration with Observability (OpenTelemetry)
+# ============================================================================
+
+require 'opentelemetry/sdk'
+
+# Configure OpenTelemetry
+OpenTelemetry::SDK.configure do |c|
+  c.service_name = 'my-dspy-app'
+  c.use_all
+end
+
+# Configure DSPy (automatically integrates with OpenTelemetry)
+DSPy.configure do |c|
+  c.lm = DSPy::LM.new('openai/gpt-4o-mini',
+    api_key: ENV['OPENAI_API_KEY'])
+end
+
+# ============================================================================
+# Configuration with Langfuse Tracing
+# ============================================================================
+
+require 'dspy/langfuse'
+
+DSPy.configure do |c|
+  c.lm = DSPy::LM.new('openai/gpt-4o-mini',
+    api_key: ENV['OPENAI_API_KEY'])
+
+  # Enable Langfuse tracing
+  c.langfuse = {
+    public_key: ENV['LANGFUSE_PUBLIC_KEY'],
+    secret_key: ENV['LANGFUSE_SECRET_KEY'],
+    host: ENV['LANGFUSE_HOST'] || 'https://cloud.langfuse.com'
+  }
+end
+
+# ============================================================================
+# Configuration with Retry Logic
+# ============================================================================
+
+class RetryableConfig
+  MAX_RETRIES = 3
+
+  def self.configure
+    DSPy.configure do |c|
+      c.lm = create_lm_with_retry
+    end
+  end
+
+  def self.create_lm_with_retry
+    lm = DSPy::LM.new('openai/gpt-4o-mini',
+      api_key: ENV['OPENAI_API_KEY'])
+
+    # Wrap with retry logic
+    lm.extend(RetryBehavior)
+    lm
+  end
+
+  module RetryBehavior
+    def forward(input, retry_count: 0)
+      super(input)
+    rescue RateLimitError, TimeoutError => e
+      if retry_count < MAX_RETRIES
+        sleep(2 ** retry_count)  # Exponential backoff
+        forward(input, retry_count: retry_count + 1)
+      else
+        raise
+      end
+    end
+  end
+end
+
+RetryableConfig.configure
+
+# ============================================================================
+# Configuration with Fallback Models
+# ============================================================================
+
+class FallbackConfig
+  def self.configure
+    DSPy.configure do |c|
+      c.lm = create_lm_with_fallback
+    end
+  end
+
+  def self.create_lm_with_fallback
+    primary = DSPy::LM.new('anthropic/claude-3-5-sonnet-20241022',
+      api_key: ENV['ANTHROPIC_API_KEY'])
+
+    fallback = DSPy::LM.new('openai/gpt-4o',
+      api_key: ENV['OPENAI_API_KEY'])
+
+    FallbackLM.new(primary, fallback)
+  end
+
+  class FallbackLM
+    def initialize(primary, fallback)
+      @primary = primary
+      @fallback = fallback
+    end
+
+    def forward(input)
+      @primary.forward(input)
+    rescue => e
+      puts "Primary model failed: #{e.message}. Falling back..."
+      @fallback.forward(input)
+    end
+  end
+end
+
+FallbackConfig.configure
+
+# ============================================================================
+# Configuration with Budget Tracking
+# ============================================================================
+
+class BudgetTrackedConfig
+  def self.configure(monthly_budget_usd:)
+    DSPy.configure do |c|
+      c.lm = BudgetTracker.new(
+        DSPy::LM.new('openai/gpt-4o',
+          api_key: ENV['OPENAI_API_KEY']),
+        monthly_budget_usd: monthly_budget_usd
+      )
+    end
+  end
+
+  class BudgetTracker
+    def initialize(lm, monthly_budget_usd:)
+      @lm = lm
+      @monthly_budget_usd = monthly_budget_usd
+      @monthly_cost = 0.0
+    end
+
+    def forward(input)
+      result = @lm.forward(input)
+
+      # Track cost (simplified - actual costs vary by model)
+      tokens = result.metadata[:usage][:total_tokens]
+      cost = estimate_cost(tokens)
+      @monthly_cost += cost
+
+      if @monthly_cost > @monthly_budget_usd
+        raise "Monthly budget of $#{@monthly_budget_usd} exceeded!"
