dspy-ruby — Ruby фреймворк для разработки AI-приложений

--- name: dspy-ruby description: Build type-safe LLM applications with DSPy.rb — Ruby's programmatic prompt framework with signatures, modules, agents, and optimization. Use when implementing predictable AI features, creating LLM signatures and modules, configuring language model providers, building agent systems with tools, optimizing prompts, or testing LLM-powered functionality in Ruby applications. --- # DSPy.rb > Build LLM apps like you build software. Type-safe, modular, testable. DSPy.rb brings software engineering best practices to LLM development. Instead of tweaking prompts, define what you want with Ruby types and let DSPy handle the rest. ## Overview DSPy.rb is a Ruby framework for building language model applications with programmatic prompts. It provides: - **Type-safe signatures** — Define inputs/outputs with Sorbet types - **Modular components** — Compose and reuse LLM logic - **Automatic optimization** — Use data to improve prompts, not guesswork - **Production-ready** — Built-in observability, testing, and error handling ## Core Concepts ### 1. Signatures Define interfaces between your app and LLMs using Ruby types: ```ruby class EmailClassifier < DSPy::Signature description "Classify customer support emails by category and priority" class Priority < T::Enum enums do Low = new('low') Medium = new('medium') High = new('high') Urgent = new('urgent') end end input do const :email_content, String const :sender, String end output do const :category, String const :priority, Priority # Type-safe enum with defined values const :confidence, Float end end ``` ### 2. Modules Build complex workflows from simple building blocks: - **Predict** — Basic LLM calls with signatures - **ChainOfThought** — Step-by-step reasoning - **ReAct** — Tool-using agents - **CodeAct** — Dynamic code generation agents (install the `dspy-code_act` gem) ### 3. Tools & Toolsets Create type-safe tools for agents with comprehensive Sorbet support: ```ruby # Enum-based tool with automatic type conversion class CalculatorTool < DSPy::Tools::Base tool_name 'calculator' tool_description 'Performs arithmetic operations with type-safe enum inputs' class Operation < T::Enum enums do Add = new('add') Subtract = new('subtract') Multiply = new('multiply') Divide = new('divide') end end sig { params(operation: Operation, num1: Float, num2: Float).returns(T.any(Float, String)) } def call(operation:, num1:, num2:) case operation when Operation::Add then num1 + num2 when Operation::Subtract then num1 - num2 when Operation::Multiply then num1 * num2 when Operation::Divide return "Error: Division by zero" if num2 == 0 num1 / num2 end end end # Multi-tool toolset with rich types class DataToolset < DSPy::Tools::Toolset toolset_name "data_processing" class Format < T::Enum enums do JSON = new('json') CSV = new('csv') XML = new('xml') end end tool :convert, description: "Convert data between formats" tool :validate, description: "Validate data structure" sig { params(data: String, from: Format, to: Format).returns(String) } def convert(data:, from:, to:) "Converted from #{from.serialize} to #{to.serialize}" end sig { params(data: String, format: Format).returns(T::Hash[String, T.any(String, Integer, T::Boolean)]) } def validate(data:, format:) { valid: true, format: format.serialize, row_count: 42, message: "Data validation passed" } end end ``` ### 4. Type System & Discriminators DSPy.rb uses sophisticated type discrimination for complex data structures: - **Automatic `_type` field injection** — DSPy adds discriminator fields to structs for type safety - **Union type support** — `T.any()` types automatically disambiguated by `_type` - **Reserved field name** — Avoid defining your own `_type` fields in structs - **Recursive filtering** — `_type` fields filtered during deserialization at all nesting levels ### 5. Optimization Improve accuracy with real data: - **MIPROv2** — Advanced multi-prompt optimization with bootstrap sampling and Bayesian optimization - **GEPA** — Genetic-Pareto Reflective Prompt Evolution with feedback maps, experiment tracking, and telemetry - **Evaluation** — Comprehensive framework with built-in and custom metrics, error handling, and batch processing ## Quick Start ```ruby # Install gem 'dspy' # Configure DSPy.configure do |c| c.lm = DSPy::LM.new('openai/gpt-4o-mini', api_key: