dspy-miprov2-optimizer

# 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 - For limited data (10-50 examples): [dspy-bootstrap-fewshot](../dspy-bootstrap-fewshot/SKILL.md) - For agentic systems: [dspy-gepa-reflective](../dspy-gepa-reflective/SKILL.md) - Measure improvements: [dspy-evaluation-suite](../dspy-evaluation-suite/SKILL.md) ## Inputs | Input | Type | Description | |-------|------|-------------| | `program` | `dspy.Module` | Program to optimize | | `trainset` | `list[dspy.Example]` | 200+ training examples | | `metric` | `callable` | Evaluation function | | `auto` | `str` | "light", "medium", or "heavy" | | `num_trials` | `int` | Optimization trials (40+) | ## Outputs | Output | Type | Description | |--------|------|-------------| | `compiled_program` | `dspy.Module` | Fully optimized program | ## Workflow Install the optional Optuna dependency before using MIPROv2: ```bash 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 ```python import dspy from dspy.teleprompt import MIPROv...