Class: DSPy::Teleprompt::MIPROv2

Inherits:
Teleprompter show all
Extended by:
T::Sig
Defined in:
lib/dspy/teleprompt/mipro_v2.rb

Overview

MIPROv2: Multi-prompt Instruction Proposal with Retrieval Optimization State-of-the-art prompt optimization combining bootstrap sampling, instruction generation, and Bayesian optimization

Defined Under Namespace

Modules: AutoMode Classes: CandidateConfig, MIPROv2Config, MIPROv2Result

Instance Attribute Summary collapse

Attributes inherited from Teleprompter

#config, #evaluator, #metric

Instance Method Summary collapse

Methods inherited from Teleprompter

#create_evaluator, #ensure_typed_examples, #evaluate_program, #save_results, #validate_inputs

Constructor Details

#initialize(metric: nil, config: nil) ⇒ MIPROv2

Returns a new instance of MIPROv2.



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# File 'lib/dspy/teleprompt/mipro_v2.rb', line 264

def initialize(metric: nil, config: nil)
  @mipro_config = config || MIPROv2Config.new
  super(metric: metric, config: @mipro_config)
  
  @proposer = DSPy::Propose::GroundedProposer.new(config: @mipro_config.proposer_config)
  @optimization_trace = []
  @evaluated_candidates = []
end

Instance Attribute Details

#mipro_config ⇒ Object (readonly)

Returns the value of attribute mipro_config.



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# File 'lib/dspy/teleprompt/mipro_v2.rb', line 253

def mipro_config
  @mipro_config
end

#proposer ⇒ Object (readonly)

Returns the value of attribute proposer.



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# File 'lib/dspy/teleprompt/mipro_v2.rb', line 256

def proposer
  @proposer
end

Instance Method Details

#compile(program, trainset:, valset: nil) ⇒ Object 



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# File 'lib/dspy/teleprompt/mipro_v2.rb', line 281

def compile(program, trainset:, valset: nil)
  validate_inputs(program, trainset, valset)

  instrument_step('miprov2_compile', {
    trainset_size: trainset.size,
    valset_size: valset&.size || 0,
    num_trials: @mipro_config.num_trials,
    optimization_strategy: @mipro_config.optimization_strategy,
    mode: infer_auto_mode
  }) do
    # Convert examples to typed format
    typed_trainset = ensure_typed_examples(trainset)
    typed_valset = valset ? ensure_typed_examples(valset) : nil

    # Use validation set if available, otherwise use part of training set
    evaluation_set = typed_valset || typed_trainset.take([typed_trainset.size / 3, 10].max)

    # Phase 1: Bootstrap few-shot examples
    emit_event('phase_start', { phase: 1, name: 'bootstrap' })
    bootstrap_result = phase_1_bootstrap(program, typed_trainset)
    emit_event('phase_complete', { 
      phase: 1, 
      success_rate: bootstrap_result.statistics[:success_rate],
      candidate_sets: bootstrap_result.candidate_sets.size
    })

    # Phase 2: Generate instruction candidates
    emit_event('phase_start', { phase: 2, name: 'instruction_proposal' })
    proposal_result = phase_2_propose_instructions(program, typed_trainset, bootstrap_result)
    emit_event('phase_complete', { 
      phase: 2, 
      num_candidates: proposal_result.num_candidates,
      best_instruction_preview: proposal_result.best_instruction[0, 50]
    })

    # Phase 3: Bayesian optimization
    emit_event('phase_start', { phase: 3, name: 'optimization' })
    optimization_result = phase_3_optimize(
      program,
      evaluation_set,
      proposal_result,
      bootstrap_result
    )
    emit_event('phase_complete', { 
      phase: 3, 
      best_score: optimization_result[:best_score],
      trials_completed: optimization_result[:trials_completed]
    })

    # Build final result
    final_result = build_miprov2_result(
      optimization_result,
      bootstrap_result,
      proposal_result
    )

    save_results(final_result)
    final_result
  end
end