ENV['OPENAI_API_KEY']) end # Define a task class SentimentAnalysis < DSPy::Signature description "Analyze sentiment of text" input do const :text, String end output do const :sentiment, String # positive, negative, neutral const :score, Float # 0.0 to 1.0 end end # Use it analyzer = DSPy::Predict.new(SentimentAnalysis) result = analyzer.call(text: "This product is amazing!") puts result.sentiment # => "positive" puts result.score # => 0.92 ``` ## Provider Adapter Gems Two strategies for connecting to LLM providers: ### Per-provider adapters (direct SDK access) ```ruby # Gemfile gem 'dspy' gem 'dspy-openai' # OpenAI, OpenRouter, Ollama gem 'dspy-anthropic' # Claude gem 'dspy-gemini' # Gemini ``` Each adapter gem pulls in the official SDK (`openai`, `anthropic`, `gemini-ai`). ### Unified adapter via RubyLLM (recommended for multi-provider) ```ruby # Gemfile gem 'dspy' gem 'dspy-ruby_llm' # Routes to any provider via ruby_llm gem 'ruby_llm' ``` RubyLLM handles provider routing based on the model name. Use the `ruby_llm/` prefix: ```ruby DSPy.configure do |c| c.lm = DSPy::LM.new('ruby_llm/gemini-2.5-flash', structured_outputs: true) # c.lm = DSPy::LM.new('ruby_llm/claude-sonnet-4-20250514', structured_outputs: true) # c.lm = DSPy::LM.new('ruby_llm/gpt-4o-mini', structured_outputs: true) end ``` ## Events System DSPy.rb ships with a structured event bus for observing runtime behavior. ### Module-Scoped Subscriptions (preferred for agents) ```ruby class MyAgent < DSPy::Module subscribe 'lm.tokens', :track_tokens, scope: :descendants def track_tokens(_event, attrs) @total_tokens += attrs.fetch(:total_tokens, 0) end end ``` ### Global Subscriptions (for observability/integrations) ```ruby subscription_id = DSPy.events.subscribe('score.create') do |event, attrs| Langfuse.export_score(attrs) end # Wildcards supported DSPy.events.subscribe('llm.*') { |name, attrs| puts "[#{name}] tokens=#{attrs[:total_tokens]}" } ``` Event names use dot-separated namespaces (`llm.generate`, `react.iteration_complete`). Every event includes module metadata (`module_path`, `module_leaf`, `module_scope.ancestry_token`) for filtering. ## Lifecycle Callbacks Rails-style lifecycle hooks ship with every `DSPy::Module`: - **`before`** — Runs ahead of `forward` for setup (metrics, context loading) - **`around`** — Wraps `forward`, calls `yield`, and lets you pair setup/teardown logic - **`after`** — Fires after `forward` returns for cleanup or persistence ```ruby class InstrumentedModule < DSPy::Module before :setup_metrics around :manage_context after :log_metrics def forward(question:) @predictor.call(question: question) end private def setup_metrics @start_time = Time.now end def manage_context load_context result = yield save_context result end def log_metrics duration = Time.now - @start_time Rails.logger.info "Prediction completed in #{duration}s" end end ``` Execution order: before → around (before yield) → forward → around (after yield) → after. Callbacks are inherited from parent classes and execute in registration order. ## Fiber-Local LM Context Override the language model temporarily using fiber-local storage: ```ruby fast_model = DSPy::LM.new("openai/gpt-4o-mini", api_key: ENV['OPENAI_API_KEY']) DSPy.with_lm(fast_model) do result = classifier.call(text: "test") # Uses fast_model inside this block end # Back to global LM outside the block ``` **LM resolution hierarchy**: Instance-level LM → Fiber-local LM (`DSPy.with_lm`) → Global LM (`DSPy.configure`). Use `configure_predictor` for fine-grained control over agent internals: ```ruby agent = DSPy::ReAct.new(MySignature, tools: tools) agent.configure { |c| c.lm = default_model } agent.configure_predictor('thought_generator') { |c| c.lm = powerful_model } ``` ## Evaluation Framework Systematically test LLM application performance with `DSPy::Evals`: ```ruby metric = DSPy::Metrics.exact_match(field: :answer, case_sensitive: false) evaluator = DSPy::Evals.new(predictor, metric: metric) result = evaluator.evaluate(test_examples, display_table: true) puts "Pass Rate: #{(result.pass_rate * 100).round(1)}%" ``` Built-in metrics: `exact_match`, `contains`, `numeric_difference`, `composite_and`. Custom metrics return `true`/`false` or a `DSPy::Prediction` with `score:` and `feedback:` fields. Use `DSPy::Example` for typed test data and `export_scores: true` to push results to Langfuse. ## GEPA Optimization GEPA (Genetic-Pareto Reflective