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0d3f7cc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 | """GEPA Self-Evolution pipeline — orchestrates the full evolution workflow.
Usage:
pipeline = EvolutionPipeline()
result = await pipeline.run(
skill_name="erp-integration",
base_content="...",
dataset_path="self-evolution/eval-datasets/erp-integration.json",
)
"""
from __future__ import annotations
import json
import logging
from datetime import UTC, datetime
from pathlib import Path
from typing import Any
from hermes.evolution.constraint_gates import ConstraintGates
from hermes.evolution.eval_suite import EvalSuite
from hermes.evolution.optimizer import GEPAOptimizer
logger = logging.getLogger(__name__)
class EvolutionPipeline:
"""Main GEPA evolution pipeline orchestrator.
Runs the full evolution cycle:
1. Initialize population from base content
2. For each generation: evaluate, select, crossover, mutate
3. Apply constraint gates
4. Track best-performing variants
5. Return evolved content
"""
def __init__(
self,
evolution_rounds: int = 10,
population_size: int = 20,
mutation_rate: float = 0.15,
crossover_rate: float = 0.7,
elite_ratio: float = 0.2,
early_stop_rounds: int = 3,
output_dir: str = "self-evolution/evolved-skills",
) -> None:
self.optimizer = GEPAOptimizer(
population_size=population_size,
mutation_rate=mutation_rate,
crossover_rate=crossover_rate,
elite_ratio=elite_ratio,
early_stop_rounds=early_stop_rounds,
)
self.constraint_gates = ConstraintGates()
self.eval_suite = EvalSuite()
self.output_dir = Path(output_dir)
self.evolution_rounds = evolution_rounds
self._history: list[dict[str, Any]] = []
async def run(
self,
skill_name: str,
base_content: str,
dataset_path: str | Path | None = None,
examples: list[dict[str, Any]] | None = None,
) -> dict[str, Any]:
"""Run the full evolution pipeline for a skill."""
logger.info(f"Starting evolution for '{skill_name}'")
if examples is None and dataset_path:
examples = self.eval_suite.load_dataset(dataset_path)
examples = examples or []
population = self.optimizer.initialize_population(base_content)
logger.info(f"Initialized population with {len(population)} individuals")
best_content = base_content
best_score: float = 0.0
generation_results: list[dict[str, Any]] = []
for round_num in range(1, self.evolution_rounds + 1):
logger.info(f"Evolution round {round_num}/{self.evolution_rounds}")
if self.optimizer.should_stop():
logger.info(f"Early stopping at round {round_num} (no improvement)")
break
fitness_scores = await self._evaluate_population(
population, skill_name, base_content, examples
)
avg_fitness = sum(fitness_scores) / len(fitness_scores) if fitness_scores else 0.0
max_fitness = max(fitness_scores) if fitness_scores else 0.0
best_idx = fitness_scores.index(max_fitness) if fitness_scores else 0
round_best = population[best_idx]
gates_result = await self.constraint_gates.check_all(
original=base_content,
evolved=round_best,
test_cases=examples,
)
round_record = {
"round": round_num,
"avg_fitness": avg_fitness,
"max_fitness": max_fitness,
"best_content": round_best[:200],
"gates_passed": gates_result["passed"],
"failed_gates": gates_result["failed_gates"],
}
generation_results.append(round_record)
if max_fitness > best_score and gates_result["passed"]:
best_score = max_fitness
best_content = round_best
population = self.optimizer.evolve(population, fitness_scores)
final_eval = await self.eval_suite.evaluate(
skill_name=skill_name,
examples=examples or [],
evolved_content=best_content,
)
result = {
"skill_name": skill_name,
"status": "completed",
"generations_run": self.optimizer.generation,
"best_score": best_score,
"improvement": best_score - self._baseline_score(examples),
"final_evaluation": final_eval,
"generation_results": generation_results,
"evolved_content": best_content,
"timestamp": datetime.now(UTC).isoformat(),
}
self._history.append(result)
await self._save_result(skill_name, result)
logger.info(f"Evolution completed for '{skill_name}' with score {best_score:.3f}")
return result
async def _evaluate_population(
self,
population: list[str],
skill_name: str,
base_content: str,
examples: list[dict[str, Any]],
) -> list[float]:
"""Evaluate fitness of each individual in the population."""
fitness_scores: list[float] = []
for individual in population:
gates_result = await self.constraint_gates.check_all(
original=base_content,
evolved=individual,
test_cases=examples,
)
if not gates_result["passed"]:
fitness_scores.append(0.0)
continue
eval_result = await self.eval_suite.evaluate(
skill_name=skill_name,
examples=examples or [],
evolved_content=individual,
)
score = eval_result.get("overall_score", 0.0)
fitness_scores.append(score)
return fitness_scores
def _baseline_score(self, examples: list[dict[str, Any]]) -> float:
"""Compute baseline score from example metrics."""
if not examples:
return 0.0
scores = []
for ex in examples:
metrics = ex.get("metrics", {})
overall = (
metrics.get("accuracy", 0) * 0.35
+ (1.0 - metrics.get("efficiency", 5000) / 10000) * 0.20
+ metrics.get("coherence", 0) * 0.20
+ metrics.get("safety", 0) * 0.25
)
scores.append(overall)
return sum(scores) / len(scores) if scores else 0.0
async def _save_result(self, skill_name: str, result: dict[str, Any]) -> None:
"""Save evolution result to disk."""
try:
self.output_dir.mkdir(parents=True, exist_ok=True)
path = self.output_dir / f"{skill_name}_v{self.optimizer.generation}.json"
with open(path, "w", encoding="utf-8") as f:
json.dump(result, f, indent=2, default=str)
logger.info(f"Saved evolution result to {path}")
except Exception as e:
logger.warning(f"Could not save evolution result: {e}")
def get_history(self) -> list[dict[str, Any]]:
"""Get evolution history."""
return list(self._history)
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