## T1: P0 测试补全(+67 个测试)
- test_utils_llm.py: extract_reply_text / extract_content_from_llm_response / parse_json_from_llm_text 各边界
- test_file_repository.py: 分类 CRUD / 树形结构 / 级联删除 / 文件创建/查询/删除/物理文件清理
- test_report.py: generate_report / generate_compare_report / render_markdown / render_json
- test_llm_score.py: OpenAI 格式 / Anthropic content-block 格式 / JSON 回退解析 / 异常降级
## T2: P1 测试补全(+28 个测试)
- test_scenarios.py: 模板列表/字段完整性/规则类型有效性 + YAML/JSON 加载/校验
- test_webhook.py: 未配置不发送 / 正确 payload / secret header / 异常静默忽略
- test_reports_api.py: GET /reports/{id} / /html / /json / /markdown / /compare 集成测试
## UTC 时区根本修复
- storage/db.py: 新增 iso_utc() 函数,确保所有 datetime 序列化输出带 Z 后缀
- runs.py / files.py / report.py: 6 处 .isoformat() → iso_utc()
- 前端 toDate() 兜底仍保留(向下兼容),但后端不再输出无时区时间戳
Co-Authored-By: Claude <noreply@anthropic.com>
120 lines
4.7 KiB
Python
120 lines
4.7 KiB
Python
"""OpenClaw Skill for AgentEvalTool — HTTP API mode.
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Triggers an evaluation run via the AgentEvalTool REST API, polls until
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completion, and returns a structured Chinese-language summary suitable for
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display in an OpenClaw conversation.
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Configuration (passed via OpenClaw skill params):
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api_base_url Base URL of AgentEvalTool (e.g. http://192.168.8.145:8001)
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api_key X-API-Key header value (if auth is enabled, else omit)
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target_id ID of the registered evaluation target
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scenario_id ID of the evaluation scenario
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poll_interval Seconds between status polls (default: 3)
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timeout Max seconds to wait for completion (default: 300)
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"""
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import time
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from typing import Any
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import httpx
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class AgentEvalSkill:
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"""OpenClaw skill: trigger evaluation and return a structured summary."""
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def __init__(self, config: dict[str, Any]):
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self.api_base = config["api_base_url"].rstrip("/")
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self.api_key = config.get("api_key")
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self.target_id = config["target_id"]
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self.scenario_id = config["scenario_id"]
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self.poll_interval = float(config.get("poll_interval", 3))
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self.timeout = float(config.get("timeout", 300))
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def _headers(self) -> dict[str, str]:
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h = {"Content-Type": "application/json"}
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if self.api_key:
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h["X-API-Key"] = self.api_key
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return h
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def run(self) -> dict[str, Any]:
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with httpx.Client(base_url=self.api_base, headers=self._headers(), timeout=30) as client:
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# 1. Start the run
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resp = client.post(
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"/api/runs",
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json={
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"target_id": self.target_id,
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"scenario_id": self.scenario_id,
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},
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)
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if resp.status_code != 200:
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return {"ok": False, "error": f"启动评测失败: HTTP {resp.status_code} {resp.text}"}
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run = resp.json()
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run_id: str = run["id"]
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# 2. Poll until completion or timeout
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deadline = time.time() + self.timeout
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while time.time() < deadline:
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time.sleep(self.poll_interval)
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status_resp = client.get(f"/api/runs/{run_id}")
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if status_resp.status_code != 200:
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continue
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run = status_resp.json()
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if run.get("status") in ("completed", "failed"):
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break
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else:
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return {
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"ok": False,
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"run_id": run_id,
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"error": f"评测超时(>{self.timeout}s 未完成)",
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}
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# 3. Fetch structured report
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report_resp = client.get(f"/api/reports/{run_id}")
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report = report_resp.json() if report_resp.status_code == 200 else {}
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return self._format_result(run, report)
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def _format_result(self, run: dict[str, Any], report: dict[str, Any]) -> dict[str, Any]:
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status = run.get("status", "unknown")
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summary = run.get("summary") or {}
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pass_rate = summary.get("pass_rate", 0.0)
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passed_cases = summary.get("passed_cases", 0)
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total_cases = summary.get("total_cases", 0)
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passed_rules = summary.get("passed_rules", 0)
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total_rules = summary.get("total_rules", 0)
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# Build Chinese natural-language summary for display in chat
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if status == "completed":
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verdict = "✅ 评测完成" if pass_rate >= 0.8 else "⚠️ 评测完成(有失败项)"
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summary_text = (
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f"{verdict}\n"
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f"• 评测对象:{report.get('target_name', self.target_id)}\n"
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f"• 评测场景:{report.get('scenario_name', self.scenario_id)}\n"
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f"• 通过率:{pass_rate * 100:.1f}%\n"
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f"• 用例:{passed_cases}/{total_cases} 通过\n"
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f"• 规则:{passed_rules}/{total_rules} 通过\n"
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f"• 报告链接:{self.api_base}/api/reports/{run['id']}"
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)
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else:
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error_info = summary.get("error", {})
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if isinstance(error_info, dict):
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error_msg = error_info.get("message", str(error_info))
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else:
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error_msg = str(error_info)
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summary_text = f"❌ 评测失败:{error_msg}"
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return {
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"ok": status == "completed",
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"run_id": run.get("id"),
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"status": status,
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"pass_rate": pass_rate,
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"summary_text": summary_text,
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"report": report,
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}
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def execute(config: dict[str, Any]) -> dict[str, Any]:
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"""OpenClaw skill entrypoint."""
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return AgentEvalSkill(config).run()
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