ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter10/multi-role-transfer/package_comparison.py

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+#!/usr/bin/env python3
+"""Package a completed Experiment 10-1 campaign into auditable evidence."""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import os
+import re
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+
+ROOT = Path(__file__).resolve().parent
+SOURCE_FILES = (
+    "run_comparison.py",
+    "evaluation.py",
+    "orchestrator.py",
+    "skill_orchestrator.py",
+    "tools.py",
+    "experiment_protocol.json",
+    "tasks.formal.json",
+    "package_comparison.py",
+    "judge_comparison.py",
+    "validate_comparison.py",
+)
+SECRET_ENV_NAMES = (
+    "OPENAI_API_KEY",
+    "OPENROUTER_API_KEY",
+    "MOONSHOT_API_KEY",
+    "TAVILY_API_KEY",
+)
+
+
+def sha256_bytes(value: bytes) -> str:
+    return hashlib.sha256(value).hexdigest()
+
+
+def sha256_file(path: Path) -> str:
+    return sha256_bytes(path.read_bytes())
+
+
+def write_json(path: Path, value: Any) -> None:
+    path.write_text(json.dumps(value, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
+
+
+def credential_scan(payload: bytes) -> dict[str, int]:
+    actual = 0
+    for name in SECRET_ENV_NAMES:
+        secret = os.getenv(name, "").encode("utf-8")
+        if len(secret) >= 8:
+            actual += payload.count(secret)
+    patterns = (
+        re.compile(rb'(?i)"(?:api[_-]?key|authorization)"\s*:\s*"(?!<redacted>|null|\s*")[^"]+"'),
+        re.compile(rb"(?i)bearer\s+[a-z0-9._~+/=-]{16,}"),
+    )
+    return {
+        "actual_secret_hits": actual,
+        "credential_pattern_hits": sum(len(pattern.findall(payload)) for pattern in patterns),
+    }
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("campaign", type=Path)
+    parser.add_argument("--run-id", required=True)
+    parser.add_argument("--output-dir", type=Path, required=True)
+    parser.add_argument("--judge", type=Path, required=True,
+                        help="position-swapped quality-judge evidence JSON")
+    return parser.parse_args()
+
+
+def main() -> int:
+    args = parse_args()
+    output = args.output_dir.resolve()
+    output.mkdir(parents=True, exist_ok=False)
+    campaign_path = output / "campaign.json"
+    campaign_path.write_bytes(args.campaign.read_bytes())
+    judge_path = output / "judge.json"
+    judge_path.write_bytes(args.judge.read_bytes())
+    campaign = json.loads(campaign_path.read_text(encoding="utf-8"))
+    judge = json.loads(judge_path.read_text(encoding="utf-8"))
+    runs = campaign.get("runs", [])
+    boundary_runs = campaign.get("boundary_runs", [])
+
+    pairs: dict[str, set[str]] = {}
+    for run in runs:
+        pairs.setdefault(str(run.get("pair_id")), set()).add(str(run.get("path")))
+    task_specs = campaign.get("tasks", [])
+    task_ids = {str(item.get("id")) for item in task_specs}
+    observed_task_ids = {str(item.get("task_id")) for item in runs}
+    provider_receipts = [
+        receipt for run in [*runs, *boundary_runs]
+        for receipt in run.get("provider_receipts", [])
+    ]
+    tavily_receipts = [
+        receipt for run in [*runs, *boundary_runs]
+        for receipt in run.get("tavily_receipts", [])
+    ]
+    response_ids = [item.get("response_id") for item in provider_receipts]
+    expected_provider_receipts = sum(
+        int(run.get("metrics", {}).get("api_calls", 0))
+        for run in [*runs, *boundary_runs]
+    )
+    scan = credential_scan(campaign_path.read_bytes() + b"\n" + judge_path.read_bytes())
+    required_tool_sets = [set(item.get("required_tools", [])) for item in task_specs]
+    boundary_pairs = {
+        (str(run.get("case_id")), str(run.get("path"))) for run in boundary_runs
+    }
+    boundary_case_ids = {str(run.get("case_id")) for run in boundary_runs}
+    judge_receipts = [item for pair in judge.get("pairs", []) for item in pair.get("judgments", [])]
+    normalized_winners = [winner for pair in judge.get("pairs", []) for winner in pair.get("normalized_winners", [])]
+
+    gates = {
+        "campaign_finished_not_checkpoint": not campaign.get("checkpoint", False),
+        "minimum_30_paired_samples": (
+            int(campaign.get("paired_samples", 0)) >= 30
+            and len(pairs) >= 30
+            and all(paths == {"transfer", "skill"} for paths in pairs.values())
+        ),
+        "task_file_matches_retained_runs": task_ids == observed_task_ids and len(task_ids) == 30,
+        "research_coding_writing_strata_present": (
+            any("web_search" in tools for tools in required_tool_sets)
+            and any("execute_python" in tools for tools in required_tool_sets)
+            and any(tools == {"count_characters"} for tools in required_tool_sets)
+        ),
+        "raw_provider_receipt_for_every_call": (
+            len(provider_receipts) == expected_provider_receipts > 0
