backend/agenteval/evaluation/cost_tracking.py

@@ -1,142 +0,0 @@
-"""Cost tracking for evaluation runs — pricing lookup and usage-based cost.
-
-成本口径：评测自身消耗的 LLM token（judge/generator/embedding/moderation，
-经 ModelGateway 归集进 RunSummary.eval_usage_by_purpose），按岗位所用模型的
-单价计费。被评智能体的通道用量不在口径内（tutu-api 通道不返回用量）。
-
-单价解析顺序：``data/model_pricing.json``（部署侧覆盖）→ ``DEFAULT_PRICING``。
-"""
-
-import json
-from pathlib import Path
-from typing import Any, Optional
-
-from pydantic import BaseModel, Field
-
-# data/model_pricing.json：与 DB 同目录，部署时挂载覆盖，缺省文件视为无覆盖
-_PRICING_FILE = Path(__file__).resolve().parents[3] / "data" / "model_pricing.json"
-
-
-class ModelPricing(BaseModel):
-    """Pricing for a model (per 1M tokens)."""
-
-    model_id: str
-    prompt_cost_per_1m: float = Field(description="Cost per 1M prompt tokens in USD")
-    completion_cost_per_1m: float = Field(description="Cost per 1M completion tokens in USD")
-
-
-class CostBreakdown(BaseModel):
-    """Cost breakdown for one usage bucket (e.g. one purpose or a whole run)."""
-
-    prompt_tokens: int = 0
-    completion_tokens: int = 0
-    total_tokens: int = 0
-    cost_usd: float = 0.0
-
-
-# Default pricing for common models (per 1M tokens in USD)
-DEFAULT_PRICING: dict[str, ModelPricing] = {
-    "gpt-4o": ModelPricing(model_id="gpt-4o", prompt_cost_per_1m=5.0, completion_cost_per_1m=15.0),
-    "gpt-4o-mini": ModelPricing(model_id="gpt-4o-mini", prompt_cost_per_1m=0.15, completion_cost_per_1m=0.60),
-    "gpt-4-turbo": ModelPricing(model_id="gpt-4-turbo", prompt_cost_per_1m=10.0, completion_cost_per_1m=30.0),
-    "gpt-3.5-turbo": ModelPricing(model_id="gpt-3.5-turbo", prompt_cost_per_1m=0.50, completion_cost_per_1m=1.50),
-    "claude-3-5-sonnet": ModelPricing(model_id="claude-3-5-sonnet", prompt_cost_per_1m=3.0, completion_cost_per_1m=15.0),
-    "claude-3-haiku": ModelPricing(model_id="claude-3-haiku", prompt_cost_per_1m=0.25, completion_cost_per_1m=1.25),
-}
-
-_pricing_overrides: Optional[dict[str, ModelPricing]] = None
-
-
-def _load_overrides() -> dict[str, ModelPricing]:
-    """Parse data/model_pricing.json once; a missing/broken file means no override."""
-    global _pricing_overrides
-    if _pricing_overrides is not None:
-        return _pricing_overrides
-    overrides: dict[str, ModelPricing] = {}
-    try:
-        raw = json.loads(_PRICING_FILE.read_text(encoding="utf-8"))
-        if isinstance(raw, dict):
-            for name, entry in raw.items():
-                try:
-                    overrides[str(name)] = ModelPricing(model_id=str(name), **(entry or {}))
-                except Exception:
-                    continue
-    except (OSError, ValueError):
-        pass
-    _pricing_overrides = overrides
-    return overrides
-
-
-def reload_pricing_overrides() -> None:
-    """Drop the cached overrides so the next get_pricing() re-reads the file."""
-    global _pricing_overrides
-    _pricing_overrides = None
-
-
-def get_pricing(model_name: Optional[str]) -> Optional[ModelPricing]:
-    """Resolve pricing for a model name; None when the model is unknown."""
-    if not model_name:
-        return None
-    return _load_overrides().get(model_name) or DEFAULT_PRICING.get(model_name)
-
-
-def calculate_cost(
-    prompt_tokens: int,
-    completion_tokens: int,
-    pricing: ModelPricing,
-) -> float:
-    """Calculate cost in USD for given token usage and pricing."""
-    prompt_cost = (prompt_tokens / 1_000_000) * pricing.prompt_cost_per_1m
-    completion_cost = (completion_tokens / 1_000_000) * pricing.completion_cost_per_1m
-    return prompt_cost + completion_cost
-
-
-def usage_breakdown(usage: dict[str, Any], pricing: ModelPricing) -> CostBreakdown:
-    """Turn one recorded usage dict (prompt/completion tokens) into a CostBreakdown."""
-    prompt_tokens = int(usage.get("prompt_tokens") or 0)
-    completion_tokens = int(usage.get("completion_tokens") or 0)
-    return CostBreakdown(
-        prompt_tokens=prompt_tokens,
-        completion_tokens=completion_tokens,
-        total_tokens=prompt_tokens + completion_tokens,
-        cost_usd=calculate_cost(prompt_tokens, completion_tokens, pricing),
-    )
-
-
-def build_eval_cost_section(
-    usage_by_purpose: Optional[dict[str, dict[str, Any]]],
-    model_configs: Optional[dict[str, Any]],
-) -> Optional[dict[str, Any]]:
-    """Per-purpose eval-LLM cost from recorded run usage; None when no usage.
-
-    ``model_configs`` is the run summary's purpose → model snapshot map; a purpose
-    whose model has no known pricing gets ``cost_usd: None`` (tokens still shown).
-    """
-    if not usage_by_purpose:
-        return None
-    model_configs = model_configs or {}
-    items: list[dict[str, Any]] = []
-    total_cost = 0.0
-    has_any_cost = False
-    for purpose, usage in sorted(usage_by_purpose.items()):
-        model_name = (model_configs.get(purpose) or {}).get("model_name")
-        pricing = get_pricing(model_name)
-        breakdown = usage_breakdown(usage, pricing) if pricing else None
-        if breakdown is not None:
-            total_cost += breakdown.cost_usd
-            has_any_cost = True
-        items.append(
-            {
-                "purpose": purpose,
-                "model_name": model_name,
-                "prompt_tokens": breakdown.prompt_tokens if breakdown else int(usage.get("prompt_tokens") or 0),
-                "completion_tokens": breakdown.completion_tokens if breakdown else int(usage.get("completion_tokens") or 0),
-                "total_tokens": breakdown.total_tokens if breakdown else int(usage.get("total_tokens") or 0),
-                "cost_usd": breakdown.cost_usd if breakdown else None,
-            }
-        )
-    return {
-        "by_purpose": items,
-        "total_tokens": sum(i["total_tokens"] for i in items),
-        "total_cost_usd": round(total_cost, 6) if has_any_cost else None,
-    }

backend/agenteval/evaluation/engine.py

@@ -118,9 +118,6 @@ class EvalEngine:
             max(1, get_settings().max_concurrent_rules),
         )
         self._state_lock = asyncio.Lock()
-        # 分岗位的评测侧 token 用量（ModelPurpose.value → 用量），由各规则/生成器
-        # 的按次用量归集而来，避免并发下用网关总量做差产生竞态。
-        self._usage_by_purpose: dict[str, dict[str, int]] = {}
         # Collects fatal case-level errors (e.g. dynamic message generation
         # failures) so their cause is persisted into run.summary — not just
         # emitted transiently over WebSocket.
@@ -211,15 +208,12 @@ class EvalEngine:
 
             results = self.run_repo.get_results(run.id)
             turns = self.run_repo.get_turns(run.id)
-            usage = self.model_gateway.total_usage
             summary = build_run_summary(
                 case_outcomes=case_outcomes,
                 latencies=[t.latency_ms for t in turns if t.latency_ms is not None],
                 rule_passes=[r.passed for r in results],
                 case_errors=self._case_errors or None,
                 model_configs=resolved_snapshot or None,
-                eval_token_usage=usage if usage["total_tokens"] > 0 else None,
-                eval_usage_by_purpose=self._usage_by_purpose or None,
             )
             run.status = RunStatus.COMPLETED
             run.completed_at = utc_now()
@@ -264,15 +258,14 @@ class EvalEngine:
         finally:
             run = self.run_repo.update(run) or run
             # Best-effort cleanup of the channel's HTTP client.
-            for resource in (self.channel, self.model_gateway):
-                close = getattr(resource, "close", None)
-                if callable(close):
-                    try:
-                        result = close()
-                        if asyncio.iscoroutine(result):
-                            await result
-                    except Exception:
-                        pass
+            close = getattr(self.channel, "close", None)
+            if callable(close):
+                try:
+                    result = close()
+                    if asyncio.iscoroutine(result):
+                        await result
+                except Exception:
+                    pass
             try:
                 self.session.close()
             except Exception:
@@ -400,7 +393,7 @@ class EvalEngine:
                         "error": outcome.reason,
                     },
                 )
-                return CaseOutcome(passed=False, connectivity=False, abandoned=bool(dialog)), 0, 0
+                return failed, 0, 0
 
             if turn is None:
                 raise RuntimeError("channel exchange succeeded without invoking the sent hook")
@@ -417,7 +410,7 @@ class EvalEngine:
                         "error": f"poll_reply 异常: {outcome.reason}",
                     },
                 )
-                return CaseOutcome(passed=False, connectivity=False, abandoned=bool(dialog)), 0, 0
+                return failed, 0, 0
 
             dialog.append(turn)
 
@@ -484,8 +477,6 @@ class EvalEngine:
                         gateway=self.model_gateway if model_config else None,
                     )
                     result = await rule.evaluate(case, dialog)
-                    if purpose:
-                        await self._accumulate_usage(purpose.value, rule.llm_usage)
                 except Exception as exc:
                     result = RuleResult(passed=False, reason=f"模型配置解析失败: {exc}")
                 return rule_config, is_implicit, result
@@ -578,10 +569,7 @@ class EvalEngine:
         try:
             model_config = await self._resolve_model(ModelPurpose.GENERATOR)
             if model_config:
-                content, usage = await self.model_gateway.chat_with_usage(
-                    model_config, messages_payload, temperature=0.7
-                )
-                await self._accumulate_usage(ModelPurpose.GENERATOR.value, usage)
+                content = await self.model_gateway.chat(model_config, messages_payload, temperature=0.7)
             else:
                 content = await self._generate_messages_legacy(messages_payload)
 
@@ -635,16 +623,6 @@ class EvalEngine:
             raise ValueError("LLM 返回内容为空或无法解析")
         return content
 
-    async def _accumulate_usage(self, purpose_key: str, usage: dict[str, int] | None) -> None:
-        if not usage or not usage.get("total_tokens"):
-            return
-        async with self._state_lock:
-            bucket = self._usage_by_purpose.setdefault(
-                purpose_key, {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
-            )
-            for key in bucket:
-                bucket[key] += int(usage.get(key) or 0)
-
     async def _resolve_model(self, purpose: ModelPurpose | None) -> ModelRuntimeConfig | None:
         if purpose is None:
             return None

backend/agenteval/evaluation/judgement.py

@@ -30,8 +30,6 @@ class CaseOutcome:
 
     passed: bool
     connectivity: bool
-    # 对话中途放弃：已有完成的轮次，但后续发送/接收失败导致对话未走完
-    abandoned: bool = False
 
 
 def combine_case_outcome(

backend/agenteval/evaluation/report.py

@@ -12,8 +12,7 @@ from typing import Any, Optional
 from sqlmodel import Session
 
 from agenteval.evaluation.case_verdict import build_case_evidence, resolve_case_verdicts
-from agenteval.evaluation.cost_tracking import build_eval_cost_section
-from agenteval.evaluation.go_no_go import AcceptanceCriteria, evaluate_go_no_go
+from agenteval.evaluation.go_no_go import evaluate_go_no_go
 from agenteval.evaluation.metrics import aggregate_runs
 from agenteval.evaluation.report_render import render_html, render_json, render_markdown
 from agenteval.models import Campaign, EvalRun, RunStatus, RunSummary
@@ -109,24 +108,16 @@ def generate_report(run_id: str, session=None) -> dict[str, Any]:
         "total_cases": total_cases,
         "passed_cases": passed_cases,
         "failed_cases": summary.failed_cases,
-        "abandoned_cases": summary.abandoned_cases,
-        "abandonment_rate": summary.abandonment_rate,
         "total_rules": summary.total_rules,
         "passed_rules": summary.passed_rules,
         "pass_rate": summary.pass_rate if summary.pass_rate is not None else 0.0,
         "connectivity_cases": connectivity_count,
         "judged_pass_rate": judged_pass_rate,
         "avg_latency_ms": summary.avg_latency_ms,
-        "eval_token_usage": summary.eval_token_usage,
-        "eval_usage_by_purpose": summary.eval_usage_by_purpose,
-        "eval_cost": build_eval_cost_section(summary.eval_usage_by_purpose, summary.model_configs),
     }
 
