feat：添加回复后打分追踪器

src/maisaka/reply_effect/judge.py

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+"""回复效果 LLM 窄维度评审。"""
+
+from __future__ import annotations
+
+from collections.abc import Awaitable, Callable
+from typing import Any, Dict, List, Tuple
+
+import json
+
+from .models import FollowupMessageSnapshot, ReplyEffectRecord, RubricScoreItem, RubricScores
+from .scoring import normalize_text_for_prompt
+
+JudgeRunner = Callable[[str], Awaitable[str]]
+
+
+async def judge_reply_effect(record: ReplyEffectRecord, judge_runner: JudgeRunner | None) -> Tuple[RubricScores, str]:
+    """执行 LLM rubric judge，失败时返回中性分。"""
+
+    if judge_runner is None:
+        return RubricScores(), "未提供 LLM judge runner"
+
+    prompt = build_judge_prompt(record)
+    try:
+        response_text = await judge_runner(prompt)
+        payload = _loads_json_object(response_text)
+        return parse_rubric_scores(payload), ""
+    except Exception as exc:
+        return RubricScores(), str(exc)
+
+
+def build_judge_prompt(record: ReplyEffectRecord) -> str:
+    """构建窄维度评分 prompt。"""
+
+    followup_text = _format_followups(record.followup_messages)
+    return (
+        "你是 Maisaka 回复效果的窄维度评审器，只评估这一次 bot 回复的交互感知质量。\n"
+        "不要评价总体满意度，不要给建议，只输出 JSON。\n\n"
+        "评分范围：1 到 5，1=很差，3=中性，5=很好。\n"
+        "uncanny_risk 的 1=完全不怪，5=非常过度拟人/越界/油腻。\n\n"
+        f"bot 回复：\n{normalize_text_for_prompt(record.reply.reply_text, 1200)}\n\n"
+        f"后续用户消息：\n{followup_text or '（暂无后续用户消息）'}\n\n"
+        "请输出严格 JSON 对象，格式如下：\n"
+        "{\n"
+        '  "social_presence": {"score": 3, "reason": "...", "evidence_spans": ["..."], "confidence": 0.7},\n'
+        '  "warmth": {"score": 3, "reason": "...", "evidence_spans": ["..."], "confidence": 0.7},\n'
+        '  "competence": {"score": 3, "reason": "...", "evidence_spans": ["..."], "confidence": 0.7},\n'
+        '  "appropriateness": {"score": 3, "reason": "...", "evidence_spans": ["..."], "confidence": 0.7},\n'
+        '  "uncanny_risk": {"score": 3, "reason": "...", "evidence_spans": ["..."], "confidence": 0.7}\n'
+        "}"
+    )
+
+
+def parse_rubric_scores(payload: Dict[str, Any]) -> RubricScores:
+    """解析 LLM rubric JSON。"""
+
+    return RubricScores(
+        social_presence=_parse_item(payload.get("social_presence")),
+        warmth=_parse_item(payload.get("warmth")),
+        competence=_parse_item(payload.get("competence")),
+        appropriateness=_parse_item(payload.get("appropriateness")),
+        uncanny_risk=_parse_item(payload.get("uncanny_risk")),
+        available=True,
+    )
+
+
+def _parse_item(raw_item: Any) -> RubricScoreItem:
+    if not isinstance(raw_item, dict):
+        raw_item = {}
+    score = _coerce_float(raw_item.get("score"), 3.0)
+    score = max(1.0, min(5.0, score))
+    evidence_spans = raw_item.get("evidence_spans")
+    if not isinstance(evidence_spans, list):
+        evidence_spans = []
+    return RubricScoreItem(
+        score=score,
+        normalized_score=round((score - 1.0) / 4.0, 4),
+        reason=str(raw_item.get("reason") or "").strip(),
+        evidence_spans=[str(item).strip() for item in evidence_spans if str(item).strip()],
+        confidence=max(0.0, min(1.0, _coerce_float(raw_item.get("confidence"), 0.0))),
+    )
+
+
+def _loads_json_object(response_text: str) -> Dict[str, Any]:
+    normalized_response = str(response_text or "").strip()
+    if normalized_response.startswith("```"):
+        normalized_response = normalized_response.strip("`")
+        if normalized_response.lower().startswith("json"):
+            normalized_response = normalized_response[4:].strip()
+    try:
+        parsed = json.loads(normalized_response)
+    except json.JSONDecodeError:
+        start = normalized_response.find("{")
+        end = normalized_response.rfind("}")
+        if start < 0 or end <= start:
+            raise
+        parsed = json.loads(normalized_response[start : end + 1])
+    if not isinstance(parsed, dict):
+        raise ValueError("LLM judge 未返回 JSON 对象")
+    return parsed
+
+
+def _format_followups(followups: List[FollowupMessageSnapshot]) -> str:
+    lines: List[str] = []
+    for index, followup in enumerate(followups[:5], start=1):
+        marker = "目标用户" if followup.is_target_user else "其他用户"
+        lines.append(
+            f"{index}. [{marker}] {normalize_text_for_prompt(followup.visible_text or followup.plain_text, 500)}"
+        )
+    return "\n".join(lines)
+
+
+def _coerce_float(value: Any, default: float) -> float:
+    try:
+        return float(value)
+    except (TypeError, ValueError):
+        return default