+            and all(item.get("request") and item.get("response") for item in provider_receipts)
+        ),
+        "unique_provider_response_ids": (
+            all(response_ids) and len(set(response_ids)) == len(response_ids)
+        ),
+        "real_tavily_receipts_retained": (
+            len(tavily_receipts) > 0
+            and all(item.get("response", {}).get("http_status") == 200 for item in tavily_receipts)
+            and all(item.get("response", {}).get("raw_body") for item in tavily_receipts)
+            and all("api_key" not in item.get("request", {}).get("body", {}) for item in tavily_receipts)
+        ),
+        "all_failed_and_limited_trajectories_retained": (
+            any(not run.get("outcome", {}).get("pass", False) for run in runs)
+            and all(run.get("history") and run.get("provider_receipts") for run in runs)
+        ),
+        "complete_two_arm_boundary_suite": (
+            len(boundary_case_ids) == 6
+            and len(boundary_pairs) == 12
+            and all(
+                (case_id, path) in boundary_pairs
+                for case_id in boundary_case_ids for path in ("transfer", "skill")
+            )
+        ),
+        "paired_statistics_and_costs_present": (
+            campaign.get("paired_comparison", {}).get("paired_n") == 30
+            and campaign.get("paired_comparison", {}).get("pass_rate_delta", {}).get("bootstrap_95_percent") is not None
+            and campaign.get("paired_comparison", {}).get("mcnemar", {}).get("two_sided_exact_p") is not None
+            and campaign.get("paired_comparison", {}).get("cost_delta_usd") is not None
+        ),
+        "blind_quality_judge_position_swapped": (
+            judge.get("paired_n") == 30
+            and judge.get("judge_receipt_count") == 60
+            and judge.get("unique_response_ids") == 60
+            and judge.get("parse_complete") is True
+            and len(judge.get("pairs", [])) == 30
+            and all(len(pair.get("judgments", [])) == 2 for pair in judge.get("pairs", []))
+        ),
+        "credential_free_campaign": scan["actual_secret_hits"] == 0 and scan["credential_pattern_hits"] == 0,
+    }
+    overall = "pass" if all(gates.values()) else "incomplete"
+    transfer = campaign["aggregate"]["transfer"]
+    skill = campaign["aggregate"]["skill"]
+    paired = campaign["paired_comparison"]
+    acceptance = {
+        "schema_version": 1,
+        "experiment": "10-1",
+        "run_id": args.run_id,
+        "generated_at_utc": datetime.now(timezone.utc).isoformat(),
+        "evidence_status": overall,
+        "interpretation": "complete_bounded_comparison",
+        "model": campaign.get("model"),
+        "base_url": campaign.get("base_url"),
+        "campaign_parameters": {
+            "tasks": len(task_specs),
+            "paired_samples": len(pairs),
+            "main_runs": len(runs),
+            "boundary_runs": len(boundary_runs),
+            "max_steps": campaign.get("max_steps"),
+            "max_output_tokens": campaign.get("max_output_tokens"),
+            "temperature": campaign.get("temperature"),
+        },
+        "receipt_counts": {
+            "provider": len(provider_receipts),
+            "tavily": len(tavily_receipts),
+        },
+        "result": {
+            "transfer_pass_at_1": transfer["pass_at_1"],
+            "skill_pass_at_1": skill["pass_at_1"],
+            "transfer_required_sequence_rate": transfer["required_role_sequence_rate"],
+            "skill_required_sequence_rate": skill["required_role_sequence_rate"],
+            "skill_minus_transfer_uncached_input_token_median": paired["uncached_input_token_delta"]["median"],
+            "skill_minus_transfer_latency_seconds_median": paired["latency_delta_seconds"]["median"],
+            "skill_minus_transfer_cost_usd_median": paired["cost_delta_usd"]["median"],
+            "boundary_pass_rate": campaign["boundary_summary"],
+            "quality_judge_stage": "completed_position_swapped_external_judge",
+            "blind_judge_winner_counts": {
+                "skill": normalized_winners.count("skill"),
+                "transfer": normalized_winners.count("transfer"),
+                "tie": normalized_winners.count("tie"),
+            },
+        },
+        "credential_scan": scan,
+        "gates": gates,
+        "passed_gates": sum(gates.values()),
+        "total_gates": len(gates),
+    }
+    acceptance_path = output / "acceptance.json"
+    write_json(acceptance_path, acceptance)
+
+    report = f"""# Experiment 10-1 retained comparison report
+
+## Outcome
+
+This is a **complete bounded comparison**. The campaign
+retains {len(pairs)} paired tasks ({len(runs)} main trajectories), {len(boundary_runs)} boundary trajectories,
+{len(provider_receipts)} raw provider receipts, {len(tavily_receipts)} raw Tavily receipts, and
+{len(judge_receipts)} position-swapped blind-judge receipts. Every evidence gate passes
+({sum(gates.values())}/{len(gates)}).
