55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
#!/usr/bin/env python3
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"""抽取智能体派发入口（阶段 F 第二部分）。
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真实模型调用必须显式授权并显式给出 provider/model：
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.venv/bin/python .agent/skills/extract-chapter-knowledge/scripts/extract_via_dispatch.py 12 3 \
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--provider catproxy-anthropic --model claude-opus-5 --thinking medium
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"""
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from __future__ import annotations
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import argparse
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import json
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import sys
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from pathlib import Path
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SCRIPT_DIR = Path(__file__).resolve().parent
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if str(SCRIPT_DIR) not in sys.path:
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sys.path.insert(0, str(SCRIPT_DIR))
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from dispatch_extraction_bridge import run_extraction_via_dispatch # noqa: E402
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REPO_ROOT = SCRIPT_DIR.parents[3]
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def main(argv: list[str] | None = None) -> int:
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parser = argparse.ArgumentParser(description="派发抽取智能体做章后知识抽取（知识链）")
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parser.add_argument("work_id", type=int)
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parser.add_argument("chapter_order", type=int)
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parser.add_argument("--provider", required=True)
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parser.add_argument("--model", required=True)
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parser.add_argument("--thinking", default=None)
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parser.add_argument("--run-id", default=None)
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args = parser.parse_args(argv)
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summary = run_extraction_via_dispatch(
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args.work_id,
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args.chapter_order,
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repo_root=REPO_ROOT,
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provider=args.provider,
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model=args.model,
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thinking=args.thinking,
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run_id=args.run_id,
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spec_dir=REPO_ROOT / "docs" / "write-chapter" / "artifacts",
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)
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print(json.dumps(
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{k: v for k, v in summary.items() if k != "lesson"},
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ensure_ascii=False, default=str,
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))
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print(f"RUN_ID={summary['run_id']}")
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print("知识草稿已落库（草稿态）；经经验确认通道 :8766 人审后才转正。")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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