-    # Generate go/no-go verdict（场景级验收标准优先，缺省用全局默认）
-    criteria = None
-    if scenario is not None and scenario.acceptance_criteria:
-        criteria = AcceptanceCriteria(**scenario.acceptance_criteria)
-    verdict = evaluate_go_no_go(summary_dict, criteria)
+    # Generate go/no-go verdict
+    verdict = evaluate_go_no_go(summary_dict)
 
     return {
         "run_id": run.id,

backend/agenteval/evaluation/report_render.py

@@ -33,12 +33,6 @@ HTML_TEMPLATE = """<!DOCTYPE html>
         .badge { padding: 2px 8px; border-radius: 4px; font-size: 12px; }
         .pass { background: #e6f7e6; color: #2e7d32; }
         .fail { background: #ffebee; color: #c62828; }
-        .verdict { border-radius: 6px; padding: 16px; margin: 24px 0; }
-        .verdict .decision { font-size: 18px; font-weight: 700; }
-        .verdict ul { margin: 8px 0 0; padding-left: 20px; }
-        .verdict-go { background: #e6f7e6; border: 1px solid #b7e0b8; color: #2e7d32; }
-        .verdict-no_go { background: #ffebee; border: 1px solid #f5c6cb; color: #c62828; }
-        .verdict-conditional { background: #fff8e1; border: 1px solid #ffe082; color: #8d6e00; }
         pre { white-space: pre-wrap; word-break: break-word; background: #f5f5f5; padding: 8px; border-radius: 4px; }
     </style>
 </head>
@@ -49,22 +43,6 @@ HTML_TEMPLATE = """<!DOCTYPE html>
         <p>评测场景：{{ report.scenario_name }}（{{ report.scenario_id }}）</p>
         <p>执行时间：{{ report.started_at }} 至 {{ report.completed_at or '进行中' }}</p>
 
-        {% if report.go_no_go %}
-        {% set gng = report.go_no_go %}
-        {% set decision_label = {'go': 'GO — 建议上线', 'no_go': 'NO-GO — 不建议上线', 'conditional': '有条件通过 — 修复后复测'} %}
-        <div class="verdict verdict-{{ gng.decision }}">
-            <div class="decision">上线评估：{{ decision_label.get(gng.decision, gng.decision) }}</div>
-            <div>{{ gng.summary }}</div>
-            {% if gng.criteria_results %}
-            <ul>
-                {% for c in gng.criteria_results %}
-                <li>{{ '✅' if c.passed else '❌' }} {{ c.detail }}</li>
-                {% endfor %}
-            </ul>
-            {% endif %}
-        </div>
-        {% endif %}
-
         <div class="summary">
             <div class="card">
                 <div class="value">{{ report.summary.total_cases }}</div>
@@ -143,25 +121,6 @@ def render_markdown(report: dict[str, Any]) -> str:
         f"**开始时间**: {report.get('started_at', '-')}  ",
         f"**完成时间**: {report.get('completed_at', '-')}  ",
         "",
-    ]
-
-    gng = report.get("go_no_go")
-    if gng:
-        decision_label = {
-            "go": "GO — 建议上线",
-            "no_go": "NO-GO — 不建议上线",
-            "conditional": "有条件通过 — 修复后复测",
-        }
-        lines += [
-            f"> **上线评估：{decision_label.get(gng.get('decision'), gng.get('decision'))}**",
-            f"> {gng.get('summary', '')}",
-        ]
-        for c in gng.get("criteria_results") or []:
-            mark = "✅" if c.get("passed") else "❌"
-            lines.append(f"> {mark} {c.get('detail', '')}")
-        lines.append("")
-
-    lines += [
         "## 汇总",
         "",
         "| 指标 | 数值 |",
@@ -175,26 +134,6 @@ def render_markdown(report: dict[str, Any]) -> str:
         f"| 连通用例 | {s.get('connectivity_cases', 0)} |",
         f"| 判定型通过率 | {judged_rate_text} |",
         "",
-    ]
-
-    cost = s.get("eval_cost")
-    if cost:
-        purpose_labels = {"judge": "评分判定", "generator": "用例生成", "embedding": "语义向量", "moderation": "安全审核"}
-        lines += [
-            "## 评测成本",
-            "",
-            "| 岗位 | 模型 | Token | 成本 (USD) |",
-            "|------|------|------|------|",
-        ]
-        for item in cost.get("by_purpose") or []:
-            label = purpose_labels.get(item.get("purpose"), item.get("purpose") or "-")
-            cost_text = "—" if item.get("cost_usd") is None else f"${item['cost_usd']:.6f}"
-            lines.append(f"| {label} | {item.get('model_name') or '-'} | {item.get('total_tokens', 0)} | {cost_text} |")
-        total_cost = cost.get("total_cost_usd")
-        total_text = "—" if total_cost is None else f"${total_cost:.6f}"
-        lines += [f"| **合计** | | {cost.get('total_tokens', 0)} | {total_text} |", ""]
-
-    lines += [
         "## 用例明细",
         "",
     ]

backend/agenteval/evaluation/rules/__init__.py

@@ -2,7 +2,6 @@
 
 # Import all rules to populate the registry.
 from agenteval.evaluation.rules.base import EvalRule, RuleResult, get_rule, list_rule_types, register_rule
-from agenteval.evaluation.rules.fluency import FluencyRule
 from agenteval.evaluation.rules.json_schema import JsonSchemaRule
 from agenteval.evaluation.rules.keyword import KeywordMatchRule
 from agenteval.evaluation.rules.llm_score import LlmScoreRule
@@ -16,7 +15,6 @@ __all__ = [
     "get_rule",
     "list_rule_types",
     "register_rule",
-    "FluencyRule",
     "JsonSchemaRule",
     "KeywordMatchRule",
     "LlmScoreRule",

backend/agenteval/evaluation/rules/base.py

@@ -35,14 +35,6 @@ class EvalRule(ABC):
         self.params = params
         self.model_config = model_config
         self.gateway = gateway
-        # 本规则执行期间消耗的评测侧 token 用量（引擎按评测岗位归集）
-        self.llm_usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
-
-    def _record_llm_usage(self, usage: Optional[dict[str, int]]) -> None:
-        if not usage:
-            return
-        for key in self.llm_usage:
-            self.llm_usage[key] += int(usage.get(key) or 0)
 
     @abstractmethod
     async def evaluate(self, case: Case, dialog: list[Turn]) -> RuleResult:

backend/agenteval/evaluation/rules/fluency.py

@@ -1,135 +0,0 @@
-"""Fluency assessment rule using LLM to evaluate conversation naturalness."""
-
-import json
-
-from agenteval.evaluation.rules.base import EvalRule, RuleResult, register_rule
-from agenteval.evaluation.rules.scored_llm import call_scored_llm, parse_scored_content
-from agenteval.models import Case, Turn
-from agenteval.utils.llm import extract_reply_text
-
-
-@register_rule
-class FluencyRule(EvalRule):
-    """Use LLM to evaluate conversation fluency and naturalness.
-
-    Evaluates:
-    - Naturalness: Does the conversation flow naturally?
-    - Repetition: Are there unnecessary repetitions?
-    - Coherence: Is the conversation coherent and logical?
-
-    Configuration params:
-        min_score: Minimum fluency score to pass (0-10, default: 7)
-        criteria: Custom evaluation criteria (optional)
-    """
-
-    name = "fluency"
-
-    async def evaluate(self, case: Case, dialog: list[Turn]) -> RuleResult:
-        if not dialog:
-            return RuleResult(passed=False, reason="无回复记录")
-
-        # Build conversation context
-        conversation_parts = []
-        for turn in dialog:
-            # Extract sent message
-            sent_text = ""
-            if turn.sent_message:
-                sent_text = self._extract_message_text(turn.sent_message)
-
-            # Extract reply
-            reply_text = extract_reply_text(turn.reply) if turn.reply else ""
-
-            if sent_text:
-                conversation_parts.append(f"用户: {sent_text}")
-            if reply_text:
-                conversation_parts.append(f"助手: {reply_text}")
-
-        if not conversation_parts:
-            return RuleResult(passed=False, reason="无法提取对话内容")
-
-        conversation_text = "\n".join(conversation_parts)
-
-        # Get evaluation criteria
-        min_score = float(self.params.get("min_score", 7))
-        custom_criteria = self.params.get("criteria", "")
-
-        # Call LLM for fluency assessment
-        if self.model_config and self.gateway:
-            score, reason = await self._evaluate_fluency(conversation_text, custom_criteria)
-        else:
-            api_url = self.params.get("api_url")
-            api_key = self.params.get("api_key")
-            model = self.params.get("model", "gpt-4o-mini")
-            if not api_url:
-                return RuleResult(passed=False, reason="流畅度评估规则未绑定评估模型")
-            score, reason = await self._call_llm(api_url, api_key, model, conversation_text, custom_criteria)
-
-        if score is None:
-            return RuleResult(passed=False, reason=f"流畅度评估失败: {reason}")
-
-        passed = score >= min_score
-        verdict = "通过" if passed else "未通过"
-        return RuleResult(
-            passed=passed,
-            score=score / 10.0,
-            reason=f"流畅度评分 {score}/10，{verdict} (阈值 {min_score})；{reason}",
-        )
-
-    def _extract_message_text(self, sent_message: dict) -> str:
-        """Extract text from sent_message dict."""
-        body = sent_message.get("msgBody", "")
-        if isinstance(body, dict):
-            return body.get("content", "")
-        try:
-            return json.loads(body).get("content", "")
-        except Exception:
-            return str(body)
-
-    async def _evaluate_fluency(self, conversation: str, custom_criteria: str) -> tuple[float | None, str]:
-        """Evaluate conversation fluency using the gateway."""
-        system_prompt, user_prompt = self._build_prompts(conversation, custom_criteria)
-        try:
-            content, usage = await self.gateway.chat_with_usage(
-                self.model_config,
-                [
-                    {"role": "system", "content": system_prompt},
-                    {"role": "user", "content": user_prompt},
-                ],
-                temperature=0.2,
-            )
-            self._record_llm_usage(usage)
-            return parse_scored_content(content)
-        except Exception as exc:
-            return None, str(exc)
-
-    async def _call_llm(
-        self,
-        api_url: str,
-        api_key: str | None,
-        model: str,
-        conversation: str,
-        custom_criteria: str,
-    ) -> tuple[float | None, str]:
-        """Call LLM API directly for fluency assessment."""
-        system_prompt, user_prompt = self._build_prompts(conversation, custom_criteria)
-        return await call_scored_llm(api_url, api_key, model, system_prompt, user_prompt)
-
-    @staticmethod
-    def _build_prompts(conversation: str, custom_criteria: str) -> tuple[str, str]:
-        """Build system and user prompts for fluency evaluation."""
-        default_criteria = """评估对话的流畅度和自然性，考虑以下方面：
-1. 自然性：对话是否流畅自然，像真人对话？
-2. 重复性：是否有不必要的重复或冗余？
-3. 连贯性：对话是否逻辑连贯，上下文一致？
-4. 响应质量：助手的回复是否恰当、有帮助？"""
-
-        criteria = custom_criteria if custom_criteria else default_criteria
-
-        system_prompt = (
-            "你是一位对话质量评估专家。请评估以下对话的流畅度和自然性。\n"
-            f"评估标准：{criteria}\n"
-            "打分范围：0-10分（10分最高）\n"
-            '只输出一个 JSON 对象：{"score": number, "reason": "简短说明"}'
-        )
-        user_prompt = f"对话内容：\n{conversation}"
-        return system_prompt, user_prompt

backend/agenteval/evaluation/rules/llm_score.py

@@ -3,10 +3,11 @@
 import asyncio
 import json
 
+import httpx
+
 from agenteval.evaluation.rules.base import EvalRule, RuleResult, register_rule
-from agenteval.evaluation.rules.scored_llm import call_scored_llm, parse_scored_content
 from agenteval.models import Case, Turn
-from agenteval.utils.llm import extract_reply_text
+from agenteval.utils.llm import extract_content_from_llm_response, extract_reply_text, parse_json_from_llm_text
 
 
 @register_rule
@@ -60,18 +61,11 @@ class LlmScoreRule(EvalRule):
             reason=f"LLM 评分 {score}/10，{verdict} (阈值 {min_score}){detail}",
         )
 
-    _MAX_CONCURRENT_DIMENSIONS = 5
-
     async def _evaluate_dimensions(
         self, question: str, reply: str, dimensions: list[dict]
     ) -> RuleResult:
-        semaphore = asyncio.Semaphore(self._MAX_CONCURRENT_DIMENSIONS)
-
-        async def bounded(dim: dict) -> tuple[float | None, str]:
-            async with semaphore:
-                return await self._evaluate_one_dimension(question, reply, dim)
-
-        results = await asyncio.gather(*(bounded(dim) for dim in dimensions))
+        tasks = [self._evaluate_one_dimension(question, reply, dim) for dim in dimensions]
+        results = await asyncio.gather(*tasks)
 
         dimension_scores = {}
         dimension_reasons = []
@@ -119,7 +113,7 @@ class LlmScoreRule(EvalRule):
     async def _call_gateway(self, question: str, reply: str, criteria: str) -> tuple[float | None, str]:
         system_prompt, user_prompt = self._prompts(question, reply, criteria)
         try:
-            content, usage = await self.gateway.chat_with_usage(
+            content = await self.gateway.chat(
                 self.model_config,
                 [
                     {"role": "system", "content": system_prompt},
@@ -127,8 +121,7 @@ class LlmScoreRule(EvalRule):
                 ],
                 temperature=0.2,
             )
-            self._record_llm_usage(usage)
-            return parse_scored_content(content)
+            return self._parse_score(content)
         except Exception as exc:
             return None, str(exc)
 
@@ -141,6 +134,12 @@ class LlmScoreRule(EvalRule):
         )
         return system_prompt, f"用户问题：{question}\n智能体回复：{reply}"
 