+      end
+
+      result
+    end
+
+    private
+
+    def estimate_cost(tokens)
+      # Simplified cost estimation (check provider pricing)
+      (tokens / 1_000_000.0) * 5.0  # $5 per 1M tokens
+    end
+  end
+end
+
+BudgetTrackedConfig.configure(monthly_budget_usd: 100)
+
+# ============================================================================
+# Configuration Initializer for Rails
+# ============================================================================
+
+# Save this as config/initializers/dspy.rb
+#
+# require 'dspy'
+#
+# DSPy.configure do |c|
+#   # Environment-specific configuration
+#   model_config = case Rails.env.to_sym
+#   when :development
+#     { provider: 'ollama', model: 'llama3.1' }
+#   when :test
+#     { provider: 'openai', model: 'gpt-4o-mini', temperature: 0.0 }
+#   when :production
+#     { provider: 'anthropic', model: 'claude-3-5-sonnet-20241022' }
+#   end
+#
+#   # Configure language model
+#   c.lm = DSPy::LM.new(
+#     "#{model_config[:provider]}/#{model_config[:model]}",
+#     api_key: ENV["#{model_config[:provider].upcase}_API_KEY"],
+#     **model_config.except(:provider, :model)
+#   )
+#
+#   # Optional: Add observability
+#   if Rails.env.production?
+#     c.langfuse = {
+#       public_key: ENV['LANGFUSE_PUBLIC_KEY'],
+#       secret_key: ENV['LANGFUSE_SECRET_KEY']
+#     }
+#   end
+# end
+
+# ============================================================================
+# Testing Configuration
+# ============================================================================
+
+# In spec/spec_helper.rb or test/test_helper.rb
+#
+# RSpec.configure do |config|
+#   config.before(:suite) do
+#     DSPy.configure do |c|
+#       c.lm = DSPy::LM.new('openai/gpt-4o-mini',
+#         api_key: ENV['OPENAI_API_KEY'],
+#         temperature: 0.0  # Deterministic for testing
+#       )
+#     end
+#   end
+# end
+
+# ============================================================================
+# Configuration Best Practices
+# ============================================================================
+
+# 1. Use environment variables for API keys (never hardcode)
+# 2. Use different models for different environments
+# 3. Use cheaper/faster models for development and testing
+# 4. Configure temperature based on use case:
+#    - 0.0-0.3: Deterministic, factual tasks
+#    - 0.7-1.0: Balanced creativity
+#    - 1.0-2.0: High creativity, content generation
+# 5. Add observability in production (OpenTelemetry, Langfuse)
+# 6. Implement retry logic and fallbacks for reliability
+# 7. Track costs and set budgets for production
+# 8. Use max_tokens to control response length and costs
--- a/plugins/compound-engineering/skills/dspy-ruby/assets/module-template.rb
+++ b/plugins/compound-engineering/skills/dspy-ruby/assets/module-template.rb
@@ -0,0 +1,326 @@
+# frozen_string_literal: true
+
+# Example DSPy Module Template
+# This template demonstrates best practices for creating composable modules
+
+# Basic module with single predictor
+class BasicModule < DSPy::Module
+  def initialize
+    super
+    # Initialize predictor with signature
+    @predictor = DSPy::Predict.new(ExampleSignature)
+  end
+
+  def forward(input_hash)
+    # Forward pass through the predictor
+    @predictor.forward(input_hash)
+  end
+end
+
+# Module with Chain of Thought reasoning
+class ChainOfThoughtModule < DSPy::Module
+  def initialize
+    super
+    # ChainOfThought automatically adds reasoning to output
+    @predictor = DSPy::ChainOfThought.new(EmailClassificationSignature)
+  end
+
+  def forward(email_subject:, email_body:)
+    result = @predictor.forward(
+      email_subject: email_subject,
+      email_body: email_body
+    )
+
+    # Result includes :reasoning field automatically
+    {
+      category: result[:category],
+      priority: result[:priority],
+      reasoning: result[:reasoning],
+      confidence: calculate_confidence(result)
+    }
+  end
+
+  private
