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dspy-miprov2-optimizer

Agent Building
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Use for MIPROv2, Bayesian optimization, instruction and demo tuning, and high-performance DSPy program optimization.

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Source SKILL.md: https://github.com/OmidZamani/dspy-skills/blob/HEAD/skills/dspy-miprov2-optimizer/SKILL.md

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DSPy MIPROv2 Optimizer

Goal

Jointly optimize instructions and few-shot demonstrations using Bayesian Optimization for maximum performance.

When to Use

  • You have 200+ training examples
  • You can afford longer optimization runs (40+ trials)
  • You need state-of-the-art performance
  • Both instructions and demos need tuning

Related Skills

Inputs

InputTypeDescription
programdspy.ModuleProgram to optimize
trainsetlist[dspy.Example]200+ training examples
metriccallableEvaluation function
autostr"light", "medium", or "heavy"
num_trialsintOptimization trials (40+)

Outputs

OutputTypeDescription
compiled_programdspy.ModuleFully optimized program

Workflow

Install the optional Optuna dependency before using MIPROv2:

pip install -U "dspy[optuna]>=3.2.1,<3.3"

Three-Stage Process

  1. Bootstrap - Generate candidate demonstrations
  2. Propose - Create grounded instruction candidates
  3. Search - Bayesian optimization over combinations

Phase 1: Setup

import dspy
from dspy.teleprompt import MIPROv2

lm = dspy.LM('openai/gpt-4o-mini')
dspy.configure(lm=lm)

Phase 2: Define Program

class RAGAgent(dspy.Module):
    def __init__(self):
        self.retrieve = dspy.Retrieve(k=3)
        self.generate = dspy.ChainOfThought("context, question -> answer")
    
    def forward(self, question):
        context = self.retrieve(question).passages
        return self.generate(context=context, question=question)

Phase 3: Optimize

from dspy.teleprompt import MIPROv2

optimizer = MIPROv2(
    metric=dspy.evaluate.answer_exact_match,
    auto="medium",  # Balanced optimization
    num_threads=24
)

compiled = optimizer.compile(RAGAgent(), trainset=trainset)

Auto Presets

PresetTrialsUse Case
"light"~10Quick iteration
"medium"~40Production optimization
"heavy"~100+Maximum performance

Production Example

import dspy
from dspy.teleprompt import MIPROv2
from dspy.evaluate import Evaluate
import json
import logging

logger = logging.getLogger(__name__)

class ReActAgent(dspy.Module):
    def __init__(self, tools):
        self.react = dspy.ReAct("question -> answer", tools=tools)
    
    def forward(self, question):
        return self.react(question=question)

def search_tool(query: str) -> list[str]:
    """Search knowledge base."""
    results = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')(query, k=3)
    return [r['long_text'] for r in results]

def optimize_agent(trainset, devset):
    """Full MIPROv2 optimization pipeline."""
    
    agent = ReActAgent(tools=[search_tool])
    
    # Baseline evaluation
    evaluator = Evaluate(
        devset=devset,
        metric=dspy.evaluate.answer_exact_match,
        num_threads=8
    )
    baseline = evaluator(agent)
    logger.info(f"Baseline: {baseline:.2%}")
    
    # MIPROv2 optimization
    optimizer = MIPROv2(
        metric=dspy.evaluate.answer_exact_match,
        auto="medium",
        num_threads=24,
        # Custom settings
        num_candidates=15,
        max_bootstrapped_demos=4,
        max_labeled_demos=8
    )
    
    compiled = optimizer.compile(agent, trainset=trainset)
    optimized = evaluator(compiled)
    logger.info(f"Optimized: {optimized:.2%}")
    
    # Save with metadata
    compiled.save("agent_mipro.json")
    
    metadata = {
        "baseline_score": baseline,
        "optimized_score": optimized,
        "improvement": optimized - baseline,
        "num_train": len(trainset),
        "num_dev": len(devset)
    }
    
    with open("optimization_metadata.json", "w") as f:
        json.dump(metadata, f, indent=2)
    
    return compiled, metadata

Instruction-Only Mode

from dspy.teleprompt import MIPROv2

# Disable demos for pure instruction optimization
optimizer = MIPROv2(
    metric=metric,
    auto="medium",
    max_bootstrapped_demos=0,
    max_labeled_demos=0
)

Best Practices

  1. Data quantity matters - 200+ examples for best results
  2. Use auto presets - Start with "medium", adjust based on results
  3. Parallel threads - Use num_threads=24 or higher if available
  4. Monitor costs - Track API usage during optimization
  5. Save intermediate - Bayesian search saves progress

Limitations

  • High computational cost (many LLM calls)
  • Requires substantial training data
  • Optimization time: hours for "heavy" preset
  • Memory intensive for large candidate sets

Official Documentation