Prompt Evolution) uses reflection-driven instruction rewrites: ```ruby gem 'dspy-gepa' teleprompter = DSPy::Teleprompt::GEPA.new( metric: metric, reflection_lm: DSPy::ReflectionLM.new('openai/gpt-4o-mini', api_key: ENV['OPENAI_API_KEY']), feedback_map: feedback_map, config: { max_metric_calls: 600, minibatch_size: 6 } ) result = teleprompter.compile(program, trainset: train, valset: val) optimized_program = result.optimized_program ``` The metric must return `DSPy::Prediction.new(score:, feedback:)` so the reflection model can reason about failures. Use `feedback_map` to target individual predictors in composite modules. ## Typed Context Pattern Replace opaque string context blobs with `T::Struct` inputs. Each field gets its own `description:` annotation in the JSON schema the LLM sees: ```ruby class NavigationContext < T::Struct const :workflow_hint, T.nilable(String), description: "Current workflow phase guidance for the agent" const :action_log, T::Array[String], default: [], description: "Compact one-line-per-action history of research steps taken" const :iterations_remaining, Integer, description: "Budget remaining. Each tool call costs 1 iteration." end class ToolSelectionSignature < DSPy::Signature input do const :query, String const :context, NavigationContext # Structured, not an opaque string end output do const :tool_name, String const :tool_args, String, description: "JSON-encoded arguments" end end ``` Benefits: type safety at compile time, per-field descriptions in the LLM schema, easy to test as value objects, extensible by adding `const` declarations. ## Schema Formats (BAML / TOON) Control how DSPy describes signature structure to the LLM: - **JSON Schema** (default) — Standard format, works with `structured_outputs: true` - **BAML** (`schema_format: :baml`) — 84% token reduction for Enhanced Prompting mode. Requires `sorbet-baml` gem. - **TOON** (`schema_format: :toon, data_format: :toon`) — Table-oriented format for both schemas and data. Enhanced Prompting mode only. BAML and TOON apply only when `structured_outputs: false`. With `structured_outputs: true`, the provider receives JSON Schema directly. ## Storage System Persist and reload optimized programs with `DSPy::Storage::ProgramStorage`: ```ruby storage = DSPy::Storage::ProgramStorage.new(storage_path: "./dspy_storage") storage.save_program(result.optimized_program, result, metadata: { optimizer: 'MIPROv2' }) ``` Supports checkpoint management, optimization history tracking, and import/export between environments. ## Rails Integration ### Directory Structure Organize DSPy components using Rails conventions: ``` app/ entities/ # T::Struct types shared across signatures signatures/ # DSPy::Signature definitions tools/ # DSPy::Tools::Base implementations concerns/ # Shared tool behaviors (error handling, etc.) modules/ # DSPy::Module orchestrators services/ # Plain Ruby services that compose DSPy modules config/ initializers/ dspy.rb # DSPy + provider configuration feature_flags.rb # Model selection per role spec/ signatures/ # Schema validation tests tools/ # Tool unit tests modules/ # Integration tests with VCR vcr_cassettes/ # Recorded HTTP interactions ``` ### Initializer ```ruby # config/initializers/dspy.rb Rails.application.config.after_initialize do next if Rails.env.test? && ENV["DSPY_ENABLE_IN_TEST"].blank? RubyLLM.configure do |config| config.gemini_api_key = ENV["GEMINI_API_KEY"] if ENV["GEMINI_API_KEY"].present? config.anthropic_api_key = ENV["ANTHROPIC_API_KEY"] if ENV["ANTHROPIC_API_KEY"].present? config.openai_api_key = ENV["OPENAI_API_KEY"] if ENV["OPENAI_API_KEY"].present? end model = ENV.fetch("DSPY_MODEL", "ruby_llm/gemini-2.5-flash") DSPy.configure do |config| config.lm = DSPy::LM.new(model, structured_outputs: true) config.logger = Rails.logger end # Langfuse observability (optional) if ENV["LANGFUSE_PUBLIC_KEY"].present? && ENV["LANGFUSE_SECRET_KEY"].present? DSPy::Observability.configure! end end ``` ### Feature-Flagged Model Selection Use different models for different roles (fast/cheap for classification, powerful for synthesis): ```ruby # config/initializers/feature_flags.rb module FeatureFlags SELECTOR_MODEL = ENV.fetch("DSPY_SELECTOR_MODEL", "ruby_llm/gemini-2.5-flash-lite") SYNTHESIZER_MODEL = ENV.fetch("DSPY_SYNTHESIZER_MODEL", "ruby_llm/gemini-2.5-flash") end ``` Then override per-tool or per-predictor: ```ruby class ClassifyTool < DSPy::Tools::Base def call(query:) predictor = DSPy::Predict.new(ClassifyQuery) predictor.configure { |c| c.lm = DSPy::LM.new(FeatureFlags::SELECTOR_MODEL, structured_outputs: true) } predictor.call(query: query) end end ``` ## Schema-Driven Signatures **Prefer typed schemas over string descriptions.