+
+  def calculate_confidence(result)
+    # Add custom logic to calculate confidence
+    # For example, based on reasoning length or specificity
+    result[:confidence] || 0.8
+  end
+end
+
+# Composable module that chains multiple steps
+class MultiStepPipeline < DSPy::Module
+  def initialize
+    super
+    # Initialize multiple predictors for different steps
+    @step1 = DSPy::Predict.new(Step1Signature)
+    @step2 = DSPy::ChainOfThought.new(Step2Signature)
+    @step3 = DSPy::Predict.new(Step3Signature)
+  end
+
+  def forward(input)
+    # Chain predictors together
+    result1 = @step1.forward(input)
+    result2 = @step2.forward(result1)
+    result3 = @step3.forward(result2)
+
+    # Combine results as needed
+    {
+      step1_output: result1,
+      step2_output: result2,
+      final_result: result3
+    }
+  end
+end
+
+# Module with conditional logic
+class ConditionalModule < DSPy::Module
+  def initialize
+    super
+    @simple_classifier = DSPy::Predict.new(SimpleClassificationSignature)
+    @complex_analyzer = DSPy::ChainOfThought.new(ComplexAnalysisSignature)
+  end
+
+  def forward(text:, complexity_threshold: 100)
+    # Use different predictors based on input characteristics
+    if text.length < complexity_threshold
+      @simple_classifier.forward(text: text)
+    else
+      @complex_analyzer.forward(text: text)
+    end
+  end
+end
+
+# Module with error handling and retry logic
+class RobustModule < DSPy::Module
+  MAX_RETRIES = 3
+
+  def initialize
+    super
+    @predictor = DSPy::Predict.new(RobustSignature)
+    @logger = Logger.new(STDOUT)
+  end
+
+  def forward(input, retry_count: 0)
+    @logger.info "Processing input: #{input.inspect}"
+
+    begin
+      result = @predictor.forward(input)
+      validate_result!(result)
+      result
+    rescue DSPy::ValidationError => e
+      @logger.error "Validation error: #{e.message}"
+
+      if retry_count < MAX_RETRIES
+        @logger.info "Retrying (#{retry_count + 1}/#{MAX_RETRIES})..."
+        sleep(2 ** retry_count) # Exponential backoff
+        forward(input, retry_count: retry_count + 1)
+      else
+        @logger.error "Max retries exceeded"
+        raise
+      end
+    end
+  end
+
+  private
+
+  def validate_result!(result)
+    # Add custom validation logic
+    raise DSPy::ValidationError, "Invalid result" unless result[:category]
+    raise DSPy::ValidationError, "Low confidence" if result[:confidence] && result[:confidence] < 0.5
+  end
+end
+
+# Module with ReAct agent and tools
+class AgentModule < DSPy::Module
+  def initialize
+    super
+
+    # Define tools for the agent
+    tools = [
+      SearchTool.new,
+      CalculatorTool.new,
+      DatabaseQueryTool.new
+    ]
+
+    # ReAct provides iterative reasoning and tool usage
+    @agent = DSPy::ReAct.new(
+      AgentSignature,
+      tools: tools,
+      max_iterations: 5
+    )
+  end
+
+  def forward(task:)
+    # Agent will autonomously use tools to complete the task
+    @agent.forward(task: task)
+  end
+end
+
+# Tool definition example
+class SearchTool < DSPy::Tool
+  def call(query:)
+    # Implement search functionality
+    results = perform_search(query)
+    { results: results }
+  end
+
+  private
+
+  def perform_search(query)
+    # Actual search implementation
+    # Could call external API, database, etc.
+    ["result1", "result2", "result3"]
+  end
+end
+
+# Module with state management
+class StatefulModule < DSPy::Module
+  attr_reader :history
+
+  def initialize
+    super
+    @predictor = DSPy::ChainOfThought.new(StatefulSignature)
+    @history = []
+  end
+
+  def forward(input)
+    # Process with context from history
+    context = build_context_from_history
+    result = @predictor.forward(
+      input: input,
+      context: context
+    )
+
+    # Store in history
+    @history << {
+      input: input,
+      result: result,
+      timestamp: Time.now
+    }
+
+    result
+  end
+
+  def reset!
