/dspy-evaluation-suite

name: dspy-evaluation-suite
version: "1.0.0"
dspy-compatibility: "3.2.1"
tags: ["evaluation"]
requires-extras: []
description: Use for evaluating DSPy programs with Evaluate, answer_exact_match, SemanticF1, custom metrics, baselines, and program comparisons.
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep

DSPy Evaluation Suite

Goal

Systematically evaluate DSPy programs using built-in and custom metrics with parallel execution.

When to Use

• Measuring program performance before/after optimization
• Comparing different program variants
• Establishing baselines
• Validating production readiness

Related Skills

• Use with any optimizer: [dspy-bootstrap-fewshot](../dspy-bootstrap-fewshot/SKILL.md), [dspy-miprov2-optimizer](../dspy-miprov2-optimizer/SKILL.md), [dspy-gepa-reflective](../dspy-gepa-reflective/SKILL.md)
• Evaluate RAG pipelines: [dspy-rag-pipeline](../dspy-rag-pipeline/SKILL.md)

Inputs

| Input | Type | Description | |-------|------|-------------| | `program` | `dspy.Module` | Program to evaluate | | `devset` | `list[dspy.Example]` | Evaluation examples | | `metric` | `callable` | Scoring function | | `num_threads` | `int` | Parallel threads |

Outputs

| Output | Type | Description | |--------|------|-------------| | `score` | `float` | Average metric score | | `results` | `list` | Per-example results |

Workflow

Phase 1: Setup Evaluator

from dspy.evaluate import Evaluate

evaluator = Evaluate(
    devset=devset,
    metric=my_metric,
    num_threads=8,
    display_progress=True
)

Phase 2: Run Evaluation

result = evaluator(my_program)
print(f"Score: {result.score:.2f}%")
# Access individual results: (example, prediction, score) tuples
for example, pred, score in result.results[:3]:
    print(f"Example: {example.question[:50]}... Score: {score}")

Built-in Metrics

answer_exact_match

import dspy

# Normalized, case-insensitive comparison
metric = dspy.evaluate.answer_exact_match

SemanticF1

LLM-based semantic evaluation:

from dspy.evaluate import SemanticF1

semantic = SemanticF1()
score = semantic(example, prediction)

Custom Metrics

Basic Metric

def exact_match(example, pred, trace=None):
    """Returns bool, int, or float."""
    return example.answer.lower().strip() == pred.answer.lower().strip()

Multi-Factor Metric

def quality_metric(example, pred, trace=None):
    """Score based on multiple factors."""
    score = 0.0
    
    # Correctness (50%)
    if example.answer.lower() in pred.answer.lower():
        score += 0.5
    
    # Conciseness (25%)
    if len(pred.answer.split()) <= 20:
        score += 0.25
    
    # Has reasoning (25%)
    if hasattr(pred, 'reasoning') and pred.reasoning:
        score += 0.25
    
    return score

GEPA-Compatible Metric

def feedback_metric(example, pred, trace=None, pred_name=None, pred_trace=None):
    """Return a GEPA-compatible score and textual feedback."""
    correct = example.answer.lower() in pred.answer.lower()
    
    if correct:
        return dspy.Prediction(score=1.0, feedback="Correct answer provided.")
    else:
        return dspy.Prediction(
            score=0.0,
            feedback=f"Expected '{example.answer}', got '{pred.answer}'"
        )

Production Example

import dspy
from dspy.evaluate import Evaluate, SemanticF1
import json
import logging
from typing import Optional
from dataclasses import dataclass

logger = logging.getLogger(__name__)

@dataclass
class EvaluationResult:
    score: float
    num_examples: int
    correct: int
    incorrect: int
    errors: int

def comprehensive_metric(example, pred, trace=None) -> float:
    """Multi-dimensional evaluation metric."""
    scores = []
    
    # 1. Correctness
    if hasattr(example, 'answer') and hasattr(pred, 'answer'):
        correct = example.answer.lower().strip() in pred.answer.lower().strip()
        scores.append(1.0 if correct else 0.0)
    
    # 2. Completeness (answer not empty or error)
    if hasattr(pred, 'answer'):
        complete = len(pred.answer.strip()) > 0 and "error" not in pred.answer.lower()
        scores.append(1.0 if complete else 0.0)
    
    # 3. Reasoning quality (if available)
    if hasattr(pred, 'reasoning'):
        has_reasoning = len(str(pred.reasoning)) > 20
        scores.append(1.0 if has_reasoning else 0.5)
    
    return sum(scores) / len(scores) if scores else 0.0

class EvaluationSuite:
    def __init__(self, devset, num_threads=8):
        self.devset = devset
        self.num_threads = num_threads
    
    def evaluate(self, program, metric=None) -> EvaluationResult:
        """Run full evaluation with detailed results."""
        metric = metric or comprehensive_metric

        evaluator = Evaluate(
            devset=self.devset,
            metric=metric,
            num_threads=self.num_threads,
            display_progress=True
        )

        eval_result = evaluator(program)

        # Extract individual scores from results
        scores = [score for example, pred, score in eval_result.results]
        correct = sum(1 for s in scores if s >= 0.5)
        errors = sum(1 for s in scores if s == 0)

        return EvaluationResult(
            score=eval_result.score,
            num_examples=len(self.devset),
            correct=correct,
            incorrect=len(self.devset) - correct - errors,
            errors=errors
        )
    
    def compare(self, programs: dict, metric=None) -> dict:
        """Compare multiple programs."""
        results = {}
        
        for name, program in programs.items():
            logger.info(f"Evaluating: {name}")
            results[name] = self.evaluate(program, metric)
        
        # Rank by score
        ranked = sorted(results.items(), key=lambda x: x[1].score, reverse=True)
        
        print("\n=== Comparison Results ===")
        for rank, (name, resu