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>
97 lines
2.8 KiB
Python
97 lines
2.8 KiB
Python
"""
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OpenClaw Skill example for AgentEvalTool.
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This skill demonstrates how OpenClaw can call the AgentEvalTool CLI
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to execute an evaluation run and fetch the report.
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In OpenClaw, register this as a skill and configure the following parameters:
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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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- report_format: "json" or "html" (default "json")
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The skill assumes that the `agenteval` CLI is available on the system PATH.
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"""
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import json
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import subprocess
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from typing import Any
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class AgentEvalSkill:
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"""OpenClaw skill wrapper for AgentEvalTool."""
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def __init__(self, config: dict[str, Any]):
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self.config = config
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def run(self) -> dict[str, Any]:
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target_id = self.config["target_id"]
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scenario_id = self.config["scenario_id"]
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report_format = self.config.get("report_format", "json")
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# 1. Trigger evaluation run
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run_cmd = [
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"agenteval",
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"run",
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"start",
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"--target-id",
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target_id,
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"--scenario-id",
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scenario_id,
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]
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run_result = subprocess.run(run_cmd, capture_output=True, text=True, check=False)
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if run_result.returncode != 0:
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return {
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"ok": False,
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"error": f"evaluation run failed: {run_result.stderr}",
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"stdout": run_result.stdout,
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}
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# Extract run_id from CLI output (last line contains "run_id=xxx")
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run_id = None
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for line in reversed(run_result.stdout.strip().splitlines()):
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if "run_id=" in line:
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run_id = line.split("run_id=")[-1].strip().split()[0]
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break
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if not run_id:
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return {
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"ok": False,
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"error": "could not extract run_id from CLI output",
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"stdout": run_result.stdout,
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}
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# 2. Fetch report
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report_cmd = [
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"agenteval",
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"report",
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"show",
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run_id,
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"--format",
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report_format,
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]
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report_result = subprocess.run(report_cmd, capture_output=True, text=True, check=False)
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if report_result.returncode != 0:
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return {
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"ok": False,
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"error": f"report fetch failed: {report_result.stderr}",
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"run_id": run_id,
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}
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report_data = report_result.stdout
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if report_format == "json":
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try:
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report_data = json.loads(report_data)
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except json.JSONDecodeError:
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pass
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return {
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"ok": True,
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"run_id": run_id,
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"report": report_data,
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}
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# Example entrypoint for OpenClaw runtime.
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def execute(config: dict[str, Any]) -> dict[str, Any]:
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return AgentEvalSkill(config).run()
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