refactor(skills): update dspy-ruby skill to DSPy.rb v0.34.3 API (#162)
Rewrite all reference files, asset templates, and SKILL.md to use current API patterns (.call(), result.field, T::Enum classes, Tools::Base). Add two new reference files (toolsets, observability) covering tools DSL, event system, and Langfuse integration. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Vicente Reig Rincón de Arellano
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@@ -1,359 +1,187 @@
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# frozen_string_literal: true
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# DSPy.rb Configuration Examples
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# This file demonstrates various configuration patterns for different use cases
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require 'dspy'
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# ============================================================================
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# Basic Configuration
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# ============================================================================
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# Simple OpenAI configuration
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DSPy.configure do |c|
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c.lm = DSPy::LM.new('openai/gpt-4o-mini',
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api_key: ENV['OPENAI_API_KEY'])
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end
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# ============================================================================
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# Multi-Provider Configuration
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# ============================================================================
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# Anthropic Claude
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DSPy.configure do |c|
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c.lm = DSPy::LM.new('anthropic/claude-3-5-sonnet-20241022',
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api_key: ENV['ANTHROPIC_API_KEY'])
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end
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# Google Gemini
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DSPy.configure do |c|
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c.lm = DSPy::LM.new('gemini/gemini-1.5-pro',
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api_key: ENV['GOOGLE_API_KEY'])
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end
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# Local Ollama
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DSPy.configure do |c|
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c.lm = DSPy::LM.new('ollama/llama3.1',
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base_url: 'http://localhost:11434')
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end
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# OpenRouter (access to 200+ models)
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DSPy.configure do |c|
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c.lm = DSPy::LM.new('openrouter/anthropic/claude-3.5-sonnet',
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api_key: ENV['OPENROUTER_API_KEY'],
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base_url: 'https://openrouter.ai/api/v1')
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end
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# ============================================================================
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# Environment-Based Configuration
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# ============================================================================
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# Different models for different environments
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if Rails.env.development?
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# Use local Ollama for development (free, private)
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DSPy.configure do |c|
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c.lm = DSPy::LM.new('ollama/llama3.1')
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end
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elsif Rails.env.test?
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# Use cheap model for testing
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DSPy.configure do |c|
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c.lm = DSPy::LM.new('openai/gpt-4o-mini',
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api_key: ENV['OPENAI_API_KEY'])
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end
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else
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# Use powerful model for production
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DSPy.configure do |c|
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c.lm = DSPy::LM.new('anthropic/claude-3-5-sonnet-20241022',
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api_key: ENV['ANTHROPIC_API_KEY'])
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end
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end
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# ============================================================================
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# Configuration with Custom Parameters
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# ============================================================================
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DSPy.configure do |c|
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c.lm = DSPy::LM.new('openai/gpt-4o',
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api_key: ENV['OPENAI_API_KEY'],
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temperature: 0.7, # Creativity (0.0-2.0, default: 1.0)
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max_tokens: 2000, # Maximum response length
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top_p: 0.9, # Nucleus sampling
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frequency_penalty: 0.0, # Reduce repetition (-2.0 to 2.0)
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presence_penalty: 0.0 # Encourage new topics (-2.0 to 2.0)
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)
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end
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# ============================================================================
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# Multiple Model Configuration (Task-Specific)
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# ============================================================================
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# Create different language models for different tasks
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module MyApp
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# Fast model for simple tasks
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FAST_LM = DSPy::LM.new('openai/gpt-4o-mini',
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api_key: ENV['OPENAI_API_KEY'],
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temperature: 0.3 # More deterministic
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)
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# Powerful model for complex tasks
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POWERFUL_LM = DSPy::LM.new('anthropic/claude-3-5-sonnet-20241022',
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api_key: ENV['ANTHROPIC_API_KEY'],
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temperature: 0.7
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)
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# Creative model for content generation
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CREATIVE_LM = DSPy::LM.new('openai/gpt-4o',
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api_key: ENV['OPENAI_API_KEY'],
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temperature: 1.2, # More creative
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top_p: 0.95
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)
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# Vision-capable model
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VISION_LM = DSPy::LM.new('openai/gpt-4o',
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api_key: ENV['OPENAI_API_KEY'])
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end
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# Use in modules
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class SimpleClassifier < DSPy::Module
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def initialize
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super
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DSPy.configure { |c| c.lm = MyApp::FAST_LM }
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@predictor = DSPy::Predict.new(SimpleSignature)
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end
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end
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class ComplexAnalyzer < DSPy::Module
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def initialize
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super
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DSPy.configure { |c| c.lm = MyApp::POWERFUL_LM }
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@predictor = DSPy::ChainOfThought.new(ComplexSignature)
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end
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end
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# ============================================================================
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# Configuration with Observability (OpenTelemetry)
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# ============================================================================
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require 'opentelemetry/sdk'
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# Configure OpenTelemetry
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OpenTelemetry::SDK.configure do |c|
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c.service_name = 'my-dspy-app'