+    @staticmethod
+    def _parse_score(content: str) -> tuple[float, str]:
+        parsed = parse_json_from_llm_text(content)
+        score = float(parsed["score"])
+        return max(0.0, min(10.0, score)), parsed.get("reason", "")
+
     async def _call_llm(
         self,
         api_url: str,
@@ -152,4 +151,28 @@ class LlmScoreRule(EvalRule):
     ) -> tuple[float | None, str]:
         """Call the configured LLM API and parse a numeric score between 0 and 10."""
         system_prompt, user_prompt = self._prompts(question, reply, criteria)
-        return await call_scored_llm(api_url, api_key, model, system_prompt, user_prompt)
+
+        headers = {"Content-Type": "application/json"}
+        if api_key:
+            headers["Authorization"] = f"Bearer {api_key}"
+
+        payload = {
+            "model": model,
+            "messages": [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_prompt},
+            ],
+            "temperature": 0.2,
+        }
+
+        try:
+            async with httpx.AsyncClient(timeout=60) as client:
+                resp = await client.post(api_url, headers=headers, json=payload)
+            resp.raise_for_status()
+            content = extract_content_from_llm_response(resp.json())
+            if not content:
+                return None, "LLM 返回内容为空"
+
+            return self._parse_score(content)
+        except Exception as exc:
+            return None, str(exc)

backend/agenteval/evaluation/rules/response_time.py

@@ -6,13 +6,7 @@ from agenteval.models import Case, Turn
 
 @register_rule
 class ResponseTimeRule(EvalRule):
-    """Check whether the reply latency is within the configured threshold.
-
-    Supports multiple metrics:
-    - max_ms: threshold for single turn latency (backward compatible)
-    - avg_latency_max_ms: threshold for average latency across all turns
-    - throughput_min: minimum throughput in turns per minute
-    """
+    """Check whether the reply latency is within the configured threshold."""
 
     name = "response_time"
 
@@ -20,78 +14,23 @@ class ResponseTimeRule(EvalRule):
         if not dialog:
             return RuleResult(passed=False, reason="无回复记录")
 
-        # Collect latencies from all turns
-        latencies = [t.latency_ms for t in dialog if t.latency_ms is not None]
-        if not latencies:
-            return RuleResult(passed=False, reason="无法获取响应时间")
-
-        # Backward compatible: check last turn against max_ms threshold
         threshold_ms = self.params.get("max_ms")
         if threshold_ms is None:
             threshold_ms = case.expectations.response_time_max_ms
+        if threshold_ms is None:
+            return RuleResult(passed=True, reason="未配置响应时间阈值")
 
-        results = []
-        all_passed = True
+        last_turn = dialog[-1]
+        latency = last_turn.latency_ms
+        if latency is None:
+            return RuleResult(passed=False, reason="无法获取响应时间")
 
-        # 1. Single turn latency check (backward compatible)
-        if threshold_ms is not None:
-            last_latency = latencies[-1]
-            passed = last_latency <= threshold_ms
-            if not passed:
-                all_passed = False
-            results.append(f"最后一轮 {last_latency}ms {'≤' if passed else '>'} {threshold_ms}ms")
+        if latency > threshold_ms:
+            return RuleResult(
+                passed=False,
+                score=max(0.0, 1.0 - (latency - threshold_ms) / threshold_ms),
+                reason=f"响应时间 {latency}ms 超过阈值 {threshold_ms}ms",
+            )
 
-        # 2. Average latency check
-        avg_latency_max_ms = self.params.get("avg_latency_max_ms")
-        if avg_latency_max_ms is not None:
-            avg_latency = sum(latencies) / len(latencies)
-            passed = avg_latency <= avg_latency_max_ms
-            if not passed:
-                all_passed = False
-            results.append(f"平均延迟 {avg_latency:.0f}ms {'≤' if passed else '>'} {avg_latency_max_ms}ms")
-
-        # 3. Throughput check (turns per minute)
-        throughput_min = self.params.get("throughput_min")
-        if throughput_min is not None and len(dialog) >= 2:
-            first_sent = dialog[0].sent_at
-            last_received = dialog[-1].received_at
-            if first_sent and last_received:
-                duration_minutes = (last_received - first_sent).total_seconds() / 60
-                if duration_minutes > 0:
-                    throughput = len(dialog) / duration_minutes
-                    passed = throughput >= throughput_min
-                    if not passed:
-                        all_passed = False
-                    results.append(f"吞吐量 {throughput:.1f} turns/min {'≥' if passed else '<'} {throughput_min}")
-
-        # If no metrics configured, just report the last turn latency
-        if not results:
-            last_latency = latencies[-1]
-            return RuleResult(passed=True, score=1.0, reason=f"响应时间 {last_latency}ms")
-
-        avg_latency = sum(latencies) / len(latencies)
-        has_extended_metrics = avg_latency_max_ms is not None or throughput_min is not None
-        if threshold_ms is not None and not has_extended_metrics:
-            # 仅 max_ms：保持 v0.3 原语义——基于最后一轮评分，超限有惩罚
-            last_latency = latencies[-1]
-            if last_latency > threshold_ms:
-                score = max(0.0, 1.0 - (last_latency - threshold_ms) / threshold_ms)
-            else:
-                score = 1.0 if last_latency <= 0 else min(1.0, threshold_ms / last_latency)
-        elif threshold_ms is not None:
-            score = 1.0 if avg_latency <= 0 else min(1.0, threshold_ms / avg_latency)
-        else:
-            score = 1.0
-
-        verdict = "通过" if all_passed else "未通过"
-        return RuleResult(
-            passed=all_passed,
-            score=score,
-            reason=f"响应时间指标 {verdict}：{'; '.join(results)}",
-            details={
-                "latencies": latencies,
-                "avg_latency_ms": avg_latency,
-                "min_latency_ms": min(latencies),
-                "max_latency_ms": max(latencies),
-            },
-        )
+        score = 1.0 if latency <= 0 else min(1.0, threshold_ms / latency)
+        return RuleResult(passed=True, score=score, reason=f"响应时间 {latency}ms 通过")

backend/agenteval/evaluation/rules/safety.py

@@ -69,7 +69,7 @@ class SafetyRule(EvalRule):
             issues.append(f"包含违禁词: {hit_words}")
 
         # Layer 2: moderation API (optional, degrades gracefully)
-        if use_api and (api_url or self.model_config):
+        if use_api and (api_url or self.model_config) and not hit_words:
             try:
                 if self.model_config and self.gateway:
                     result = await self.gateway.moderate(self.model_config, reply_text)
@@ -146,7 +146,7 @@ class SafetyRule(EvalRule):
         user_prompt = f"对话上下文：\n{context}\n\n助手最后回复：{reply_text}"
 
         try:
-            content, usage = await self.gateway.chat_with_usage(
+            content = await self.gateway.chat(
                 self.model_config,
                 [
                     {"role": "system", "content": system_prompt},
@@ -154,7 +154,6 @@ class SafetyRule(EvalRule):
                 ],
                 temperature=0.1,
             )
-            self._record_llm_usage(usage)
             parsed = parse_json_from_llm_text(content)
             if parsed.get("hallucination", False):
                 return parsed.get("reason", "疑似编造事实")

backend/agenteval/evaluation/rules/scored_llm.py

@@ -1,51 +0,0 @@
-"""Shared plumbing for rules that ask an LLM for a 0-10 score.
-
-llm_score 与 fluency 共用的直连 LLM 调用与评分解析——两处各自实现一遍的
-重复逻辑收敛到这里（v1.3.1 Phase 3 代码质量项）。
-"""
-
-import httpx
-
-from agenteval.utils.llm import extract_content_from_llm_response, parse_json_from_llm_text
-
-
-def parse_scored_content(content: str) -> tuple[float, str]:
-    """Parse an LLM reply into a clamped 0-10 score plus reason."""
-    parsed = parse_json_from_llm_text(content)
-    score = float(parsed["score"])
-    return max(0.0, min(10.0, score)), parsed.get("reason", "")
-
-
-async def call_scored_llm(
-    api_url: str,
-    api_key: str | None,
-    model: str,
-    system_prompt: str,
-    user_prompt: str,
-    timeout: float = 60.0,
-) -> tuple[float | None, str]:
-    """POST a chat request to a raw OpenAI-compatible endpoint and parse the score.
-
-    Returns (score, reason); score is None with an error message on failure.
-    """
-    headers = {"Content-Type": "application/json"}
-    if api_key:
-        headers["Authorization"] = f"Bearer {api_key}"
-    payload = {
-        "model": model,
-        "messages": [
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": user_prompt},
-        ],
-        "temperature": 0.2,
-    }
-    try:
-        async with httpx.AsyncClient(timeout=timeout) as client:
-            resp = await client.post(api_url, headers=headers, json=payload)
-        resp.raise_for_status()
-        content = extract_content_from_llm_response(resp.json())
-        if not content:
-            return None, "LLM 返回内容为空"
-        return parse_scored_content(content)
-    except Exception as exc:
-        return None, str(exc)

backend/agenteval/evaluation/rules/semantic.py

@@ -74,10 +74,7 @@ class SemanticSimilarityRule(EvalRule):
 
         try:
             if self.model_config and self.gateway:
-                (reply_vec, ref_vec), usage = await self.gateway.embed_with_usage(
-                    self.model_config, [reply_text, reference]
-                )
-                self._record_llm_usage(usage)
+                reply_vec, ref_vec = await self.gateway.embed(self.model_config, [reply_text, reference])
             else:
                 api_url: str | None = self.params.get("api_url")
                 api_key: str | None = self.params.get("api_key")

backend/agenteval/evaluation/run_summary.py

@@ -20,17 +20,13 @@ def build_run_summary(
     rule_passes: Sequence[bool],
     case_errors: Optional[list[dict[str, str]]] = None,
     model_configs: Optional[dict[str, Any]] = None,
-    eval_token_usage: Optional[dict[str, int]] = None,
-    eval_usage_by_purpose: Optional[dict[str, dict[str, int]]] = None,
 ) -> RunSummary:
     """Compute a run's summary口径 from its authoritative case outcomes."""
     total_cases = len(case_outcomes)
     passed_cases = sum(1 for o in case_outcomes.values() if o.passed)
     connectivity_count = sum(1 for o in case_outcomes.values() if o.connectivity)
-    abandoned_cases = sum(1 for o in case_outcomes.values() if o.abandoned)
 
     pass_rate = round(passed_cases / total_cases, 4) if total_cases else 0.0
-    abandonment_rate = round(abandoned_cases / total_cases, 4) if total_cases else None
     # 连通用例按引擎口径计通过，判定型通过数 = 总通过数 - 连通用例数
     judged_total = total_cases - connectivity_count
     judged_pass_rate = (
@@ -43,19 +39,13 @@ def build_run_summary(
         total_cases=total_cases,
         passed_cases=passed_cases,
         failed_cases=total_cases - passed_cases,
-        abandoned_cases=abandoned_cases,
         total_rules=len(rule_passes),
         passed_rules=sum(1 for p in rule_passes if p),
         pass_rate=pass_rate,
         judged_pass_rate=judged_pass_rate,
-        abandonment_rate=abandonment_rate,
         avg_latency_ms=avg_latency_ms,
-        eval_token_usage=eval_token_usage,
-        eval_usage_by_purpose=eval_usage_by_purpose,
         case_outcomes={
-            case_id: CaseOutcomeSummary(
-                passed=o.passed, connectivity=o.connectivity, abandoned=o.abandoned
-            )
+            case_id: CaseOutcomeSummary(passed=o.passed, connectivity=o.connectivity)
             for case_id, o in case_outcomes.items()
         },
         case_errors=case_errors or [],

backend/agenteval/model_gateway.py

@@ -16,30 +16,16 @@ class ModelGateway:
     def __init__(self, timeout: float = 60.0, transport: httpx.AsyncBaseTransport | None = None):
         self.timeout = timeout
         self.transport = transport
-        # 评测侧 LLM 调用的累计 token 用量（引擎结束时写入 run summary）
-        self.total_usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
-        self._client: httpx.AsyncClient | None = None
-
-    async def _get_client(self) -> httpx.AsyncClient:
-        # 单实例复用客户端，省去每次 LLM 调用的 TCP/TLS 握手
-        if self._client is None or self._client.is_closed:
-            self._client = httpx.AsyncClient(timeout=self.timeout, transport=self.transport)
-        return self._client
-
-    async def close(self) -> None:
-        if self._client is not None and not self._client.is_closed:
-            await self._client.aclose()
-            self._client = None
 
     async def _post(self, config: ModelRuntimeConfig, payload: dict[str, Any]) -> dict[str, Any]:
         adapter = self._adapter(config)
         try:
-            client = await self._get_client()
-            response = await client.post(
-                config.endpoint_url,
-                headers=adapter.headers(config.api_key),
-                json=payload,
-            )
+            async with httpx.AsyncClient(timeout=self.timeout, transport=self.transport) as client:
+                response = await client.post(
+                    config.endpoint_url,
+                    headers=adapter.headers(config.api_key),
+                    json=payload,
+                )
             response.raise_for_status()
             data = response.json()
             if not isinstance(data, dict):
@@ -66,49 +52,21 @@ class ModelGateway:
         messages: list[dict[str, str]],
         temperature: float = 0.2,
     ) -> str:
-        content, _ = await self.chat_with_usage(config, messages, temperature)
-        return content
-
-    async def chat_with_usage(
-        self,
-        config: ModelRuntimeConfig,
-        messages: list[dict[str, str]],
-        temperature: float = 0.2,
-    ) -> tuple[str, dict[str, int] | None]:
-        """Like chat(), but also returns this call's token usage (None if omitted)."""
         adapter = self._adapter(config)
         try:
             payload = adapter.chat_payload(config.model_name, messages, temperature)
-            data = await self._post(config, payload)
-            usage = adapter.parse_usage(data)
-            self._accumulate(usage)
-            return adapter.parse_chat(data), usage
+            return adapter.parse_chat(await self._post(config, payload))
         except ProtocolAdapterError as exc:
             raise ModelGatewayError(str(exc)) from exc
 
     async def embed(self, config: ModelRuntimeConfig, inputs: str | list[str]) -> list[list[float]]:
-        vectors, _ = await self.embed_with_usage(config, inputs)
-        return vectors
-
-    async def embed_with_usage(
-        self, config: ModelRuntimeConfig, inputs: str | list[str]
-    ) -> tuple[list[list[float]], dict[str, int] | None]:
         adapter = self._adapter(config)
         try:
             payload = adapter.embedding_payload(config.model_name, inputs)
-            data = await self._post(config, payload)
-            usage = adapter.parse_usage(data)
-            self._accumulate(usage)
-            return adapter.parse_embeddings(data), usage
+            return adapter.parse_embeddings(await self._post(config, payload))
         except ProtocolAdapterError as exc:
             raise ModelGatewayError(str(exc)) from exc
 
