refactor(engine): thin _run_case and _save_rule_results

Three near-identical Turn(...)+save_turn blocks (send-fail / poll-except /
happy path) collapse into one _persist_turn helper differing only by the
optional fields set. The期望→隐式规则 translation becomes a pure,
unit-testable derive_implicit_rules seam (CONTEXT: 期望与规则叠加生效),
and the rule_type→ModelPurpose map is hoisted to a module constant.

backend/agenteval/evaluation/engine.py

@@ -8,11 +8,13 @@ via an ``asyncio.Event`` cancel token.
 import asyncio
 import uuid
 from dataclasses import dataclass
+from datetime import datetime
 from typing import Any, Callable, Optional
 
 from agenteval.channels.base import EvalChannel
 from agenteval.channels.factory import ChannelFactory
 from agenteval.config import get_settings
+from agenteval.evaluation.implicit_rules import derive_implicit_rules
 from agenteval.evaluation.judgement import CaseOutcome, RuleOutcome, combine_case_outcome
 from agenteval.evaluation.rules import RuleResult, get_rule
 from agenteval.evaluation.run_summary import build_run_summary
@@ -59,6 +61,14 @@ def _build_send_message(content: str) -> dict[str, Any]:
     }
 
 
+# 需要模型资源的规则类型 → 评测岗位（ModelPurpose）；其余规则无需模型
+RULE_PURPOSE = {
+    "llm_score": ModelPurpose.JUDGE,
+    "semantic_similarity": ModelPurpose.EMBEDDING,
+    "safety": ModelPurpose.MODERATION,
+}
+
+
 class EvalEngine:
     """Execute evaluation scenarios against targets.
 
@@ -237,6 +247,36 @@ class EvalEngine:
 
     # ── case / turn execution ─────────────────────────────────────────
 
+    def _persist_turn(
+        self,
+        run: EvalRun,
+        case: Case,
+        round_index: int,
+        message: str,
+        sent_at: datetime,
+        *,
+        question_msg_id: Optional[str] = None,
+        reply: Optional[dict] = None,
+        received_at: Optional[datetime] = None,
+        latency_ms: Optional[int] = None,
+    ) -> Turn:
+        """Build, persist, and return a Turn. The three call sites (send-fail /
+        poll-except / happy path) differ only in which optional fields are set."""
+        turn = Turn(
+            id=str(uuid.uuid4()),
+            run_id=run.id,
+            case_id=case.id,
+            round_index=round_index,
+            sent_message=_build_send_message(message),
+            sent_at=sent_at,
+            question_msg_id=question_msg_id,
+            reply=reply,
+            received_at=received_at,
+            latency_ms=latency_ms,
+        )
+        self.result_repo.save_turn(turn)
+        return turn
+
     async def _run_case(
         self,
         run: EvalRun,
@@ -277,15 +317,7 @@ class EvalEngine:
             sent_at = utc_now()
             send_result = await self.channel.send(message)
             if not send_result.ok:
-                turn = Turn(
-                    id=str(uuid.uuid4()),
-                    run_id=run.id,
-                    case_id=case.id,
-                    round_index=round_index,
-                    sent_message=_build_send_message(message),
-                    sent_at=sent_at,
-                )
-                self.result_repo.save_turn(turn)
+                turn = self._persist_turn(run, case, round_index, message, sent_at)
                 await self._save_rule_results(run, case, turn, [], progress_callback)
                 await self._emit(
                     progress_callback,
@@ -305,17 +337,15 @@ class EvalEngine:
                 )
             except Exception as poll_exc:
                 received_at = utc_now()
-                turn = Turn(
-                    id=str(uuid.uuid4()),
-                    run_id=run.id,
-                    case_id=case.id,
-                    round_index=round_index,
-                    sent_message=_build_send_message(message),
-                    sent_at=sent_at,
+                turn = self._persist_turn(
+                    run,
+                    case,
+                    round_index,
+                    message,
+                    sent_at,
                     question_msg_id=send_result.question_msg_id,
                     received_at=received_at,
                 )
-                self.result_repo.save_turn(turn)
                 await self._emit(
                     progress_callback,
                     "turn_error",
@@ -332,19 +362,17 @@ class EvalEngine:
             if sent_at and received_at:
                 latency_ms = int((received_at - sent_at).total_seconds() * 1000)
 