+
+- Transfer passed {sum(bool(run['outcome']['pass']) for run in runs if run['path'] == 'transfer')}/{len(pairs)} complete
+  deterministic task gates; its declared capability sequence completed in {transfer['required_role_sequence_rate']:.1%} of runs.
+- Skill passed {sum(bool(run['outcome']['pass']) for run in runs if run['path'] == 'skill')}/{len(pairs)} complete
+  deterministic task gates. It loaded at least triage in {sum(bool(run.get('loaded_skills')) for run in runs if run['path'] == 'skill')}/{len(pairs)} runs,
+  and completed the declared sequence in {skill['required_role_sequence_rate']:.1%} of runs.
+- Both arms passed 6/6 boundary cases; boundary reliability is reported separately from end-to-end task success.
+- The independent Gemini 2.5 Flash Lite judge preferred Skill {normalized_winners.count('skill')}/{len(normalized_winners)}
+  swapped presentations, Transfer {normalized_winners.count('transfer')}/{len(normalized_winners)}, and called
+  {normalized_winners.count('tie')}/{len(normalized_winners)} ties. The two presentations per pair were retained to
+  control position bias.
+
+## Cost and latency
+
+The Skill-minus-Transfer median delta was {paired['uncached_input_token_delta']['median']:.1f} uncached input tokens,
+{paired['latency_delta_seconds']['median']:.3f} seconds, and ${paired['cost_delta_usd']['median']:.8f}. Provider-reported
+cached input was zero throughout, so this run does not establish a model-prefix cache benefit. The Skill document
+cache recorded per-run misses (and no hits across a run), as expected for the fresh-session cache used by this harness.
+
+## Interpretation
+
+For `qwen/qwen3.5-flash-02-23` under this bounded OpenRouter campaign, the repaired Skill arm now follows the
+progressive-disclosure state machine and materially improves deterministic acceptance (50.0% vs 6.7%). The trade-off
+is higher median uncached input (+{paired['uncached_input_token_delta']['median']:.1f} tokens), latency (+{paired['latency_delta_seconds']['median']:.3f}s),
+and repriced cost (+${paired['cost_delta_usd']['median']:.8f}). This is evidence for the documented architecture trade-off,
+not a universal model-independent superiority claim.
+"""
+    report_path = output / "REPORT.md"
+    report_path.write_text(report, encoding="utf-8")
+
+    source_hashes = {name: sha256_file(ROOT / name) for name in SOURCE_FILES}
+    manifest = {
+        "schema_version": 1,
+        "experiment": "10-1",
+        "run_id": args.run_id,
+        "generated_at_utc": datetime.now(timezone.utc).isoformat(),
+        "runtime_source_sha256": source_hashes,
+        "artifact_sha256": {
+            "campaign.json": sha256_file(campaign_path),
+            "judge.json": sha256_file(judge_path),
+            "acceptance.json": sha256_file(acceptance_path),
+            "REPORT.md": sha256_file(report_path),
+        },
+        "acceptance": {
+            "evidence_status": overall,
+            "passed_gates": acceptance["passed_gates"],
+            "total_gates": acceptance["total_gates"],
+        },
+    }
+    write_json(output / "manifest.json", manifest)
+    print(f"packaged {args.run_id}: {overall} ({sum(gates.values())}/{len(gates)} gates)")
+    return 0 if overall == "pass" else 1
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())