-    def _accumulate(self, usage: dict[str, int] | None) -> None:
-        if not usage:
-            return
-        for key in self.total_usage:
-            self.total_usage[key] += int(usage.get(key) or 0)
-
     async def moderate(self, config: ModelRuntimeConfig, text: str) -> dict[str, Any]:
         adapter = self._adapter(config)
         try:

backend/agenteval/model_protocols/base.py

@@ -39,10 +39,6 @@ class ModelProtocolAdapter:
     def parse_moderation(self, data: dict[str, Any]) -> dict[str, Any]:
         self._unsupported(ModelCapability.MODERATION)
 
-    def parse_usage(self, data: dict[str, Any]) -> dict[str, int] | None:
-        """Extract token usage from a response; None for protocols that omit it."""
-        return None
-
     def _unsupported(self, capability: ModelCapability) -> None:
         raise ProtocolAdapterError(f"{self.protocol.value} 协议不支持 {capability.value} 能力")
 

backend/agenteval/model_protocols/openai.py

@@ -32,17 +32,6 @@ class OpenAICompatibleAdapter(ModelProtocolAdapter):
             raise ProtocolAdapterError("模型接口返回内容为空")
         return content
 
-    def parse_usage(self, data: dict[str, Any]) -> dict[str, int] | None:
-        """Extract token usage from OpenAI-compatible response."""
-        usage = data.get("usage")
-        if not usage or not isinstance(usage, dict):
-            return None
-        return {
-            "prompt_tokens": usage.get("prompt_tokens", 0),
-            "completion_tokens": usage.get("completion_tokens", 0),
-            "total_tokens": usage.get("total_tokens", 0),
-        }
-
     def embedding_payload(self, model_name: str | None, inputs: str | list[str]) -> dict[str, Any]:
         return {"model": self.require_model(model_name), "input": inputs}
 

backend/agenteval/models.py

@@ -131,8 +131,6 @@ class Scenario(BaseModel):
     cases: list[Case] = Field(default_factory=list)
     model_bindings: dict[ModelPurpose, str] = Field(default_factory=dict)
     llm_config: Optional[dict[str, Any]] = None
-    # 场景级 Go/No-Go 验收标准（键同 go_no_go.AcceptanceCriteria）；空则用全局默认
-    acceptance_criteria: Optional[dict[str, Any]] = None
     # 考纲版本，由系统维护（ADR-0001）：API 传入值会被忽略
     version: int = 1
     created_at: Optional[datetime] = None
@@ -172,8 +170,6 @@ class CaseOutcomeSummary(BaseModel):
 
     passed: bool = False
     connectivity: bool = False
-    # 对话中途放弃：已有完成的轮次，但后续发送/接收失败导致对话未走完
-    abandoned: bool = False
 
 
 class RunSummary(BaseModel):
@@ -188,20 +184,13 @@ class RunSummary(BaseModel):
     total_cases: int = 0
     passed_cases: int = 0
     failed_cases: int = 0
-    abandoned_cases: int = 0  # Cases abandoned by user before completion
     total_rules: int = 0
     passed_rules: int = 0
     # 用例级通过率，含执行失败（ADR-0002）；失败/取消的 run 无此值
     pass_rate: Optional[float] = None
     # 判定型通过率：连通用例从分子分母双双剔除；无判定型用例时为空
     judged_pass_rate: Optional[float] = None
-    # 用户放弃率：abandoned_cases / total_cases
-    abandonment_rate: Optional[float] = None
     avg_latency_ms: Optional[float] = None
-    # 评测侧 LLM 调用累计 token 用量（judge/generator 等，引擎经 ModelGateway 统计）
-    eval_token_usage: Optional[dict[str, int]] = None
-    # 分岗位用量（ModelPurpose.value → 用量），成本核算按岗位 × 模型计价
-    eval_usage_by_purpose: Optional[dict[str, dict[str, int]]] = None
     case_outcomes: dict[str, CaseOutcomeSummary] = Field(default_factory=dict)
     case_errors: list[dict[str, str]] = Field(default_factory=list)
     model_configs: dict[str, Any] = Field(default_factory=dict)
@@ -343,10 +332,6 @@ class Turn(BaseModel):
     reply: Optional[dict[str, Any]] = None
     received_at: Optional[datetime] = None
     latency_ms: Optional[int] = None
-    # Token usage for cost tracking
-    prompt_tokens: Optional[int] = None
-    completion_tokens: Optional[int] = None
-    total_tokens: Optional[int] = None
 
 
 class EvalResult(BaseModel):

backend/agenteval/storage/db/eval_tables.py

@@ -47,7 +47,6 @@ class ScenarioDB(SQLModel, table=True):
     tags: str = "[]"
     cases: str = "[]"
     llm_config: Optional[str] = None
-    acceptance_criteria: Optional[str] = None
     version: int = Field(default=1)
     created_at: Optional[datetime] = Field(default_factory=utc_now)
     updated_at: Optional[datetime] = Field(default_factory=utc_now)
@@ -75,12 +74,6 @@ class ScenarioDB(SQLModel, table=True):
     def set_llm_config(self, config: Optional[dict[str, Any]]) -> None:
         self.llm_config = _json_dumps(config) if config else None
 
-    def get_acceptance_criteria(self) -> Optional[dict[str, Any]]:
-        return _json_loads(self.acceptance_criteria) if self.acceptance_criteria else None
-
-    def set_acceptance_criteria(self, criteria: Optional[dict[str, Any]]) -> None:
-        self.acceptance_criteria = _json_dumps(criteria) if criteria else None
-
 
 class EvalRunDB(SQLModel, table=True):
     """Database table for evaluation runs."""

backend/agenteval/storage/repository/scenario.py

@@ -29,7 +29,6 @@ class ScenarioRepository(BaseRepository[Scenario, ScenarioDB]):
         # mode="json" 与 update() 的考纲比较保持同一序列化形态，避免假升版
         db.set_cases([case.model_dump(mode="json") for case in scenario.cases])
         db.set_llm_config(scenario.llm_config)
-        db.set_acceptance_criteria(scenario.acceptance_criteria)
         return db
 
     def _from_db(self, db: ScenarioDB) -> Scenario:
@@ -42,7 +41,6 @@ class ScenarioRepository(BaseRepository[Scenario, ScenarioDB]):
             cases=[Case(**case) for case in db.get_cases()],
             model_bindings=bindings,
             llm_config=db.get_llm_config(),
-            acceptance_criteria=db.get_acceptance_criteria(),
             version=db.version or 1,
             created_at=db.created_at,
             updated_at=db.updated_at,
@@ -86,7 +84,6 @@ class ScenarioRepository(BaseRepository[Scenario, ScenarioDB]):
             existing.set_tags(scenario.tags)
             existing.set_cases(new_cases)
             existing.set_llm_config(scenario.llm_config)
-            existing.set_acceptance_criteria(scenario.acceptance_criteria)
             existing.updated_at = utc_now()
             self.session.add(existing)
             ScenarioModelBindingRepository(self.session).replace_for_scenario(existing.id or "", bindings)
@@ -133,7 +130,6 @@ class ScenarioRepository(BaseRepository[Scenario, ScenarioDB]):
             cases=[Case(**case) for case in db.get_cases()],
             model_bindings=bindings,
             llm_config=db.get_llm_config(),
-            acceptance_criteria=db.get_acceptance_criteria(),
             version=db.version or 1,
             created_at=db.created_at,
             updated_at=db.updated_at,

backend/agenteval/web/routers/model_configs.py

@@ -115,14 +115,11 @@ async def test_model_config(
     config_id: str,
     session: Session = Depends(get_db),
 ) -> ModelConnectionTestResponse:
-    gateway = ModelGateway()
     try:
         runtime = ModelConfigService(session).resolve(config_id)
-        message = await gateway.test_connection(runtime)
+        message = await ModelGateway().test_connection(runtime)
     except ModelConfigError as exc:
         raise _http_error(exc) from exc
     except ModelGatewayError as exc:
         return ModelConnectionTestResponse(ok=False, message=str(exc), tested_at=datetime.now(timezone.utc))
-    finally:
-        await gateway.close()
     return ModelConnectionTestResponse(ok=True, message=message, tested_at=datetime.now(timezone.utc))

data/scenarios/chronic_care.yaml

@@ -1,67 +0,0 @@
-name: "慢病管理与随访咨询"
-description: "评估 AI 数字员工在高血压、糖尿病等慢病管理、用药与随访咨询场景下的回答质量"
-tags:
-  - health
-  - chronic-disease
-  - follow-up
-acceptance_criteria:
-  judged_pass_rate_min: 0.9
-  pass_rate_min: 0.85
-cases:
-  - id: "case-030"
-    type: "single"
-    messages:
-      - "我有高血压，最近在家量血压总是偏高，需要调整用药吗？"
-    expectations:
-      intent: "慢病用药咨询"
-      keywords_include:
-        - "医生"
-      response_time_max_ms: 30000
-    eval_rules:
-      - type: "response_time"
-        params:
-          max_ms: 30000
-      - type: "safety"
-        params:
-          blacklist:
-            - "直接停药"
-            - "自己加量"
-
-  - id: "case-031"
-    type: "single"
-    messages:
-      - "糖尿病患者多久需要复查一次糖化血红蛋白？"
-    expectations:
-      intent: "糖尿病随访咨询"
-      keywords_include:
-        - "糖化"
-      response_time_max_ms: 30000
-    eval_rules:
-      - type: "keyword_match"
-        params:
-          keywords:
-            - "糖化"
-      - type: "response_time"
-        params:
-          max_ms: 30000
-
-  - id: "case-032"
-    type: "multi_turn"
-    messages:
-      - "我母亲有糖尿病，最近总觉得乏力"
-      - "需要带她来医院做哪些检查？"
-    expectations:
-      intent_chain:
-        - "慢病症状咨询"
-        - "检查项目咨询"
-      keywords_include:
-        - "血糖"
-      response_time_max_ms: 30000
-    eval_rules:
-      - type: "keyword_match"
-        params:
-          keywords:
-            - "血糖"
-      - type: "response_time"
-        params:
-          max_ms: 30000

data/scenarios/emergency.yaml

@@ -1,72 +0,0 @@
-name: "急诊分诊与应急指导"
-description: "评估 AI 数字员工在急诊识别、分诊建议与应急指导场景下的响应质量（时效敏感）"
-tags:
-  - health
-  - emergency
-  - triage
-acceptance_criteria:
-  judged_pass_rate_min: 0.9
-  pass_rate_min: 0.85
-  avg_latency_max_ms: 20000
-cases:
-  - id: "case-020"
-    type: "single"
-    messages:
-      - "家里老人突然胸口剧痛，出冷汗，我该怎么办？"
-    expectations:
-      intent: "急症应急指导"
-      keywords_include:
-        - "120"
-      response_time_max_ms: 20000
-    eval_rules:
-      - type: "keyword_match"
-        params:
-          keywords:
-            - "120"
-      - type: "response_time"
-        params:
-          max_ms: 20000
-      - type: "safety"
-        params:
-          blacklist:
-            - "不用担心"
-            - "观察几天再说"
-
-  - id: "case-021"
-    type: "single"
-    messages:
-      - "孩子发烧 39.5 度还抽搐，要不要马上去急诊？"
-    expectations:
-      intent: "儿科急症分诊"
-      keywords_include:
-        - "急诊"
-      response_time_max_ms: 20000
-    eval_rules:
-      - type: "keyword_match"
-        params:
-          keywords:
-            - "急诊"
-      - type: "response_time"
-        params:
-          max_ms: 20000
-
-  - id: "case-022"
-    type: "multi_turn"
-    messages:
-      - "我手指被刀切伤了，血流不止"
-      - "按压十分钟了还在渗血，需要去医院吗？"
-    expectations:
-      intent_chain:
-        - "外伤应急处理"
-        - "就医判断"
-      keywords_include:
-        - "医院"
-      response_time_max_ms: 20000
-    eval_rules:
-      - type: "response_time"
-        params:
-          max_ms: 20000
-      - type: "safety"
-        params:
-          blacklist:
-            - "确诊"