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c.use_all
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end
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# Configure DSPy (automatically integrates with OpenTelemetry)
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DSPy.configure do |c|
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c.lm = DSPy::LM.new('openai/gpt-4o-mini',
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api_key: ENV['OPENAI_API_KEY'])
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end
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# ============================================================================
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# Configuration with Langfuse Tracing
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# ============================================================================
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require 'dspy/langfuse'
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DSPy.configure do |c|
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c.lm = DSPy::LM.new('openai/gpt-4o-mini',
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api_key: ENV['OPENAI_API_KEY'])
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# Enable Langfuse tracing
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c.langfuse = {
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public_key: ENV['LANGFUSE_PUBLIC_KEY'],
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secret_key: ENV['LANGFUSE_SECRET_KEY'],
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host: ENV['LANGFUSE_HOST'] || 'https://cloud.langfuse.com'
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}
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end
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# ============================================================================
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# Configuration with Retry Logic
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# ============================================================================
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class RetryableConfig
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MAX_RETRIES = 3
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def self.configure
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DSPy.configure do |c|
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c.lm = create_lm_with_retry
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end
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end
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def self.create_lm_with_retry
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lm = DSPy::LM.new('openai/gpt-4o-mini',
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api_key: ENV['OPENAI_API_KEY'])
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# Wrap with retry logic
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lm.extend(RetryBehavior)
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lm
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end
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module RetryBehavior
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def forward(input, retry_count: 0)
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super(input)
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rescue RateLimitError, TimeoutError => e
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if retry_count < MAX_RETRIES
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sleep(2 ** retry_count) # Exponential backoff
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forward(input, retry_count: retry_count + 1)
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else
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raise
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end
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end
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end
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end
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RetryableConfig.configure
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# ============================================================================
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# Configuration with Fallback Models
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# ============================================================================
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class FallbackConfig
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def self.configure
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DSPy.configure do |c|
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c.lm = create_lm_with_fallback
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end
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end
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def self.create_lm_with_fallback
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primary = DSPy::LM.new('anthropic/claude-3-5-sonnet-20241022',
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api_key: ENV['ANTHROPIC_API_KEY'])
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fallback = DSPy::LM.new('openai/gpt-4o',
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api_key: ENV['OPENAI_API_KEY'])
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FallbackLM.new(primary, fallback)
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end
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class FallbackLM
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def initialize(primary, fallback)
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@primary = primary
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@fallback = fallback
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end
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def forward(input)
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@primary.forward(input)
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rescue => e
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puts "Primary model failed: #{e.message}. Falling back..."
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@fallback.forward(input)
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end
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end
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end
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FallbackConfig.configure
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# ============================================================================
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# Configuration with Budget Tracking
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# ============================================================================
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class BudgetTrackedConfig
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def self.configure(monthly_budget_usd:)
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DSPy.configure do |c|
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c.lm = BudgetTracker.new(
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DSPy::LM.new('openai/gpt-4o',
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api_key: ENV['OPENAI_API_KEY']),
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monthly_budget_usd: monthly_budget_usd
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)
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end
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end
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class BudgetTracker
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def initialize(lm, monthly_budget_usd:)
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@lm = lm
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@monthly_budget_usd = monthly_budget_usd
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@monthly_cost = 0.0
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end
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def forward(input)
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result = @lm.forward(input)
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# Track cost (simplified - actual costs vary by model)
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tokens = result.metadata[:usage][:total_tokens]
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cost = estimate_cost(tokens)
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@monthly_cost += cost
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if @monthly_cost > @monthly_budget_usd
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raise "Monthly budget of $#{@monthly_budget_usd} exceeded!"
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end
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result
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end
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private
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def estimate_cost(tokens)
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# Simplified cost estimation (check provider pricing)
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(tokens / 1_000_000.0) * 5.0 # $5 per 1M tokens
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end
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end
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end
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BudgetTrackedConfig.configure(monthly_budget_usd: 100)
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# ============================================================================
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# Configuration Initializer for Rails
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# ============================================================================
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# Save this as config/initializers/dspy.rb
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# =============================================================================
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# DSPy.rb Configuration Template — v0.34.3 API
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#
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# require 'dspy'
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# Rails initializer patterns for DSPy.rb with RubyLLM, observability,
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# and feature-flagged model selection.