** Let the type system communicate structure to the LLM rather than prose in the signature description. ### Entities as Shared Types Define reusable `T::Struct` and `T::Enum` types in `app/entities/` and reference them across signatures: ```ruby # app/entities/search_strategy.rb class SearchStrategy < T::Enum enums do SingleSearch = new("single_search") DateDecomposition = new("date_decomposition") end end # app/entities/scored_item.rb class ScoredItem < T::Struct const :id, String const :score, Float, description: "Relevance score 0.0-1.0" const :verdict, String, description: "relevant, maybe, or irrelevant" const :reason, String, default: "" end ``` ### Schema vs Description: When to Use Each **Use schemas (T::Struct/T::Enum)** for: - Multi-field outputs with specific types - Enums with defined values the LLM must pick from - Nested structures, arrays of typed objects - Outputs consumed by code (not displayed to users) **Use string descriptions** for: - Simple single-field outputs where the type is `String` - Natural language generation (summaries, answers) - Fields where constraint guidance helps (e.g., `description: "YYYY-MM-DD format"`) **Rule of thumb**: If you'd write a `case` statement on the output, it should be a `T::Enum`. If you'd call `.each` on it, it should be `T::Array[SomeStruct]`. ## Tool Patterns ### Tools That Wrap Predictions A common pattern: tools encapsulate a DSPy prediction, adding error handling, model selection, and serialization: ```ruby class RerankTool < DSPy::Tools::Base tool_name "rerank" tool_description "Score and rank search results by relevance" MAX_ITEMS = 200 MIN_ITEMS_FOR_LLM = 5 sig { params(query: String, items: T::Array[T::Hash[Symbol, T.untyped]]).returns(T::Hash[Symbol, T.untyped]) } def call(query:, items: []) return { scored_items: items, reranked: false } if items.size < MIN_ITEMS_FOR_LLM capped_items = items.first(MAX_ITEMS) predictor = DSPy::Predict.new(RerankSignature) predictor.configure { |c| c.lm = DSPy::LM.new(FeatureFlags::SYNTHESIZER_MODEL, structured_outputs: true) } result = predictor.call(query: query, items: capped_items) { scored_items: result.scored_items, reranked: true } rescue => e Rails.logger.warn "[RerankTool] LLM rerank failed: #{e.message}" { error: "Rerank failed: #{e.message}", scored_items: items, reranked: false } end end ``` **Key patterns:** - Short-circuit LLM calls when unnecessary (small data, trivial cases) - Cap input size to prevent token overflow - Per-tool model selection via `configure` - Graceful error handling with fallback data ### Error Handling Concern ```ruby module ErrorHandling extend ActiveSupport::Concern private def safe_predict(signature_class, **inputs) predictor = DSPy::Predict.new(signature_class) yield predictor if block_given? predictor.call(**inputs) rescue Faraday::Error, Net::HTTPError => e Rails.logger.error "[#{self.class.name}] API error: #{e.message}" nil rescue JSON::ParserError => e Rails.logger.error "[#{self.class.name}] Invalid LLM output: #{e.message}" nil end end ``` ## Observability ### Tracing with DSPy::Context Wrap operations in spans for Langfuse/OpenTelemetry visibility: ```ruby result = DSPy::Context.with_span( operation: "tool_selector.select", "dspy.module" => "ToolSelector", "tool_selector.tools" => tool_names.join(",") ) do @predictor.call(query: query, context: context, available_tools: schemas) end ``` ### Setup for Langfuse ```ruby # Gemfile gem 'dspy-o11y' gem 'dspy-o11y-langfuse' # .env LANGFUSE_PUBLIC_KEY=pk-... LANGFUSE_SECRET_KEY=sk-... DSPY_TELEMETRY_BATCH_SIZE=5 ``` Every `DSPy::Predict`, `DSPy::ReAct`, and tool call is automatically traced when observability is configured. ### Score Reporting Report evaluation scores to Langfuse: ```ruby DSPy.score(name: "relevance", value: 0.85, trace_id: current_trace_id) ``` ## Testing ### VCR Setup for Rails ```ruby 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'] } end ``` ### Signature Schema Tests Test that signatures produce valid schemas without calling any LLM: ```ruby RSpec.describe ClassifyResearchQuery do it "has required input fields" do schema = described_class.input_json_schema expect(schema[:required]).to include("query") end it "has typed output fields" do schema = described_class.output_json_schema expect(schema[:properties]).to have_key(:search_strategy) end end ``` ### Tool Tests with Mocked Predictions ```ruby RSpec.describe RerankTool do let(:tool) { described_class.new } it "skips LLM for small result sets" do expect(DSPy::Predict).not_to receive(:new) result = tool.call(query: "test", items: [{ id: "1" }]) expect(result[:reranked]).to be false end it "calls LLM for large result sets", :vcr do items = 10.times.map { |i| { id: i.to_s, title: "Item #{i}" } } result = tool.call(query: "relevant items", items: items) expect(result[:reranked]).to be true end end ``` ## Resources - `references/core-concepts.md` — Signatures, modules, predictors, type system deep-dive - `references/toolsets.md` — Tools::Base, Tools::Toolset DSL, type safety, testing - `references/providers.md` — Provider adapters, RubyLLM, fiber-local LM context, compatibility matrix - `references/optimization.md` — MIPROv2, GEPA, evaluation framework, storage system - `references/observability.md` — Event system, dspy-o11y gems, Langfuse, score reporting - `assets/signature-template.rb` — Signature scaffold with T::Enum, Date/Time, defaults, union types - `assets/module-template.rb` — Module scaffold with .call(), lifecycle callbacks, fiber-local LM - `assets/config-template.rb` — Rails initializer with RubyLLM, observability, feature flags ## Key URLs - Homepage: https://oss.vicente.services/dspy.rb/ - GitHub: https://github.com/vicentereig/dspy.rb - Documentation: https://oss.vicente.services/dspy.rb/getting-started/ ## Guidelines for Claude When helping users with DSPy.rb: 1. **Schema over prose** — Define output structure with `T::Struct` and `T::Enum` types, not string descriptions 2. **Entities in `app/entities/`** — Extract shared types so signatures stay thin 3. **Per-tool model selection** — Use `predictor.configure { |c| c.lm = ... }` to pick the right model per task 4. **Short-circuit LLM calls** — Skip the LLM for trivial cases (small data, cached results) 5. **Cap input sizes** — Prevent token overflow by limiting array sizes before sending to LLM 6. **Test schemas without LLM** — Validate `input_json_schema` and `output_json_schema` in unit tests 7. **VCR for integration tests** — Record real HTTP interactions, never mock LLM responses by hand 8. **Trace with spans** — Wrap tool calls in `DSPy::Context.with_span` for observability 9. **Graceful degradation** — Always rescue LLM errors and return fallback data ### Signature Best Practices **Keep description concise** — The signature `description` should state the goal, not the field details: ```ruby # Good — concise goal class ParseOutline < DSPy::Signature description 'Extract block-level structure from HTML as a flat list of skeleton sections.' input do const :html, String, description: 'Raw HTML to parse' end output do const :sections, T::Array[Section], description: 'Block elements: headings, paragraphs, code blocks, lists' end end ``` **Use defaults over nilable arrays** — For OpenAI structured outputs compatibility: ```ruby # Good — works with OpenAI structured outputs class ASTNode < T::Struct const :children, T::Array[ASTNode], default: [] end ``` ### Recursive Types with `$defs` DSPy.rb supports recursive types in structured outputs using JSON Schema `$defs`: ```ruby class TreeNode < T::Struct const :value, String const :children, T::Array[TreeNode], default: [] # Self-reference end ``` The schema generator automatically creates `#/$defs/TreeNode` references for recursive types, compatible with OpenAI and Gemini structured outputs. ### Field Descriptions for T::Struct DSPy.rb extends T::Struct to support field-level `description:` kwargs that flow to JSON Schema: ```ruby class ASTNode < T::Struct const :node_type, NodeType, description: 'The type of node (heading, paragraph, etc.)' const :text, String, default: "", description: 'Text content of the node' const :level, Integer, default: 0 # No description — field is self-explanatory const :children, T::Array[ASTNode], default: [] end ``` **When to use field descriptions**: complex field semantics, enum-like strings, constrained values, nested structs with ambiguous names. **When to skip**: self-explanatory fields like `name`, `id`, `url`, or boolean flags. ## Version Current: 0.34.3