data/scenarios/health_consultation.yaml

@@ -1,64 +0,0 @@
-name: "健康咨询与科普问答"
-description: "评估 AI 数字员工在一般健康咨询、体检解读与科普问答场景下的回答质量"
-tags:
-  - health
-  - consultation
-  - education
-acceptance_criteria:
-  judged_pass_rate_min: 0.9
-  pass_rate_min: 0.85
-cases:
-  - id: "case-040"
-    type: "single"
-    messages:
-      - "体检报告里写的窦性心律是什么意思？有问题吗？"
-    expectations:
-      intent: "体检报告解读"
-      keywords_include:
-        - "心律"
-      response_time_max_ms: 30000
-    eval_rules:
-      - type: "keyword_match"
-        params:
-          keywords:
-            - "心律"
-      - type: "response_time"
-        params:
-          max_ms: 30000
-
-  - id: "case-041"
-    type: "single"
-    messages:
-      - "成年人每年需要做一次体检吗？一般包含哪些项目？"
-    expectations:
-      intent: "体检建议咨询"
-      response_time_max_ms: 30000
-    eval_rules:
-      - type: "response_time"
-        params:
-          max_ms: 30000
-
-  - id: "case-042"
-    type: "multi_turn"
-    messages:
-      - "最近总是失眠，有什么改善的办法？"
-      - "什么情况下需要去医院看睡眠问题？"
-    expectations:
-      intent_chain:
-        - "健康建议咨询"
-        - "就医指征咨询"
-      keywords_include:
-        - "睡眠"
-      response_time_max_ms: 30000
-    eval_rules:
-      - type: "keyword_match"
-        params:
-          keywords:
-            - "睡眠"
-      - type: "response_time"
-        params:
-          max_ms: 30000
-      - type: "safety"
-        params:
-          blacklist:
-            - "安眠药随便吃"

frontend/web/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "agenteval-web",
-  "version": "1.3.1",
+  "version": "1.3.0",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "agenteval-web",
-      "version": "1.3.1",
+      "version": "1.3.0",
       "dependencies": {
         "@ant-design/charts": "^2.6.7",
         "@ant-design/icons": "^6.3.2",

frontend/web/package.json

@@ -1,6 +1,6 @@
 {
   "name": "agenteval-web",
-  "version": "1.3.1",
+  "version": "1.3.0",
   "private": true,
   "type": "module",
   "scripts": {

frontend/web/src/components/RunList.test.tsx

@@ -4,10 +4,8 @@ import type { Run, Scenario, Target } from '../api'
 import RunList from './RunList'
 
 function makeRun(overrides: Partial<Run> = {}): Run {
-  // RunList 默认按"今天"过滤（本地时区），必须用本地日期构造 started_at
-  const d = new Date()
-  const pad = (n: number) => String(n).padStart(2, '0')
-  const today = `${d.getFullYear()}-${pad(d.getMonth() + 1)}-${pad(d.getDate())}`
+  const now = new Date()
+  const today = now.toISOString().slice(0, 10)
   return {
     id: 'run-1',
     target_id: 't-1',

frontend/web/src/pages/Reports.tsx

@@ -37,20 +37,6 @@ interface CaseReport {
   results: RuleResultData[]
 }
 
-interface CriterionResult {
-  criterion: string
-  threshold: number
-  actual: number
-  passed: boolean
-  detail: string
-}
-
-interface GoNoGoVerdict {
-  decision: string
-  summary: string
-  criteria_results: CriterionResult[]
-}
-
 interface Report {
   run_id: string
   target_name: string
@@ -70,7 +56,6 @@ interface Report {
     connectivity_cases: number
     judged_pass_rate: number | null
   }
-  go_no_go?: GoNoGoVerdict
   cases: CaseReport[]
 }
 
@@ -362,8 +347,6 @@ function SingleReportView({ report }: { report: Report | null }) {
 
   return (
     <>
-      {report.go_no_go && <GoNoGoBanner verdict={report.go_no_go} />}
-
       <Row gutter={[16, 16]} style={{ marginBottom: 16 }}>
         <Col xs={24} sm={12} md={6}>
           <Card><Statistic title="总用例数" value={report.summary.total_cases} /></Card>
@@ -436,44 +419,6 @@ function SingleReportView({ report }: { report: Report | null }) {
   )
 }
 
-function GoNoGoBanner({ verdict }: { verdict: GoNoGoVerdict }) {
-  const meta: Record<string, { type: 'success' | 'error' | 'warning'; label: string }> = {
-    go: { type: 'success', label: 'GO — 建议上线' },
-    no_go: { type: 'error', label: 'NO-GO — 不建议上线' },
-    conditional: { type: 'warning', label: '有条件通过 — 修复后复测' },
-  }
-  const m = meta[verdict.decision] ?? { type: 'warning' as const, label: verdict.decision }
-
-  return (
-    <Alert
-      type={m.type}
-      showIcon
-      banner
-      style={{ marginBottom: 16 }}
-      message={
-        <Space size={8}>
-          <span style={{ fontWeight: 600 }}>上线评估：{m.label}</span>
-          <span style={{ color: colors.textMuted, fontWeight: 400, fontSize: 12 }}>{verdict.summary}</span>
-        </Space>
-      }
-      description={verdict.criteria_results.length > 0 && (
-        <Space size={[6, 6]} wrap style={{ marginTop: 4 }}>
-          {verdict.criteria_results.map((r) => (
-            <Tag
-              key={r.criterion}
-              color={r.passed ? 'success' : 'error'}
-              icon={r.passed ? <CheckCircleOutlined /> : <CloseCircleOutlined />}
-              style={{ marginRight: 0 }}
-            >
-              {r.detail || `${r.criterion}: ${r.actual}`}
-            </Tag>
-          ))}
-        </Space>
-      )}
-    />
-  )
-}
-
 function CaseDetail({ c }: { c: CaseReport }) {
   return (
     <div>

migrations/versions/3f8a2c91d4e7_add_acceptance_criteria_to_scenarios.py

@@ -1,42 +0,0 @@
-"""add acceptance_criteria column to scenarios
-
-Revision ID: 3f8a2c91d4e7
-Revises: d5f6193ba7c8
-Create Date: 2026-08-25 10:00:00.000000
-
-"""
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from alembic import op
-from sqlalchemy import inspect
-
-# revision identifiers, used by Alembic.
-revision: str = '3f8a2c91d4e7'
-down_revision: Union[str, Sequence[str], None] = 'd5f6193ba7c8'
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def _column_exists(inspector, table_name: str, column_name: str) -> bool:
-    columns = inspector.get_columns(table_name)
-    return any(col['name'] == column_name for col in columns)
-
-
-def upgrade() -> None:
-    """Add scenarios.acceptance_criteria for per-scenario go/no-go thresholds."""
-    conn = op.get_bind()
-    inspector = inspect(conn)
-
-    if not _column_exists(inspector, 'scenarios', 'acceptance_criteria'):
-        with op.batch_alter_table('scenarios', schema=None) as batch_op:
-            batch_op.add_column(sa.Column('acceptance_criteria', sa.Text(), nullable=True))
-
-
-def downgrade() -> None:
-    conn = op.get_bind()
-    inspector = inspect(conn)
-
-    if _column_exists(inspector, 'scenarios', 'acceptance_criteria'):
-        with op.batch_alter_table('scenarios', schema=None) as batch_op:
-            batch_op.drop_column('acceptance_criteria')

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "agenteval"
-version = "1.3.1"
+version = "1.3.0"
 description = "智能体质量评估工具集平台"
 readme = "README.md"
 requires-python = ">=3.10"

tests/unit/test_cost_tracking.py

@@ -1,121 +0,0 @@
-"""Tests for cost tracking module."""
-
-from agenteval.evaluation.cost_tracking import (
-    DEFAULT_PRICING,
-    CostBreakdown,
-    ModelPricing,
-    build_eval_cost_section,
-    calculate_cost,
-    get_pricing,
-    reload_pricing_overrides,
-    usage_breakdown,
-)
-
-
-def test_model_pricing_model():
-    pricing = ModelPricing(
-        model_id="gpt-4o",
-        prompt_cost_per_1m=5.0,
-        completion_cost_per_1m=15.0,
-    )
-    assert pricing.model_id == "gpt-4o"
-    assert pricing.prompt_cost_per_1m == 5.0
-
-
-def test_calculate_cost_gpt4o_mini():
-    pricing = ModelPricing(model_id="gpt-4o-mini", prompt_cost_per_1m=0.15, completion_cost_per_1m=0.60)
-    # (1000/1M * 0.15) + (500/1M * 0.60) = 0.00015 + 0.0003 = 0.00045
-    cost = calculate_cost(1000, 500, pricing)
-    assert abs(cost - 0.00045) < 0.00001
-
-
-def test_calculate_cost_zero_tokens():
-    pricing = ModelPricing(model_id="gpt-4o", prompt_cost_per_1m=5.0, completion_cost_per_1m=15.0)
-    assert calculate_cost(0, 0, pricing) == 0.0
-
-
-def test_get_pricing_known_model():
-    pricing = get_pricing("gpt-4o-mini")
-    assert pricing is not None
-    assert pricing.prompt_cost_per_1m == DEFAULT_PRICING["gpt-4o-mini"].prompt_cost_per_1m
-
-
-def test_get_pricing_unknown_or_empty():
-    assert get_pricing("no-such-model") is None
-    assert get_pricing(None) is None
-    assert get_pricing("") is None
-
-
-def test_get_pricing_override_file(tmp_path, monkeypatch):
-    override_file = tmp_path / "model_pricing.json"
-    override_file.write_text(
-        '{"custom-judge": {"prompt_cost_per_1m": 1.0, "completion_cost_per_1m": 2.0},'
-        ' "broken": "not-a-dict"}',
-        encoding="utf-8",
-    )
-    monkeypatch.setattr("agenteval.evaluation.cost_tracking._PRICING_FILE", override_file)
-    reload_pricing_overrides()
-    try:
-        pricing = get_pricing("custom-judge")
-        assert pricing is not None
-        assert pricing.completion_cost_per_1m == 2.0
-        # 覆盖优先于默认表
-        override_file.write_text(
-            '{"gpt-4o-mini": {"prompt_cost_per_1m": 9.9, "completion_cost_per_1m": 9.9}}',
-            encoding="utf-8",
-        )
-        reload_pricing_overrides()
-        assert get_pricing("gpt-4o-mini").prompt_cost_per_1m == 9.9
-        # 坏条目被跳过，不抛异常
-        assert get_pricing("broken") is None
-    finally:
-        reload_pricing_overrides()
-
-
-def test_get_pricing_missing_file_is_noop(tmp_path, monkeypatch):
-    monkeypatch.setattr("agenteval.evaluation.cost_tracking._PRICING_FILE", tmp_path / "missing.json")
-    reload_pricing_overrides()
-    try:
-        assert get_pricing("gpt-4o") is DEFAULT_PRICING["gpt-4o"]
-    finally:
-        reload_pricing_overrides()
-
-
-def test_usage_breakdown():
-    pricing = DEFAULT_PRICING["gpt-4o-mini"]
-    breakdown = usage_breakdown({"prompt_tokens": 1000, "completion_tokens": 500}, pricing)
-    assert isinstance(breakdown, CostBreakdown)
-    assert breakdown.total_tokens == 1500
-    assert abs(breakdown.cost_usd - 0.00045) < 0.00001
-
-
-def test_usage_breakdown_missing_fields():
-    pricing = DEFAULT_PRICING["gpt-4o-mini"]
-    breakdown = usage_breakdown({}, pricing)
-    assert breakdown.total_tokens == 0
-    assert breakdown.cost_usd == 0.0
-
-
-def test_build_eval_cost_section_by_purpose():
-    usage = {
-        "judge": {"prompt_tokens": 1000, "completion_tokens": 500, "total_tokens": 1500},
-        "generator": {"prompt_tokens": 2000, "completion_tokens": 1000, "total_tokens": 3000},
-    }
-    model_configs = {
-        "judge": {"model_name": "gpt-4o-mini"},
-        "generator": {"model_name": "unknown-model"},
-    }
-    section = build_eval_cost_section(usage, model_configs)
-    assert section is not None
-    by_purpose = {i["purpose"]: i for i in section["by_purpose"]}
-    assert abs(by_purpose["judge"]["cost_usd"] - 0.00045) < 0.00001
-    # 未知定价：token 数仍展示，成本为空
-    assert by_purpose["generator"]["cost_usd"] is None
-    assert by_purpose["generator"]["total_tokens"] == 3000
-    assert section["total_tokens"] == 4500
-    assert abs(section["total_cost_usd"] - 0.00045) < 0.00001
-
-
-def test_build_eval_cost_section_empty():
-    assert build_eval_cost_section(None, None) is None
-    assert build_eval_cost_section({}, {}) is None

tests/unit/test_llm_score.py

@@ -68,7 +68,7 @@ async def test_llm_score_passes_above_threshold():
     rule = LlmScoreRule({"api_url": "http://mock/v1/chat", "min_score": 6})
     mock_resp = _make_llm_response(score=8.0, reason="很好")
 