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#
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# DSPy.configure do |c|
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# # Environment-specific configuration
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# model_config = case Rails.env.to_sym
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# when :development
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# { provider: 'ollama', model: 'llama3.1' }
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# when :test
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# { provider: 'openai', model: 'gpt-4o-mini', temperature: 0.0 }
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# when :production
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# { provider: 'anthropic', model: 'claude-3-5-sonnet-20241022' }
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# end
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#
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# # Configure language model
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# c.lm = DSPy::LM.new(
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# "#{model_config[:provider]}/#{model_config[:model]}",
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# api_key: ENV["#{model_config[:provider].upcase}_API_KEY"],
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# **model_config.except(:provider, :model)
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# )
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#
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# # Optional: Add observability
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# if Rails.env.production?
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# c.langfuse = {
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# public_key: ENV['LANGFUSE_PUBLIC_KEY'],
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# secret_key: ENV['LANGFUSE_SECRET_KEY']
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# }
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# end
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# end
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# Key patterns:
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# - Use after_initialize for Rails setup
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# - Use dspy-ruby_llm for multi-provider routing
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# - Use structured_outputs: true for reliable parsing
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# - Use dspy-o11y + dspy-o11y-langfuse for observability
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# - Use ENV-based feature flags for model selection
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# =============================================================================
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# ============================================================================
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# Testing Configuration
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# ============================================================================
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# In spec/spec_helper.rb or test/test_helper.rb
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# =============================================================================
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# Gemfile Dependencies
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# =============================================================================
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#
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# RSpec.configure do |config|
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# config.before(:suite) do
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# DSPy.configure do |c|
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# c.lm = DSPy::LM.new('openai/gpt-4o-mini',
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# api_key: ENV['OPENAI_API_KEY'],
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# temperature: 0.0 # Deterministic for testing
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# )
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# # Core
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# gem 'dspy'
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#
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# # Provider adapter (choose one strategy):
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#
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# # Strategy A: Unified adapter via RubyLLM (recommended)
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# gem 'dspy-ruby_llm'
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# gem 'ruby_llm'
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#
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# # Strategy B: Per-provider adapters (direct SDK access)
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# gem 'dspy-openai' # OpenAI, OpenRouter, Ollama
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# gem 'dspy-anthropic' # Claude
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# gem 'dspy-gemini' # Gemini
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#
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# # Observability (optional)
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# gem 'dspy-o11y'
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# gem 'dspy-o11y-langfuse'
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#
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# # Optimization (optional)
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# gem 'dspy-miprov2' # MIPROv2 optimizer
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# gem 'dspy-gepa' # GEPA optimizer
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#
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# # Schema formats (optional)
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# gem 'sorbet-baml' # BAML schema format (84% token reduction)
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# =============================================================================
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# Rails Initializer — config/initializers/dspy.rb
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# =============================================================================
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Rails.application.config.after_initialize do
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# Skip in test unless explicitly enabled
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next if Rails.env.test? && ENV["DSPY_ENABLE_IN_TEST"].blank?
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# Configure RubyLLM provider credentials
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RubyLLM.configure do |config|
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config.gemini_api_key = ENV["GEMINI_API_KEY"] if ENV["GEMINI_API_KEY"].present?
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config.anthropic_api_key = ENV["ANTHROPIC_API_KEY"] if ENV["ANTHROPIC_API_KEY"].present?
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config.openai_api_key = ENV["OPENAI_API_KEY"] if ENV["OPENAI_API_KEY"].present?
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end
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# Configure DSPy with unified RubyLLM adapter
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model = ENV.fetch("DSPY_MODEL", "ruby_llm/gemini-2.5-flash")
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DSPy.configure do |config|
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config.lm = DSPy::LM.new(model, structured_outputs: true)
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config.logger = Rails.logger
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end
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# Enable Langfuse observability (optional)
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if ENV["LANGFUSE_PUBLIC_KEY"].present? && ENV["LANGFUSE_SECRET_KEY"].present?
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DSPy::Observability.configure!
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end
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end
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# =============================================================================
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# Feature Flags — config/initializers/feature_flags.rb
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# =============================================================================
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# Use different models for different roles:
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# - Fast/cheap for classification, routing, simple tasks
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# - Powerful for synthesis, reasoning, complex analysis
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module FeatureFlags
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SELECTOR_MODEL = ENV.fetch("DSPY_SELECTOR_MODEL", "ruby_llm/gemini-2.5-flash-lite")
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SYNTHESIZER_MODEL = ENV.fetch("DSPY_SYNTHESIZER_MODEL", "ruby_llm/gemini-2.5-flash")
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REASONING_MODEL = ENV.fetch("DSPY_REASONING_MODEL", "ruby_llm/claude-sonnet-4-20250514")
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end
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# Usage in tools/modules:
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#
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# class ClassifyTool < DSPy::Tools::Base
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# def call(query:)
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# predictor = DSPy::Predict.new(ClassifySignature)
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# predictor.configure { |c| c.lm = DSPy::LM.new(FeatureFlags::SELECTOR_MODEL, structured_outputs: true) }
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# predictor.call(query: query)
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# end
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# end
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# =============================================================================
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# Environment Variables — .env
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# =============================================================================
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#
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# # Provider API keys (set the ones you need)
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# GEMINI_API_KEY=...