+    @history.clear
+  end
+
+  private
+
+  def build_context_from_history
+    @history.last(5).map { |h| h[:result][:summary] }.join("\n")
+  end
+end
+
+# Module that uses different LLMs for different tasks
+class MultiModelModule < DSPy::Module
+  def initialize
+    super
+
+    # Fast, cheap model for simple classification
+    @fast_predictor = create_predictor(
+      'openai/gpt-4o-mini',
+      SimpleClassificationSignature
+    )
+
+    # Powerful model for complex analysis
+    @powerful_predictor = create_predictor(
+      'anthropic/claude-3-5-sonnet-20241022',
+      ComplexAnalysisSignature
+    )
+  end
+
+  def forward(input, use_complex: false)
+    if use_complex
+      @powerful_predictor.forward(input)
+    else
+      @fast_predictor.forward(input)
+    end
+  end
+
+  private
+
+  def create_predictor(model, signature)
+    lm = DSPy::LM.new(model, api_key: ENV["#{model.split('/').first.upcase}_API_KEY"])
+    DSPy::Predict.new(signature, lm: lm)
+  end
+end
+
+# Module with caching
+class CachedModule < DSPy::Module
+  def initialize
+    super
+    @predictor = DSPy::Predict.new(CachedSignature)
+    @cache = {}
+  end
+
+  def forward(input)
+    # Create cache key from input
+    cache_key = create_cache_key(input)
+
+    # Return cached result if available
+    if @cache.key?(cache_key)
+      puts "Cache hit for #{cache_key}"
+      return @cache[cache_key]
+    end
+
+    # Compute and cache result
+    result = @predictor.forward(input)
+    @cache[cache_key] = result
+    result
+  end
+
+  def clear_cache!
+    @cache.clear
+  end
+
+  private
+
+  def create_cache_key(input)
+    # Create deterministic hash from input
+    Digest::MD5.hexdigest(input.to_s)
+  end
+end
+
+# Usage Examples:
+#
+# Basic usage:
+#   module = BasicModule.new
+#   result = module.forward(field_name: "value")
+#
+# Chain of Thought:
+#   module = ChainOfThoughtModule.new
+#   result = module.forward(
+#     email_subject: "Can't log in",
+#     email_body: "I'm unable to access my account"
+#   )
+#   puts result[:reasoning]
+#
+# Multi-step pipeline:
+#   pipeline = MultiStepPipeline.new
+#   result = pipeline.forward(input_data)
+#
+# With error handling:
+#   module = RobustModule.new
+#   begin
+#     result = module.forward(input_data)
+#   rescue DSPy::ValidationError => e
+#     puts "Failed after retries: #{e.message}"
+#   end
+#
+# Agent with tools:
+#   agent = AgentModule.new
+#   result = agent.forward(task: "Find the population of Tokyo")
+#
+# Stateful processing:
+#   module = StatefulModule.new
+#   result1 = module.forward("First input")
+#   result2 = module.forward("Second input")  # Has context from first
+#   module.reset!  # Clear history
+#
+# With caching:
+#   module = CachedModule.new
+#   result1 = module.forward(input)  # Computes result
+#   result2 = module.forward(input)  # Returns cached result
--- a/plugins/compound-engineering/skills/dspy-ruby/assets/signature-template.rb
+++ b/plugins/compound-engineering/skills/dspy-ruby/assets/signature-template.rb
@@ -0,0 +1,143 @@
+# frozen_string_literal: true
+
+# Example DSPy Signature Template
+# This template demonstrates best practices for creating type-safe signatures
+
+class ExampleSignature < DSPy::Signature
+  # Clear, specific description of what this signature does
+  # Good: "Classify customer support emails into Technical, Billing, or General categories"
+  # Avoid: "Classify emails"
+  description "Describe what this signature accomplishes and what output it produces"
+
+  # Input fields: Define what data the LLM receives
+  input do
+    # Basic field with description
+    const :field_name, String, desc: "Clear description of this input field"
+
+    # Numeric fields
+    const :count, Integer, desc: "Number of items to process"
+    const :score, Float, desc: "Confidence score between 0.0 and 1.0"
+
+    # Boolean fields
+    const :is_active, T::Boolean, desc: "Whether the item is currently active"
+
+    # Array fields