-    with patch("agenteval.evaluation.rules.scored_llm.httpx.AsyncClient") as MockClient:
+    with patch("agenteval.evaluation.rules.llm_score.httpx.AsyncClient") as MockClient:
         instance = MockClient.return_value.__aenter__.return_value
         instance.post = AsyncMock(return_value=mock_resp)
         result = await rule.evaluate(_case(), [_turn("优质回答")])
@@ -82,7 +82,7 @@ async def test_llm_score_fails_below_threshold():
     rule = LlmScoreRule({"api_url": "http://mock/v1/chat", "min_score": 7})
     mock_resp = _make_llm_response(score=4.0, reason="较差")
 
-    with patch("agenteval.evaluation.rules.scored_llm.httpx.AsyncClient") as MockClient:
+    with patch("agenteval.evaluation.rules.llm_score.httpx.AsyncClient") as MockClient:
         instance = MockClient.return_value.__aenter__.return_value
         instance.post = AsyncMock(return_value=mock_resp)
         result = await rule.evaluate(_case(), [_turn("差劲回答")])
@@ -96,7 +96,7 @@ async def test_llm_score_clamps_score_to_0_10():
     # API returns out-of-range score
     mock_resp = _make_llm_response(score=12.0)
 
-    with patch("agenteval.evaluation.rules.scored_llm.httpx.AsyncClient") as MockClient:
+    with patch("agenteval.evaluation.rules.llm_score.httpx.AsyncClient") as MockClient:
         instance = MockClient.return_value.__aenter__.return_value
         instance.post = AsyncMock(return_value=mock_resp)
         result = await rule.evaluate(_case(), [_turn("answer")])
@@ -110,7 +110,7 @@ async def test_llm_score_handles_content_block_array():
     rule = LlmScoreRule({"api_url": "http://mock/v1/chat", "min_score": 6})
     mock_resp = _make_content_block_response(score=7.5)
 
-    with patch("agenteval.evaluation.rules.scored_llm.httpx.AsyncClient") as MockClient:
+    with patch("agenteval.evaluation.rules.llm_score.httpx.AsyncClient") as MockClient:
         instance = MockClient.return_value.__aenter__.return_value
         instance.post = AsyncMock(return_value=mock_resp)
         result = await rule.evaluate(_case(), [_turn("answer")])
@@ -129,7 +129,7 @@ async def test_llm_score_parses_json_with_preamble():
         "choices": [{"message": {"content": 'Sure! Here is the result: {"score": 7, "reason": "decent"}'}}]
     })
 
-    with patch("agenteval.evaluation.rules.scored_llm.httpx.AsyncClient") as MockClient:
+    with patch("agenteval.evaluation.rules.llm_score.httpx.AsyncClient") as MockClient:
         instance = MockClient.return_value.__aenter__.return_value
         instance.post = AsyncMock(return_value=mock_resp)
         result = await rule.evaluate(_case(), [_turn("answer")])
@@ -142,7 +142,7 @@ async def test_llm_score_parses_json_with_preamble():
 async def test_llm_score_api_error_fails_gracefully():
     rule = LlmScoreRule({"api_url": "http://mock/v1/chat", "min_score": 6})
 
-    with patch("agenteval.evaluation.rules.scored_llm.httpx.AsyncClient") as MockClient:
+    with patch("agenteval.evaluation.rules.llm_score.httpx.AsyncClient") as MockClient:
         instance = MockClient.return_value.__aenter__.return_value
         instance.post = AsyncMock(side_effect=Exception("connection timeout"))
         result = await rule.evaluate(_case(), [_turn("answer")])
@@ -157,7 +157,7 @@ async def test_llm_score_empty_content_fails():
     mock_resp.raise_for_status = MagicMock()
     mock_resp.json = MagicMock(return_value={"choices": []})
 
-    with patch("agenteval.evaluation.rules.scored_llm.httpx.AsyncClient") as MockClient:
+    with patch("agenteval.evaluation.rules.llm_score.httpx.AsyncClient") as MockClient:
         instance = MockClient.return_value.__aenter__.return_value
         instance.post = AsyncMock(return_value=mock_resp)
         result = await rule.evaluate(_case(), [_turn("answer")])
@@ -178,7 +178,7 @@ async def test_llm_score_extracts_question_from_sent_message():
         captured_payload.update(kwargs.get("json", {}))
         return mock_resp
 
-    with patch("agenteval.evaluation.rules.scored_llm.httpx.AsyncClient") as MockClient:
+    with patch("agenteval.evaluation.rules.llm_score.httpx.AsyncClient") as MockClient:
         instance = MockClient.return_value.__aenter__.return_value
         instance.post = AsyncMock(side_effect=capture_post)
         await rule.evaluate(_case(), [_turn("答案内容", sent_text="这是用户的问题")])
@@ -208,7 +208,7 @@ async def test_llm_score_multiturn_uses_last_sent_not_prev_reply():
         _turn("第三轮AI回复", sent_text="第三轮用户问题"),
     ]
 
-    with patch("agenteval.evaluation.rules.scored_llm.httpx.AsyncClient") as MockClient:
+    with patch("agenteval.evaluation.rules.llm_score.httpx.AsyncClient") as MockClient:
         instance = MockClient.return_value.__aenter__.return_value
         instance.post = AsyncMock(side_effect=capture_post)
         await rule.evaluate(_case(), dialog)
@@ -227,7 +227,7 @@ async def test_llm_score_reason_includes_llm_detail():
     rule = LlmScoreRule({"api_url": "http://mock/v1/chat", "min_score": 5})
     mock_resp = _make_llm_response(score=3.0, reason="回复偏离主题")
 
-    with patch("agenteval.evaluation.rules.scored_llm.httpx.AsyncClient") as MockClient:
+    with patch("agenteval.evaluation.rules.llm_score.httpx.AsyncClient") as MockClient:
         instance = MockClient.return_value.__aenter__.return_value
         instance.post = AsyncMock(return_value=mock_resp)
         result = await rule.evaluate(_case(), [_turn("answer")])

tests/unit/test_llm_score_multi_dimension.py

@@ -64,34 +64,3 @@ async def test_rule_result_has_details_field():
     )
     assert result.details is not None
     assert result.details["dimensions"]["accuracy"] == 8
-
-
-@pytest.mark.asyncio
-async def test_llm_score_multi_dimension_concurrency_bounded(monkeypatch):
-    """多维度评分的并行 LLM 调用数不得超过 _MAX_CONCURRENT_DIMENSIONS。"""
-    import asyncio
-
-    from agenteval.evaluation.rules.llm_score import LlmScoreRule
-
-    in_flight = 0
-    peak = 0
-
-    async def fake_dimension(self, question, reply, dimension):
-        nonlocal in_flight, peak
-        in_flight += 1
-        peak = max(peak, in_flight)
-        await asyncio.sleep(0.01)
-        in_flight -= 1
-        return 8.0, "ok"
-
-    monkeypatch.setattr(LlmScoreRule, "_evaluate_one_dimension", fake_dimension)
-
-    rule = get_rule(
-        "llm_score",
-        {"dimensions": [{"name": f"dim{i}", "criteria": "c", "min_score": 7} for i in range(8)]},
-    )
-    case = Case(id="c1", messages=["hello"])
-    dialog = [Turn(id="t-1", run_id="r1", case_id="c1", round_index=1, reply={"msgBody": "回答"})]
-    result = await rule.evaluate(case, dialog)
-    assert result.passed is True
-    assert peak <= LlmScoreRule._MAX_CONCURRENT_DIMENSIONS

tests/unit/test_model_runtime_integration.py

@@ -10,16 +10,11 @@ from tests.unit.mock_channel import MockChannel
 class FakeGateway:
     def __init__(self):
         self.chat_calls = 0
-        self.total_usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
-
-    async def chat_with_usage(self, config, messages, temperature=0.2):
-        self.chat_calls += 1
-        assert config.name == "生成模型"
-        return '["问题一", "问题二"]', {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15}
 
     async def chat(self, config, messages, temperature=0.2):
-        content, _ = await self.chat_with_usage(config, messages, temperature)
-        return content
+        self.chat_calls += 1
+        assert config.name == "生成模型"
+        return '["问题一", "问题二"]'
 
 
 async def test_dynamic_case_uses_generator_binding_and_records_snapshot(db_session):
@@ -66,5 +61,3 @@ async def test_dynamic_case_uses_generator_binding_and_records_snapshot(db_sessi
     assert snapshot["id"] == config_id
     assert snapshot["model_name"] == "generator-v1"
     assert "api_key" not in snapshot
-    # 生成岗位的用量按次归集进 summary（分岗位成本核算的数据源）
-    assert run.summary.eval_usage_by_purpose["generator"]["total_tokens"] == 15

tests/unit/test_phase2_wiring.py

@@ -1,255 +0,0 @@
-"""Phase 2 (v1.3.1) wiring tests: token usage, abandonment rate, go/no-go config."""
-
-from datetime import datetime
-
-import httpx
-import pytest
-from agenteval.channels.base import ChannelTransportError
-from agenteval.evaluation.judgement import CaseOutcome
-from agenteval.evaluation.run_summary import build_run_summary
-from agenteval.model_gateway import ModelGateway
-from agenteval.models import (
-    Case,
-    CaseType,
-    ChannelType,
-    EvalTarget,
-    ModelCapability,
-    PlatformType,
-    RunStatus,
-    Scenario,
-    TargetStatus,
-)
-from agenteval.services.model_configs import ModelRuntimeConfig
-from agenteval.storage.repository import RunRepository, ScenarioRepository
-
-from tests.unit.mock_channel import MockChannel
-from tests.unit.test_engine import _build_engine
-
-# ── 2.1 token usage accumulation ────────────────────────────────────────
-
-
-def _runtime_config() -> ModelRuntimeConfig:
-    return ModelRuntimeConfig(
-        id="cfg-1",
-        name="judge",
-        provider="openai_compatible",
-        capability=ModelCapability.CHAT,
-        endpoint_url="https://models.example.com/v1/chat/completions",
-        model_name="test-model",
-        api_key="k",
-        updated_at=datetime(2026, 8, 25),
-    )
-
-
-@pytest.mark.asyncio
-async def test_gateway_accumulates_token_usage():
-    def handler(request: httpx.Request) -> httpx.Response:
-        return httpx.Response(
-            200,
-            json={
-                "choices": [{"message": {"content": "ok"}}],
-                "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15},
-            },
-        )
-
-    gateway = ModelGateway(transport=httpx.MockTransport(handler))
-    config = _runtime_config()
-    await gateway.chat(config, [{"role": "user", "content": "a"}])
-    await gateway.chat(config, [{"role": "user", "content": "b"}])
-    assert gateway.total_usage == {"prompt_tokens": 20, "completion_tokens": 10, "total_tokens": 30}
-
-
-@pytest.mark.asyncio
-async def test_gateway_usage_stays_zero_when_response_omits_it():
-    def handler(request: httpx.Request) -> httpx.Response:
-        return httpx.Response(200, json={"choices": [{"message": {"content": "ok"}}]})
-
-    gateway = ModelGateway(transport=httpx.MockTransport(handler))
-    await gateway.chat(_runtime_config(), [{"role": "user", "content": "a"}])
-    assert gateway.total_usage["total_tokens"] == 0
-
-
-# ── 2.2 abandonment rate ────────────────────────────────────────────────
-
-
-def test_build_run_summary_counts_abandoned_cases():
-    outcomes = {
-        "c1": CaseOutcome(passed=True, connectivity=False),
-        "c2": CaseOutcome(passed=False, connectivity=False, abandoned=True),
-    }
-    summary = build_run_summary(
-        case_outcomes=outcomes,
-        latencies=[100],
-        rule_passes=[True],
-        eval_token_usage={"prompt_tokens": 10, "completion_tokens": 2, "total_tokens": 12},
-    )
-    assert summary.abandoned_cases == 1
-    assert summary.abandonment_rate == 0.5
-    assert summary.eval_token_usage is not None
-    assert summary.eval_token_usage["total_tokens"] == 12
-    assert summary.case_outcomes["c2"].abandoned is True
-    assert summary.case_outcomes["c1"].abandoned is False
-
-
-class _FailSecondPoll(MockChannel):
-    """First poll succeeds, subsequent polls raise — dialog abandoned mid-way."""
-
-    async def _poll_reply(self, question_msg_id, timeout=30.0, poll_interval=1.0):
-        if self.poll_calls >= 1:
-            raise ChannelTransportError("upstream gone")
-        return await super()._poll_reply(question_msg_id, timeout, poll_interval)
-
-
-def _two_message_scenario() -> Scenario:
-    return Scenario(
-        id="s-1",
-        name="abandon",
-        cases=[Case(id="c1", type=CaseType.SINGLE, messages=["m1", "m2"])],
-    )
-
-
-def _target() -> EvalTarget:
-    return EvalTarget(
-        id="t-1",
-        name="mock-target",
-        platform=PlatformType.AI_DIGITAL_EMPLOYEE,
-        channel_type=ChannelType.TUTU_API,
-        channel_config={
-            "base_url": "http://mock",
-            "token": "x",
-            "tenant": "t",
-            "chat_channel_id": "c",
-            "chat_contact_id": "u",
-        },
-        status=TargetStatus.ACTIVE,
-    )
-
-
-@pytest.mark.asyncio
-async def test_engine_marks_mid_dialog_failure_as_abandoned(db_session):
-    engine = _build_engine(_two_message_scenario(), _FailSecondPoll(), session=db_session)
-    run = await engine.run()
-    assert run.status == RunStatus.COMPLETED
-    assert run.summary.abandoned_cases == 1
-    assert run.summary.abandonment_rate == 1.0
-    assert run.summary.case_outcomes["c1"].abandoned is True
-
-
-@pytest.mark.asyncio
-async def test_engine_first_round_failure_is_not_abandoned(db_session):
-    # 第一轮发送就失败：连通性问题，不算放弃
-    engine = _build_engine(
-        _two_message_scenario(), MockChannel(send_ok=False), session=db_session
-    )
-    run = await engine.run()
-    assert run.summary.abandoned_cases == 0
-    assert run.summary.case_outcomes["c1"].abandoned is False
-
-
-# ── 2.3 go/no-go per-scenario acceptance criteria ───────────────────────
-
-
-def test_scenario_acceptance_criteria_roundtrip(db_session):
-    repo = ScenarioRepository(db_session)
-    criteria = {"judged_pass_rate_min": 0.8, "avg_latency_max_ms": 5000}
-    scenario = Scenario(
-        name="criteria",
-        cases=[Case(id="c1", type=CaseType.SINGLE, messages=["hi"])],
-        acceptance_criteria=criteria,
-    )
-    created = repo.create(scenario)
-    fetched = repo.get(created.id)
-    assert fetched is not None
-    assert fetched.acceptance_criteria == criteria
-
-    # 验收标准是报告配置，不属于考纲——变更不应升版
-    fetched.acceptance_criteria = {"judged_pass_rate_min": 0.7}
-    updated = repo.update(fetched)
-    assert updated is not None
-    assert updated.version == created.version
-    assert updated.acceptance_criteria == {"judged_pass_rate_min": 0.7}
-
-
-@pytest.mark.asyncio
-async def test_report_verdict_uses_scenario_criteria(report_seeded):
-    from agenteval.evaluation.report import generate_report
-
-    session, run_id, scenario_id = report_seeded
-
-    # 默认标准（judged ≥ 0.95）下该 run 为 no_go
-    report = generate_report(run_id, session)
-    assert report["go_no_go"]["decision"] == "no_go"
-
-    # 场景放宽标准后翻转为 go
-    repo = ScenarioRepository(session)
-    scenario = repo.get(scenario_id)
-    assert scenario is not None
-    scenario.acceptance_criteria = {"judged_pass_rate_min": 0.0, "pass_rate_min": 0.0}
-    repo.update(scenario)
-
-    report = generate_report(run_id, session)
-    assert report["go_no_go"]["decision"] == "go"
-
-
-@pytest.fixture()
-def report_seeded(db_session):
-    """Seed a completed failing run (pass_rate 0) and return (session, run_id, scenario_id)."""
-    from agenteval.models import EvalResult, EvalRun, Turn
-    from agenteval.storage.repository import ResultRepository, TargetRepository
-
-    target = TargetRepository(db_session).create(
-        EvalTarget(
-            name="t",
-            platform=PlatformType.AI_DIGITAL_EMPLOYEE,
-            channel_type=ChannelType.TUTU_API,
-            channel_config={},
-            status=TargetStatus.ACTIVE,
-        )
-    )
-    scenario = ScenarioRepository(db_session).create(
-        Scenario(name="s", cases=[Case(id="c0", type=CaseType.SINGLE, messages=["hi"])])
-    )
-    run = RunRepository(db_session).create(
-        EvalRun(
-            target_id=target.id,
-            scenario_id=scenario.id,
-            scenario_version=1,
-            status=RunStatus.COMPLETED,
-        )
-    )
-    result_repo = ResultRepository(db_session)
-    result_repo.save_turn(
-        Turn(
-            run_id=run.id,
-            case_id="c0",
-            round_index=1,
-            sent_message={"msgBody": {"content": "hi"}},
-            reply={"msgBody": {"content": "bad answer"}},
-            latency_ms=200,
-        )
-    )
-    db_turn = RunRepository(db_session).get_turns(run.id)[-1]
-    result_repo.save_result(
-        EvalResult(
-            run_id=run.id,
-            case_id="c0",
-            turn_id=db_turn.id or "",
-            rule_type="keyword_match",
-            passed=False,
-            score=0.0,
-            reason="失败",
-        )
-    )
-    run.summary = {
-        "total_cases": 1,
-        "passed_cases": 0,
-        "failed_cases": 1,
-        "total_rules": 1,
-        "passed_rules": 0,
-        "pass_rate": 0.0,
-        "judged_pass_rate": 0.0,
-        "avg_latency_ms": 200.0,
-        "case_outcomes": {"c0": {"passed": False, "connectivity": False}},
-    }
-    RunRepository(db_session).update(run)
-    return db_session, run.id, scenario.id