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# ANTHROPIC_API_KEY=...
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# OPENAI_API_KEY=...
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#
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# # DSPy model configuration
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# DSPY_MODEL=ruby_llm/gemini-2.5-flash
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# DSPY_SELECTOR_MODEL=ruby_llm/gemini-2.5-flash-lite
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# DSPY_SYNTHESIZER_MODEL=ruby_llm/gemini-2.5-flash
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# DSPY_REASONING_MODEL=ruby_llm/claude-sonnet-4-20250514
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#
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# # Langfuse observability (optional)
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# LANGFUSE_PUBLIC_KEY=pk-...
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# LANGFUSE_SECRET_KEY=sk-...
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# DSPY_TELEMETRY_BATCH_SIZE=5
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#
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# # Test environment
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# DSPY_ENABLE_IN_TEST=1 # Set to enable DSPy in test env
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# =============================================================================
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# Per-Provider Configuration (without RubyLLM)
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# =============================================================================
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# OpenAI (dspy-openai gem)
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# DSPy.configure do |c|
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# c.lm = DSPy::LM.new('openai/gpt-4o-mini', api_key: ENV['OPENAI_API_KEY'])
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# end
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# ============================================================================
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# Configuration Best Practices
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# ============================================================================
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# Anthropic (dspy-anthropic gem)
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# DSPy.configure do |c|
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# c.lm = DSPy::LM.new('anthropic/claude-sonnet-4-20250514', api_key: ENV['ANTHROPIC_API_KEY'])
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# end
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# 1. Use environment variables for API keys (never hardcode)
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# 2. Use different models for different environments
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# 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
|
||||
# Gemini (dspy-gemini gem)
|
||||
# DSPy.configure do |c|
|
||||
# c.lm = DSPy::LM.new('gemini/gemini-2.5-flash', api_key: ENV['GEMINI_API_KEY'])
|
||||
# end
|
||||
|
||||
# Ollama (dspy-openai gem, local models)
|
||||
# DSPy.configure do |c|
|
||||
# c.lm = DSPy::LM.new('ollama/llama3.2', base_url: 'http://localhost:11434')
|
||||
# end
|
||||
|
||||
# OpenRouter (dspy-openai gem, 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
|
||||
|
||||
# =============================================================================
|
||||
# VCR Test Configuration — spec/support/dspy.rb
|
||||
# =============================================================================
|
||||
|
||||
# VCR.configure do |config|
|
||||
# config.cassette_library_dir = "spec/vcr_cassettes"
|
||||
# config.hook_into :webmock
|
||||
# config.configure_rspec_metadata!
|
||||
# config.filter_sensitive_data('<GEMINI_API_KEY>') { ENV['GEMINI_API_KEY'] }
|
||||
# config.filter_sensitive_data('<OPENAI_API_KEY>') { ENV['OPENAI_API_KEY'] }
|
||||
# config.filter_sensitive_data('<ANTHROPIC_API_KEY>') { ENV['ANTHROPIC_API_KEY'] }
|
||||
# end
|
||||
|
||||
# =============================================================================
|
||||
# Schema Format Configuration (optional)
|
||||
# =============================================================================
|
||||
|
||||
# BAML schema format — 84% token reduction for Enhanced Prompting mode
|
||||
# DSPy.configure do |c|
|
||||
# c.lm = DSPy::LM.new('openai/gpt-4o-mini',
|
||||
# api_key: ENV['OPENAI_API_KEY'],
|
||||
# schema_format: :baml # Requires sorbet-baml gem
|
||||
# )
|
||||
# end
|
||||
|
||||
# TOON schema + data format — table-oriented format
|
||||
# DSPy.configure do |c|
|
||||
# c.lm = DSPy::LM.new('openai/gpt-4o-mini',
|
||||
# api_key: ENV['OPENAI_API_KEY'],
|
||||
# schema_format: :toon, # How DSPy describes the signature
|
||||
# data_format: :toon # How inputs/outputs are rendered in prompts
|
||||
# )
|
||||
# end
|
||||
#
|
||||
# Note: BAML and TOON apply only when structured_outputs: false.
|
||||
# With structured_outputs: true, the provider receives JSON Schema directly.
|
||||
|
||||
Reference in New Issue
Block a user