+    const :tags, T::Array[String], desc: "List of tags associated with the item"
+
+    # Optional: Enum for constrained values
+    const :priority, T.enum(["Low", "Medium", "High"]), desc: "Priority level"
+  end
+
+  # Output fields: Define what data the LLM produces
+  output do
+    # Primary output
+    const :result, String, desc: "The main result of the operation"
+
+    # Classification result with enum
+    const :category, T.enum(["Technical", "Billing", "General"]),
+      desc: "Category classification - must be one of: Technical, Billing, General"
+
+    # Confidence/metadata
+    const :confidence, Float, desc: "Confidence score (0.0-1.0) for this classification"
+
+    # Optional reasoning (automatically added by ChainOfThought)
+    # const :reasoning, String, desc: "Step-by-step reasoning for the classification"
+  end
+end
+
+# Example with multimodal input (vision)
+class VisionExampleSignature < DSPy::Signature
+  description "Analyze an image and answer questions about its content"
+
+  input do
+    const :image, DSPy::Image, desc: "The image to analyze"
+    const :question, String, desc: "Question about the image content"
+  end
+
+  output do
+    const :answer, String, desc: "Detailed answer to the question about the image"
+    const :confidence, Float, desc: "Confidence in the answer (0.0-1.0)"
+  end
+end
+
+# Example for complex analysis task
+class SentimentAnalysisSignature < DSPy::Signature
+  description "Analyze the sentiment of text with nuanced emotion detection"
+
+  input do
+    const :text, String, desc: "The text to analyze for sentiment"
+    const :context, String, desc: "Additional context about the text source or situation"
+  end
+
+  output do
+    const :sentiment, T.enum(["Positive", "Negative", "Neutral", "Mixed"]),
+      desc: "Overall sentiment - must be Positive, Negative, Neutral, or Mixed"
+
+    const :emotions, T::Array[String],
+      desc: "List of specific emotions detected (e.g., joy, anger, sadness, fear)"
+
+    const :intensity, T.enum(["Low", "Medium", "High"]),
+      desc: "Intensity of the detected sentiment"
+
+    const :confidence, Float,
+      desc: "Confidence in the sentiment classification (0.0-1.0)"
+  end
+end
+
+# Example for code generation task
+class CodeGenerationSignature < DSPy::Signature
+  description "Generate Ruby code based on natural language requirements"
+
+  input do
+    const :requirements, String,
+      desc: "Natural language description of what the code should do"
+
+    const :constraints, String,
+      desc: "Any specific requirements or constraints (e.g., libraries to use, style preferences)"
+  end
+
+  output do
+    const :code, String,
+      desc: "Complete, working Ruby code that fulfills the requirements"
+
+    const :explanation, String,
+      desc: "Brief explanation of how the code works and any important design decisions"
+
+    const :dependencies, T::Array[String],
+      desc: "List of required gems or dependencies"
+  end
+end
+
+# Usage Examples:
+#
+# Basic usage with Predict:
+#   predictor = DSPy::Predict.new(ExampleSignature)
+#   result = predictor.forward(
+#     field_name: "example value",
+#     count: 5,
+#     score: 0.85,
+#     is_active: true,
+#     tags: ["tag1", "tag2"],
+#     priority: "High"
+#   )
+#   puts result[:result]
+#   puts result[:category]
+#   puts result[:confidence]
+#
+# With Chain of Thought reasoning:
+#   predictor = DSPy::ChainOfThought.new(SentimentAnalysisSignature)
+#   result = predictor.forward(
+#     text: "I absolutely love this product! It exceeded all my expectations.",
+#     context: "Product review on e-commerce site"
+#   )
+#   puts result[:reasoning]  # See the LLM's step-by-step thinking
+#   puts result[:sentiment]
+#   puts result[:emotions]
+#
+# With Vision:
+#   predictor = DSPy::Predict.new(VisionExampleSignature)
+#   result = predictor.forward(
+#     image: DSPy::Image.from_file("path/to/image.jpg"),
+#     question: "What objects are visible in this image?"
+#   )
+#   puts result[:answer]