a77cd83e6a
## 新增功能 - 文件管理模块：分类树 + 文件上传/下载/删除 - 文件上传支持拖拽（Dragger）+ 手动上传（customRequest 模式） ## 页面布局统一（参照评测执行页） - 仪表盘/评测对象/评测场景/评测报告 全部改为全高 flex 布局 - 统一内联页头样式（h2 + 竖线分隔 + 描述） - 表格撑满高度、overflow 处理 - 每页添加刷新按钮 ## Bug 修复 - 分类树操作按钮 hover 不可见（CSS 规则缺失） - 文件上传失败（multipart boundary 缺失） - LLM API 响应 content blocks 数组格式支持（_extract_content_from_api_response） - response_time_max_ms 被静默忽略（隐式规则传空 params） - 空 messages 导致 IndexError 崩溃 - poll_reply 异常中止整个 run（缺 try/catch） - engine finally 未关闭 session - 3 个页面 UTC 时间戳解析偏差 8 小时 ## 后端 - EvalEngine: poll_reply 异常保护、空 dialog 保护、session 关闭 - LLM API 响应解析支持 content-block-array 格式 - 隐式 response_time 规则正确传递 max_ms 参数 ## 前端 - api.ts: 移除手动 Content-Type（让浏览器自动添加 boundary） - Files.tsx: customRequest 替代 beforeUpload、布局优化 - index.css: 分类树 hover 规则 - Targets/Scenarios/Home/Reports: 全高布局改造 - 3 个页面时间戳改用 formatDateTime()（修复 UTC 偏差） Co-Authored-By: Claude <noreply@anthropic.com>
54 lines
1.8 KiB
Python
54 lines
1.8 KiB
Python
"""Keyword matching evaluation rule."""
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from agenteval.evaluation.rules.base import EvalRule, RuleResult, register_rule
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from agenteval.models import Case, Turn
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def _extract_text(reply) -> str:
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"""Extract plain text from a reply object for matching."""
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if reply is None:
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return ""
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if isinstance(reply, str):
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return reply
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if isinstance(reply, dict):
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# Tutu-api message structure: msgBody.content
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body = reply.get("msgBody") or reply.get("content", "")
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if isinstance(body, dict):
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return body.get("content", "")
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return str(body)
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return str(reply)
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@register_rule
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class KeywordMatchRule(EvalRule):
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"""Check whether the reply contains required keywords and excludes forbidden ones."""
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name = "keyword_match"
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def evaluate(self, case: Case, dialog: list[Turn]) -> RuleResult:
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if not dialog:
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return RuleResult(passed=False, reason="无回复记录")
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last_turn = dialog[-1]
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reply = last_turn.reply
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text = _extract_text(reply).lower()
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params = self.params
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include = [k.lower() for k in params.get("keywords", [])]
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exclude = [k.lower() for k in params.get("exclude_keywords", [])]
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missing = [k for k in include if k not in text]
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found_excluded = [k for k in exclude if k in text]
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if missing or found_excluded:
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reasons = []
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if missing:
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reasons.append(f"缺少关键词: {missing}")
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if found_excluded:
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reasons.append(f"包含禁用词: {found_excluded}")
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return RuleResult(passed=False, reason="; ".join(reasons))
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match_count = sum(1 for k in include if k in text)
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score = 1.0 if not include else match_count / len(include)
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return RuleResult(passed=True, score=score, reason="关键词匹配通过")
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