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ai-agent-book 精选快照(<2MB 代码与文档,来自 github.com/bojieli/ai-agent-book)
2026-08-20 13:12:50 +00:00

116 lines
3.9 KiB
Python

#!/usr/bin/env python3
"""Re-judge only incomplete saved 7-3 rubric records without mutating 7-4.
The source campaign remains immutable. Each supplemental judgment records the
source record identity and answer hash so the full 7-3 validator can join it
without confusing it with a newly executed memory-system trajectory.
"""
from __future__ import annotations
import argparse
import hashlib
import json
import sys
from datetime import datetime, timezone
from pathlib import Path
HERE = Path(__file__).resolve().parent
EVAL_DIR = HERE.parents[1] / "chapter3" / "user-memory-evaluation"
sys.path.insert(0, str(EVAL_DIR))
from evaluator import LLMEvaluator # noqa: E402
from framework import UserMemoryEvaluationFramework # noqa: E402
REQUIRED_DIMENSIONS = {"precision", "recall", "reasoning", "proactivity"}
def answer_hash(answer: str) -> str:
return hashlib.sha256(answer.encode("utf-8")).hexdigest()
def complete_rubric(row: dict) -> bool:
dimensions = row.get("rubric_details") or {}
hallucination = row.get("hallucination_detail")
return (
set(dimensions) == REQUIRED_DIMENSIONS
and isinstance(hallucination, dict)
and "detected" in hallucination
and all(
isinstance(detail, dict)
and bool(detail.get("reasoning"))
and bool(detail.get("evidence") or detail.get("boundary_case"))
for detail in dimensions.values()
)
)
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument(
"--source",
type=Path,
default=HERE / "results" / "full_7_4_60_cases_costed.json",
)
parser.add_argument(
"--output",
type=Path,
default=HERE / "results" / "full_7_3_missing_rubric_supplement.json",
)
parser.add_argument("--evaluator", default="kimi", choices=["kimi", "openai"])
parser.add_argument("--model", default="kimi-k2.5")
args = parser.parse_args()
source = json.loads(args.source.read_text(encoding="utf-8"))
missing = [row for row in source.get("records", []) if not complete_rubric(row)]
framework = UserMemoryEvaluationFramework(str(EVAL_DIR / "test_cases"))
judge = LLMEvaluator(args.evaluator, model=args.model)
supplements = []
for row in missing:
test_case = framework.get_test_case(row["test_id"])
result = judge.evaluate(test_case, row["answer"])
supplements.append({
"test_id": row["test_id"],
"system": row["system"],
"layer": row["layer"],
"answer_sha256": answer_hash(row["answer"]),
"provider": args.evaluator,
"model": args.model,
"evaluation": result.model_dump(mode="json"),
})
print(f"Re-judged {row['test_id']} / {row['system']}")
complete = all(
set(item["evaluation"].get("dimensions", {})) == REQUIRED_DIMENSIONS
and item["evaluation"].get("hallucination") is not None
and all(
detail.get("reasoning") and (detail.get("evidence") or detail.get("boundary_case"))
for detail in item["evaluation"]["dimensions"].values()
)
for item in supplements
)
report = {
"schema_version": "1.0",
"experiment": "7-3",
"purpose": "supplement incomplete rubric evidence only; source trajectories are unchanged",
"generated_at_utc": datetime.now(timezone.utc).isoformat(),
"source_file": str(args.source),
"missing_records_detected": len(missing),
"supplements": supplements,
"status": "complete" if complete else "incomplete",
}
args.output.parent.mkdir(parents=True, exist_ok=True)
args.output.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
print(json.dumps({
"status": report["status"],
"supplements": len(supplements),
"output": str(args.output),
}))
return 0 if complete else 1
if __name__ == "__main__":
raise SystemExit(main())