-            turn = Turn(
-                id=str(uuid.uuid4()),
-                run_id=run.id,
-                case_id=case.id,
-                round_index=round_index,
-                sent_message=_build_send_message(message),
-                sent_at=sent_at,
+            turn = self._persist_turn(
+                run,
+                case,
+                round_index,
+                message,
+                sent_at,
                 question_msg_id=send_result.question_msg_id,
                 reply=reply.raw_message if reply else None,
                 received_at=received_at,
                 latency_ms=latency_ms,
             )
-            self.result_repo.save_turn(turn)
             dialog.append(turn)
 
             await self._emit(
@@ -380,27 +408,7 @@ class EvalEngine:
         from agenteval.models import EvalRuleConfig
 
         rules_config: list[EvalRuleConfig] = list(case.eval_rules)
-
-        implicit_config: list[EvalRuleConfig] = []
-        if case.expectations.response_time_max_ms:
-            implicit_config.append(
-                EvalRuleConfig(
-                    type="response_time",
-                    params={
-                        "max_ms": case.expectations.response_time_max_ms,
-                    },
-                )
-            )
-        if case.expectations.keywords_include or case.expectations.keywords_exclude:
-            implicit_config.append(
-                EvalRuleConfig(
-                    type="keyword_match",
-                    params={
-                        "keywords": case.expectations.keywords_include,
-                        "exclude_keywords": case.expectations.keywords_exclude,
-                    },
-                )
-            )
+        implicit_config = derive_implicit_rules(case.expectations)
 
         all_replied = bool(dialog) and all(t.reply is not None for t in dialog)
 
@@ -415,11 +423,7 @@ class EvalEngine:
 
         all_rules = [(cfg, False) for cfg in rules_config] + [(cfg, True) for cfg in implicit_config]
         for rule_config, is_implicit in all_rules:
-            purpose = {
-                "llm_score": ModelPurpose.JUDGE,
-                "semantic_similarity": ModelPurpose.EMBEDDING,
-                "safety": ModelPurpose.MODERATION,
-            }.get(rule_config.type)
+            purpose = RULE_PURPOSE.get(rule_config.type)
             try:
                 model_config = self._resolve_model(purpose) if purpose else None
                 rule = get_rule(

backend/agenteval/evaluation/implicit_rules.py

@@ -0,0 +1,30 @@
+"""Translate a case's期望 (Expectation) into implicit评估规则.
+
+期望描述"想要什么"，评估规则是"怎么判定"——期望派生的隐式规则与显式规则
+叠加生效（CONTEXT.md）。此翻译是纯逻辑，独立于规则执行与持久化，便于单测。
+"""
+
+from agenteval.models import EvalRuleConfig, Expectation
+
+
+def derive_implicit_rules(expectation: Expectation) -> list[EvalRuleConfig]:
+    """Build the implicit rule configs a case's expectation implies."""
+    rules: list[EvalRuleConfig] = []
+    if expectation.response_time_max_ms:
+        rules.append(
+            EvalRuleConfig(
+                type="response_time",
+                params={"max_ms": expectation.response_time_max_ms},
+            )
+        )
+    if expectation.keywords_include or expectation.keywords_exclude:
+        rules.append(
+            EvalRuleConfig(
+                type="keyword_match",
+                params={
+                    "keywords": expectation.keywords_include,
+                    "exclude_keywords": expectation.keywords_exclude,
+                },
+            )
+        )
+    return rules

tests/unit/test_implicit_rules.py

@@ -0,0 +1,45 @@
+"""Unit tests for derive_implicit_rules — 期望→隐式规则的纯翻译."""
+
+from agenteval.evaluation.implicit_rules import derive_implicit_rules
+from agenteval.models import Expectation
+
+
+def test_empty_expectation_yields_no_rules():
+    assert derive_implicit_rules(Expectation()) == []
+
+
+def test_intent_and_coherence_alone_yield_no_rules():
+    # intent / coherence_min_score 不派生隐式规则（无对应规则类型）
+    exp = Expectation(intent="问诊分流", coherence_min_score=0.8)
+    assert derive_implicit_rules(exp) == []
+
+
+def test_response_time_yields_response_time_rule():
+    rules = derive_implicit_rules(Expectation(response_time_max_ms=2000))
+    assert len(rules) == 1
+    assert rules[0].type == "response_time"
+    assert rules[0].params == {"max_ms": 2000}
+
+
+def test_keywords_include_yields_keyword_rule():
+    rules = derive_implicit_rules(Expectation(keywords_include=["挂号", "门诊"]))
+    assert len(rules) == 1
+    assert rules[0].type == "keyword_match"
+    assert rules[0].params == {"keywords": ["挂号", "门诊"], "exclude_keywords": []}
+
+
+def test_keywords_exclude_alone_yields_keyword_rule():
+    rules = derive_implicit_rules(Expectation(keywords_exclude=["投诉"]))
+    assert len(rules) == 1
+    assert rules[0].type == "keyword_match"
+    assert rules[0].params == {"keywords": [], "exclude_keywords": ["投诉"]}
+
+
+def test_both_expectations_yield_two_rules_in_order():
+    exp = Expectation(
+        response_time_max_ms=1500,
+        keywords_include=["预约"],
+        keywords_exclude=["取消"],
+    )
+    rules = derive_implicit_rules(exp)
+    assert [r.type for r in rules] == ["response_time", "keyword_match"]