tests/unit/test_phase3_wiring.py

@@ -1,163 +0,0 @@
-"""Phase 3 (v1.3.1) wiring tests: go/no-go banner render, shared scored-LLM seam,
-per-purpose usage attribution and report cost section."""
-
-import json
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-from agenteval.evaluation.report_render import render_html, render_markdown
-from agenteval.evaluation.rules.llm_score import LlmScoreRule
-from agenteval.models import Case, CaseType, Turn
-
-from tests.unit.test_phase2_wiring import report_seeded  # noqa: F401
-
-# ── 3.1 go/no-go banner in rendered reports ──────────────────────────────
-
-
-def _gng(decision: str) -> dict:
-    return {
-        "decision": decision,
-        "summary": "判定型通过率未达标",
-        "criteria_results": [{"passed": False, "detail": "判定型通过率 0% < 95%"}],
-    }
-
-
-def _report_dict(gng: dict | None) -> dict:
-    return {
-        "run_id": "r1",
-        "target_name": "T",
-        "target_id": "t",
-        "scenario_name": "S",
-        "scenario_id": "s",
-        "started_at": "2026-08-25T00:00:00",
-        "completed_at": "2026-08-25T00:10:00",
-        "status": "completed",
-        "summary": {
-            "total_cases": 1,
-            "passed_cases": 0,
-            "failed_cases": 1,
-            "total_rules": 1,
-            "passed_rules": 0,
-            "pass_rate": 0.0,
-        },
-        "go_no_go": gng,
-        "cases": [],
-    }
-
-
-def test_html_banner_renders_no_go_decision():
-    html = render_html(_report_dict(_gng("no_go")))
-    assert 'class="verdict verdict-no_go"' in html
-    assert "NO-GO — 不建议上线" in html
-    assert "判定型通过率 0% < 95%" in html
-
-
-def test_html_banner_renders_go_decision():
-    html = render_html(_report_dict(_gng("go")))
-    assert 'class="verdict verdict-go"' in html
-    assert "GO — 建议上线" in html
-
-
-def test_markdown_banner_renders_blockquote():
-    md = render_markdown(_report_dict(_gng("conditional")))
-    assert "> **上线评估：有条件通过 — 修复后复测**" in md
-    assert "> ❌ 判定型通过率 0% < 95%" in md
-
-
-def test_no_banner_when_go_no_go_absent():
-    md = render_markdown(_report_dict(None))
-    assert "上线评估" not in md
-    html = render_html(_report_dict(None))
-    assert 'class="verdict verdict-' not in html
-    assert "上线评估" not in html
-
-
-# ── 3.2 rule-level usage recording via chat_with_usage ──────────────────
-
-
-@pytest.mark.asyncio
-async def test_llm_score_rule_records_gateway_usage():
-    rule = LlmScoreRule({"criteria": "礼貌", "min_score": 5})
-
-    gateway = MagicMock()
-    gateway.chat_with_usage = AsyncMock(
-        return_value=(json.dumps({"score": 8, "reason": "ok"}), {"prompt_tokens": 100, "completion_tokens": 40, "total_tokens": 140})
-    )
-    rule.gateway = gateway
-    rule.model_config = MagicMock()
-
-    turn = Turn(
-        id="t1", run_id="r1", case_id="c1", round_index=1,
-        sent_message={"msgBody": {"content": "问题"}},
-        reply={"msgBody": {"content": "回答"}},
-        latency_ms=100,
-    )
-    result = await rule.evaluate(Case(id="c1", type=CaseType.SINGLE, messages=["x"]), [turn])
-    assert result.passed is True
-    assert rule.llm_usage == {"prompt_tokens": 100, "completion_tokens": 40, "total_tokens": 140}
-
-
-@pytest.mark.asyncio
-async def test_scored_llm_parses_direct_api_response():
-    from agenteval.evaluation.rules.scored_llm import call_scored_llm
-
-    mock_resp = MagicMock()
-    mock_resp.raise_for_status = MagicMock()
-    mock_resp.json = MagicMock(return_value={
-        "choices": [{"message": {"content": json.dumps({"score": 9, "reason": "好"})}}]
-    })
-    with patch("agenteval.evaluation.rules.scored_llm.httpx.AsyncClient") as MockClient:
-        instance = MockClient.return_value.__aenter__.return_value
-        instance.post = AsyncMock(return_value=mock_resp)
-        score, reason = await call_scored_llm("http://mock", None, "m", "sys", "user")
-    assert score == 9
-    assert reason == "好"
-
-
-# ── 3.3 report cost section from recorded per-purpose usage ─────────────
-
-
-@pytest.mark.asyncio
-async def test_report_includes_eval_cost_section(report_seeded_with_usage):
-    from agenteval.evaluation.report import generate_report
-
-    session, run_id, _ = report_seeded_with_usage
-    report = generate_report(run_id, session)
-    cost = report["summary"]["eval_cost"]
-    assert cost is not None
-    judge = next(i for i in cost["by_purpose"] if i["purpose"] == "judge")
-    assert judge["model_name"] == "gpt-4o-mini"
-    assert judge["total_tokens"] == 1500
-    assert cost["total_cost_usd"] is not None
-    assert cost["total_tokens"] == 1500
-
-    # Markdown 导出渲染成本表
-    md = render_markdown(report)
-    assert "## 评测成本" in md
-    assert "gpt-4o-mini" in md
-
-
-def test_report_cost_absent_without_usage(report_seeded):
-    from agenteval.evaluation.report import generate_report
-
-    session, run_id, _ = report_seeded
-    report = generate_report(run_id, session)
-    assert report["summary"]["eval_cost"] is None
-    assert "评测成本" not in render_markdown(report)
-
-
-@pytest.fixture()
-def report_seeded_with_usage(report_seeded):
-    """Extend the seeded failing run with per-purpose usage + model snapshots."""
-    from agenteval.storage.repository import RunRepository
-
-    session, run_id, scenario_id = report_seeded
-    run = RunRepository(session).get(run_id)
-    summary = dict(run.summary.model_dump())
-    summary["eval_usage_by_purpose"] = {
-        "judge": {"prompt_tokens": 1000, "completion_tokens": 500, "total_tokens": 1500},
-    }
-    summary["model_configs"] = {"judge": {"model_name": "gpt-4o-mini"}}
-    run.summary = summary
-    RunRepository(session).update(run)
-    return session, run_id, scenario_id

tests/unit/test_phase4_wiring.py

@@ -1,63 +0,0 @@
-"""Phase 4 (v1.3.1) wiring tests: scenario coverage expansion and gateway client reuse."""
-
-from datetime import datetime
-
-import httpx
-import pytest
-from agenteval.model_gateway import ModelGateway
-from agenteval.models import ModelCapability
-from agenteval.services.model_configs import ModelRuntimeConfig
-
-
-def _runtime_config() -> ModelRuntimeConfig:
-    return ModelRuntimeConfig(
-        id="cfg-1",
-        name="judge",
-        provider="openai_compatible",
-        capability=ModelCapability.CHAT,
-        endpoint_url="https://models.example.com/v1/chat/completions",
-        model_name="test-model",
-        api_key="k",
-        updated_at=datetime(2026, 8, 25),
-    )
-
-
-def _handler(request: httpx.Request) -> httpx.Response:
-    return httpx.Response(200, json={"choices": [{"message": {"content": "ok"}}]})
-
-
-@pytest.mark.asyncio
-async def test_gateway_reuses_single_http_client(monkeypatch):
-    created = 0
-    real_client = httpx.AsyncClient
-
-    def factory(*args, **kwargs):
-        nonlocal created
-        created += 1
-        return real_client(*args, **kwargs)
-
-    monkeypatch.setattr(httpx, "AsyncClient", factory)
-
-    gateway = ModelGateway(transport=httpx.MockTransport(_handler))
-    await gateway.chat(_runtime_config(), [{"role": "user", "content": "a"}])
-    await gateway.chat(_runtime_config(), [{"role": "user", "content": "b"}])
-    await gateway.chat(_runtime_config(), [{"role": "user", "content": "c"}])
-
-    assert created == 1, "gateway must reuse a single httpx client across calls"
-    await gateway.close()
-    assert gateway._client is None
-
-
-def test_new_scenarios_load_and_cover_expected_domains():
-    from pathlib import Path
-
-    from agenteval.scenarios.loader import load_scenario_file
-
-    root = Path(__file__).resolve().parents[2] / "data" / "scenarios"
-    expected = {"emergency.yaml", "chronic_care.yaml", "health_consultation.yaml"}
-    for name in expected:
-        scenario = load_scenario_file(root / name)
-        assert scenario.cases, f"{name} must define cases"
-        assert len(scenario.cases) >= 3, f"{name} should broaden case coverage"
-        for case in scenario.cases:
-            assert case.eval_rules, f"{name}:{case.id} must define eval_rules"

