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

chapter9/ai-style-skill/extract_rules.py

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+"""从用户纠正的 before/after 对中开放式提炼候选写作规则。
+
+本模块不包含预置模式库。模型直接比较用户给出的 before/after 与纠正原话，
+发现已有清单之外的新规律。所有候选都统一使用 LLM judge；模型只能提出候选，
+是否合并、校准和激活仍由模型外部代码决定。
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+import re
+from typing import Any, Dict, List, Tuple
+
+from llm_client import chat
+
+ROOT = Path(__file__).resolve().parent
+DATA_DIR = ROOT / "data"
+
+
+def load_pairs(path: Path | None = None) -> List[Dict[str, Any]]:
+    return json.loads((path or DATA_DIR / "feedback_pairs.json").read_text(encoding="utf-8"))
+
+
+_LLM_EXTRACT_PROMPT = """你是写作规范的提炼助手。请比较本批用户纠正的 before/after 对，
+从反馈本身归纳具体、可复核的写作规则。不要依赖任何预置的模式清单，也不要把规则限制在
+正则表达式能检测的现象；语义、句法、语气和篇章层面的新规律都可以提出。
+
+返回 JSON：{{"rules": [{{"id": "rule-<英文短横线命名>", "name": "...",
+"definition": "清楚说明什么情况下命中，以及什么相似情况不应命中",
+"bad_example": "取自 before 的原文片段", "good_example": "对应的 after 原文片段",
+"rewrite_hint": "具体改写建议", "scope": ["适用场景"],
+"source_ids": ["支撑该规则的反馈对 id"]}}]}}
+
+要求：
+1. 每条规则必须至少有一条 source_ids，且 id、坏例、好例都只能来自输入；不要编造证据。
+2. 同一现象只返回一条规则。规则要区分“滥用”和合理使用，不能写成一刀切禁令。
+3. 如果本批现象与“当前规则”语义相同，必须复用当前规则的 id；只有发现新规律才创建新 id。
+4. 返回的候选将全部交给外部 LLM judge，用独立人工金标集校准后才可能激活。
+
+当前规则（可能为空）：
+{existing_rules}
+
+本批反馈：
+{pairs}
+"""
+
+
+def _strip_json_fence(content: str) -> str:
+    return re.sub(r"^```(?:json)?\s*|\s*```$", "", content.strip(), flags=re.I)
+
+
+def _validate_candidate(
+    rule: Dict[str, Any], pair_by_id: Dict[str, Dict[str, Any]]
+) -> Dict[str, Any] | None:
+    required = (
+        "id", "name", "definition", "bad_example", "good_example",
+        "rewrite_hint", "source_ids",
+    )
+    if not all(rule.get(key) for key in required):
+        return None
+    if not re.fullmatch(r"rule-[a-z0-9]+(?:-[a-z0-9]+)*", str(rule["id"])):
+        return None
+
+    source_ids = list(dict.fromkeys(rule["source_ids"]))
+    if not source_ids or any(source_id not in pair_by_id for source_id in source_ids):
+        return None
+
+    bad_example = str(rule["bad_example"])
+    good_example = str(rule["good_example"])
+    supported = any(
+        bad_example in pair_by_id[source_id]["before"]
+        and good_example in pair_by_id[source_id]["after"]
+        for source_id in source_ids
+    )
+    if not supported:
+        return None
+
+    return {
+        "id": rule["id"],
+        "name": str(rule["name"]),
+        "definition": str(rule["definition"]),
+        "detector": {"type": "llm"},
+        "bad_example": bad_example,
+        "good_example": good_example,
+        "rewrite_hint": str(rule["rewrite_hint"]),
+        "scope": sorted({str(item) for item in rule.get("scope", [])}),
+        "source_ids": source_ids,
+        "status": "candidate",
+    }
+
+
+def extract_with_llm(
+    pairs: List[Dict[str, Any]],
+    *,
+    provider: str,
+    model: str | None = None,
+    seed: int = 8901,
+    existing_rules: List[Dict[str, Any]] | None = None,
+) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
+    """开放式提炼候选规则，返回 ``(候选列表, API 证据回执)``。"""
+    brief = [
+        {
+            "id": pair["id"],
+            "scene": pair["scene"],
+            "before": pair["before"],
+            "after": pair["after"],
+            "correction": pair["correction"],
+        }
+        for pair in pairs
+    ]
+    current = [
+        {"id": rule["id"], "name": rule["name"], "definition": rule["definition"]}
+        for rule in (existing_rules or [])
+    ]
+    content, receipt = chat(
+        [{
+            "role": "user",
+            "content": _LLM_EXTRACT_PROMPT.format(
+                pairs=json.dumps(brief, ensure_ascii=False, indent=2),
+                existing_rules=json.dumps(current, ensure_ascii=False, indent=2),
+            ),
+        }],
+        provider=provider,
+        model=model,
+        seed=seed,
+        max_tokens=16000,
+    )
+    payload = json.loads(_strip_json_fence(content))
+    pair_by_id = {pair["id"]: pair for pair in pairs}
+    candidates = []
+    for rule in payload.get("rules", []):
+        candidate = _validate_candidate(rule, pair_by_id)
+        if candidate is not None:
+            candidates.append(candidate)
+    return candidates, receipt
+
+
+def write_candidates(candidates: List[Dict[str, Any]], path: Path | None = None) -> Path:
+    out = path or DATA_DIR / "candidate_rules.json"
+    out.parent.mkdir(parents=True, exist_ok=True)
+    out.write_text(json.dumps(candidates, ensure_ascii=False, indent=2), encoding="utf-8")
+    return out