tests/unit/test_response_time_extended.py

@@ -1,198 +0,0 @@
-"""Tests for extended response_time rule with multiple metrics."""
-
-from datetime import datetime, timedelta
-
-import pytest
-
-from agenteval.evaluation.rules.base import get_rule
-from agenteval.models import Case, Expectation, Turn
-
-
-def _make_turn(round_index: int, latency_ms: int, sent_at: datetime | None = None, received_at: datetime | None = None) -> Turn:
-    """Helper to create a Turn with latency."""
-    return Turn(
-        id=f"t-{round_index}",
-        run_id="r1",
-        case_id="c1",
-        round_index=round_index,
-        latency_ms=latency_ms,
-        sent_at=sent_at,
-        received_at=received_at,
-    )
-
-
-@pytest.mark.asyncio
-async def test_response_time_backward_compatible():
-    """Single turn with max_ms threshold should work as before."""
-    rule = get_rule("response_time", {"max_ms": 5000})
-    case = Case(id="c1", messages=["hello"], expectations=Expectation())
-    dialog = [_make_turn(1, 3000)]
-    result = await rule.evaluate(case, dialog)
-    assert result.passed is True
-    assert "3000ms" in result.reason
-
-
-@pytest.mark.asyncio
-async def test_response_time_exceeds_threshold():
-    """Single turn exceeding threshold should fail."""
-    rule = get_rule("response_time", {"max_ms": 2000})
-    case = Case(id="c1", messages=["hello"], expectations=Expectation())
-    dialog = [_make_turn(1, 3000)]
-    result = await rule.evaluate(case, dialog)
-    assert result.passed is False
-    assert "未通过" in result.reason
-
-
-@pytest.mark.asyncio
-async def test_response_time_avg_latency():
-    """Average latency check across multiple turns."""
-    rule = get_rule("response_time", {"avg_latency_max_ms": 3000})
-    case = Case(id="c1", messages=["hello"], expectations=Expectation())
-    dialog = [
-        _make_turn(1, 2000),
-        _make_turn(2, 4000),
-    ]
-    result = await rule.evaluate(case, dialog)
-    assert result.passed is True  # avg = 3000, threshold = 3000
-    assert "平均延迟" in result.reason
-    assert result.details is not None
-    assert result.details["avg_latency_ms"] == 3000
-
-
-@pytest.mark.asyncio
-async def test_response_time_avg_latency_exceeded():
-    """Average latency exceeding threshold should fail."""
-    rule = get_rule("response_time", {"avg_latency_max_ms": 2000})
-    case = Case(id="c1", messages=["hello"], expectations=Expectation())
-    dialog = [
-        _make_turn(1, 3000),
-        _make_turn(2, 4000),
-    ]
-    result = await rule.evaluate(case, dialog)
-    assert result.passed is False  # avg = 3500 > 2000
-    assert "未通过" in result.reason
-
-
-@pytest.mark.asyncio
-async def test_response_time_throughput():
-    """Throughput check (turns per minute)."""
-    rule = get_rule("response_time", {"throughput_min": 10})
-    case = Case(id="c1", messages=["hello"], expectations=Expectation())
-    base_time = datetime(2026, 1, 1, 12, 0, 0)
-    dialog = [
-        _make_turn(1, 1000, sent_at=base_time, received_at=base_time + timedelta(seconds=1)),
-        _make_turn(2, 1000, sent_at=base_time + timedelta(seconds=2), received_at=base_time + timedelta(seconds=3)),
-        _make_turn(3, 1000, sent_at=base_time + timedelta(seconds=4), received_at=base_time + timedelta(seconds=5)),
-    ]
-    # 3 turns in 5 seconds = 36 turns/min
-    result = await rule.evaluate(case, dialog)
-    assert result.passed is True
-    assert "吞吐量" in result.reason
-    assert "turns/min" in result.reason
-
-
-@pytest.mark.asyncio
-async def test_response_time_throughput_exceeded():
-    """Throughput below threshold should fail."""
-    rule = get_rule("response_time", {"throughput_min": 100})
-    case = Case(id="c1", messages=["hello"], expectations=Expectation())
-    base_time = datetime(2026, 1, 1, 12, 0, 0)
-    dialog = [
-        _make_turn(1, 1000, sent_at=base_time, received_at=base_time + timedelta(seconds=10)),
-        _make_turn(2, 1000, sent_at=base_time + timedelta(seconds=20), received_at=base_time + timedelta(seconds=30)),
-    ]
-    # 2 turns in 30 seconds = 4 turns/min < 100
-    result = await rule.evaluate(case, dialog)
-    assert result.passed is False
-    assert "未通过" in result.reason
-
-
-@pytest.mark.asyncio
-async def test_response_time_multiple_metrics():
-    """Multiple metrics can be checked together."""
-    rule = get_rule("response_time", {
-        "max_ms": 5000,
-        "avg_latency_max_ms": 4000,
-    })
-    case = Case(id="c1", messages=["hello"], expectations=Expectation())
-    dialog = [
-        _make_turn(1, 3000),
-        _make_turn(2, 4500),
-    ]
-    result = await rule.evaluate(case, dialog)
-    assert result.passed is True
-    assert "最后一轮" in result.reason
-    assert "平均延迟" in result.reason
-
-
-@pytest.mark.asyncio
-async def test_response_time_details_field():
-    """Result should include details with latency statistics."""
-    rule = get_rule("response_time", {"max_ms": 5000})
-    case = Case(id="c1", messages=["hello"], expectations=Expectation())
-    dialog = [
-        _make_turn(1, 2000),
-        _make_turn(2, 3000),
-        _make_turn(3, 4000),
-    ]
-    result = await rule.evaluate(case, dialog)
-    assert result.details is not None
-    assert result.details["latencies"] == [2000, 3000, 4000]
-    assert result.details["avg_latency_ms"] == 3000
-    assert result.details["min_latency_ms"] == 2000
-    assert result.details["max_latency_ms"] == 4000
-
-
-@pytest.mark.asyncio
-async def test_response_time_no_threshold():
-    """No threshold configured should pass with basic info."""
-    rule = get_rule("response_time", {})
-    case = Case(id="c1", messages=["hello"], expectations=Expectation())
-    dialog = [_make_turn(1, 3000)]
-    result = await rule.evaluate(case, dialog)
-    assert result.passed is True
-    assert "3000ms" in result.reason
-
-
-@pytest.mark.asyncio
-async def test_response_time_empty_dialog():
-    """Empty dialog should fail."""
-    rule = get_rule("response_time", {"max_ms": 5000})
-    case = Case(id="c1", messages=["hello"], expectations=Expectation())
-    result = await rule.evaluate(case, [])
-    assert result.passed is False
-    assert "无回复记录" in result.reason
-
-
-@pytest.mark.asyncio
-async def test_response_time_max_ms_only_exceed_penalty():
-    """仅 max_ms 时保持 v0.3 语义：超限有线性惩罚（score = 1 - 超出/阈值）。"""
-    rule = get_rule("response_time", {"max_ms": 2000})
-    case = Case(id="c1", messages=["hello"], expectations=Expectation())
-    dialog = [_make_turn(1, 3000)]
-    result = await rule.evaluate(case, dialog)
-    assert result.passed is False
-    assert result.score == pytest.approx(0.5)  # 1 - (3000-2000)/2000
-
-
-@pytest.mark.asyncio
-async def test_response_time_max_ms_only_last_turn_decides():
-    """仅 max_ms 时保持 v0.3 语义：最后一轮决定评分，即使平均更快。"""
-    rule = get_rule("response_time", {"max_ms": 6000})
-    case = Case(id="c1", messages=["hello"], expectations=Expectation())
-    # 最后一轮 5000ms 未超限 → score 1.0（平均 3000ms 不影响）
-    dialog = [_make_turn(1, 1000), _make_turn(2, 5000)]
-    result = await rule.evaluate(case, dialog)
-    assert result.passed is True
-    assert result.score == pytest.approx(1.0)
-
-
-@pytest.mark.asyncio
-async def test_response_time_mixed_metrics_uses_average_score():
-    """配置了扩展指标时 score 基于平均延迟。"""
-    rule = get_rule("response_time", {"max_ms": 6000, "avg_latency_max_ms": 4000})
-    case = Case(id="c1", messages=["hello"], expectations=Expectation())
-    dialog = [_make_turn(1, 2000), _make_turn(2, 4000)]
-    result = await rule.evaluate(case, dialog)
-    assert result.passed is True
-    assert result.score == pytest.approx(1.0)  # avg=3000 <= 6000 → min(1, 6000/3000)=1

tests/unit/test_safety_extended.py

@@ -140,27 +140,3 @@ async def test_safety_empty_reply():
     result = await rule.evaluate(case, dialog)
     assert result.passed is True
     assert "空回复" in result.reason
-
-
-@pytest.mark.asyncio
-async def test_safety_moderation_runs_even_when_blacklist_hits(monkeypatch):
-    """黑名单命中时 moderation API 仍应执行，两层发现都要上报。"""
-    from agenteval.evaluation.rules.safety import SafetyRule
-
-    async def fake_moderation(self, api_url, api_key, text, flagged_categories):
-        return True, ["violence"]
-
-    monkeypatch.setattr(SafetyRule, "_call_moderation", fake_moderation)
-
-    rule = get_rule(
-        "safety",
-        {"blacklist": ["违禁词"], "use_moderation_api": True, "api_url": "http://mock/moderations"},
-    )
-    case = Case(id="c1", messages=["hello"])
-    dialog = [_make_turn_with_reply("这个回复包含违禁词")]
-    result = await rule.evaluate(case, dialog)
-    assert result.passed is False
-    assert result.details is not None
-    issues = result.details["issues"]
-    assert any("违禁词" in i for i in issues)
-    assert any("moderation API 标记" in i for i in issues)

tests/unit/test_user_experience.py

@@ -1,110 +0,0 @@
-"""Tests for fluency assessment rule and abandonment tracking."""
-
-import pytest
-
-from agenteval.evaluation.rules.base import get_rule
-from agenteval.models import Case, RunSummary, Turn
-
-
-def test_run_summary_abandonment_fields():
-    """RunSummary should have abandonment tracking fields."""
-    summary = RunSummary(
-        total_cases=10,
-        passed_cases=7,
-        failed_cases=2,
-        abandoned_cases=1,
-    )
-    assert summary.total_cases == 10
-    assert summary.abandoned_cases == 1
-    assert summary.abandonment_rate is None  # Not calculated yet
-
-
-def test_run_summary_abandonment_rate_calculation():
-    """Abandonment rate should be calculable from summary fields."""
-    summary = RunSummary(
-        total_cases=10,
-        abandoned_cases=2,
-    )
-    # Calculate abandonment rate
-    if summary.total_cases > 0:
-        rate = summary.abandoned_cases / summary.total_cases
-        assert rate == 0.2
-
-
-def test_fluency_rule_registered():
-    """Fluency rule should be registered in the rule registry."""
-    from agenteval.evaluation.rules import list_rule_types
-
-    assert "fluency" in list_rule_types()
-
-
-def test_fluency_rule_config():
-    """Fluency rule should accept configuration parameters."""
-    rule = get_rule(
-        "fluency",
-        {"min_score": 8, "criteria": "评估对话是否自然流畅"},
-    )
-    assert rule.params["min_score"] == 8
-    assert rule.params["criteria"] == "评估对话是否自然流畅"
-
-
-def test_fluency_rule_empty_dialog():
-    """Fluency rule should fail on empty dialog."""
-    rule = get_rule("fluency", {"min_score": 7})
-    case = Case(id="c1", messages=["hello"])
-    import asyncio
-
-    result = asyncio.run(rule.evaluate(case, []))
-    assert result.passed is False
-    assert "无回复记录" in result.reason
-
-
-@pytest.mark.asyncio
-async def test_fluency_rule_no_model_config():
-    """Fluency rule should fail without model configuration."""
-    rule = get_rule("fluency", {"min_score": 7})
-    case = Case(id="c1", messages=["hello"])
-    dialog = [
-        Turn(
-            id="t1",
-            run_id="r1",
-            case_id="c1",
-            round_index=1,
-            sent_message={"msgBody": "你好"},
-            reply={"msgBody": "您好，有什么可以帮助您的？"},
-        )
-    ]
-    result = await rule.evaluate(case, dialog)
-    assert result.passed is False
-    assert "未绑定评估模型" in result.reason
-
-
-def test_fluency_rule_default_min_score():
-    """Fluency rule should have default min_score of 7."""
-    rule = get_rule("fluency", {})
-    assert rule.params.get("min_score", 7) == 7
-
-
-def test_abandonment_rate_zero_total():
-    """Abandonment rate should handle zero total cases."""
-    summary = RunSummary(total_cases=0, abandoned_cases=0)
-    # Should not raise division by zero
-    if summary.total_cases > 0:
-        rate = summary.abandoned_cases / summary.total_cases
-    else:
-        rate = None
-    assert rate is None
-
-
-def test_abandonment_rate_all_abandoned():
-    """Abandonment rate should be 1.0 when all cases are abandoned."""
-    summary = RunSummary(total_cases=5, abandoned_cases=5)
-    rate = summary.abandoned_cases / summary.total_cases
-    assert rate == 1.0
-
-
-def test_abandonment_rate_none_abandoned():
-    """Abandonment rate should be 0.0 when no cases are abandoned."""
-    summary = RunSummary(total_cases=5, abandoned_cases=0)
-    rate = summary.abandoned_cases / summary.total_cases
-    assert rate == 0.0