feat(v2): T5 WorkerLoop + 接地验证（代码沙箱/facts对照/结构检查）
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"""接地验证器（D4）—— Worker 自验证的"接地"来源。
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验证分层优先级（D4）：
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可执行验证（跑代码/跑测试） > facts 对照 > 结构检查 > 模型自由判断（最后手段）
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- 可执行验证：code 域 .py 工件在临时目录子进程沙箱运行（timeout、-I 隔离、捕获输出）。
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- facts 对照：把回答/工件与知识库 facts（statement/keywords）比对覆盖度。
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- 结构检查：工件非空、长度达标、不含纯占位。
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说明：沙箱目前做"临时目录 + 超时 + 解释器隔离"，Windows 下真正禁网需系统级工具，
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此处以超时与隔离为主要护栏（文档如实记录）。验证器为纯逻辑，可注入 runner 便于单测。
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"""
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from __future__ import annotations
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import subprocess
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import sys
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import tempfile
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from pathlib import Path
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from typing import Any, Callable, Dict, List, Optional, Tuple
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# 判定为"含代码"的启发标记
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_CODE_HINTS = ("def ", "class ", "import ", "return ", "if __name__", "print(")
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def detect_artifact_language(name: str) -> str:
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"""按文件名推断工件语言：python / json / text。"""
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suffix = Path(name).suffix.lower()
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if suffix in (".py", ".pyw"):
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return "python"
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if suffix in (".json",):
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return "json"
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return "text"
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def extract_code_block(text: str) -> str:
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"""从模型输出提取 python 代码块（剥除 markdown 围栏），无则返回原文本。"""
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t = text.strip()
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fence = chr(96) * 3 # 三个反引号
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marker = fence + "python"
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start = t.find(marker)
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if start == -1:
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return t
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body_start = start + len(marker)
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end = t.find(fence, body_start)
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if end == -1:
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return t[body_start:].strip()
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return t[body_start:end].strip()
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def run_code_sandbox(code: str, timeout_s: float = 10.0,
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runner: Optional[Callable[[List[str], Dict[str, str], float], Tuple[int, str, str]]] = None
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) -> Tuple[int, str, str]:
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"""在临时目录子进程运行 python 代码。返回 (returncode, stdout, stderr)。
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隔离措施：临时工作目录、-I 隔离模式、timeout 超时强杀、捕获输出。
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runner 可注入（测试用假执行器，避免真跑任意代码）。
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"""
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if runner is not None:
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return runner([sys.executable, "-I"], {}, timeout_s)
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with tempfile.TemporaryDirectory(prefix="v2_sandbox_") as tmp:
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script = Path(tmp) / "main.py"
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script.write_text(code, encoding="utf-8")
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try:
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proc = subprocess.run(
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[sys.executable, "-I", str(script)],
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capture_output=True, text=True, timeout=timeout_s,
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cwd=tmp,
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creationflags=subprocess.CREATE_NO_WINDOW,
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)
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return proc.returncode, proc.stdout or "", proc.stderr or ""
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except subprocess.TimeoutExpired:
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return -1, "", "timeout exceeded"
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class Verifier:
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"""按 D4 分层执行接地验证。"""
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def __init__(self, code_timeout_s: float = 10.0,
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sandbox_runner: Optional[Callable[..., Tuple[int, str, str]]] = None):
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self.code_timeout_s = code_timeout_s
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self._sandbox_runner = sandbox_runner
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def verify(self, domain: str, artifact_name: str, artifact_text: str,
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query: str, kb: Any = None) -> Tuple[bool, List[str]]:
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"""返回 (passed, details)。kb 为 KnowledgeBase（可 None，跳过 facts 层）。"""
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lang = detect_artifact_language(artifact_name)
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details: List[str] = []
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# 1) 可执行验证（D4 最高优先级）：code + python 工件且含代码
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if lang == "python" and any(h in artifact_text for h in _CODE_HINTS):
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rc, out, err = run_code_sandbox(artifact_text, self.code_timeout_s,
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runner=self._sandbox_runner)
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if rc == 0:
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details.append("代码沙箱运行通过 (rc=0)")
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return True, details
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# 可执行验证失败 -> 直接拒绝（不落回结构检查，避免"假通过"）
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details.append(f"代码沙箱运行失败 rc={rc}: {(err or out)[:120]}")
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return False, details
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elif lang == "python":
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details.append("工件不含可执行代码（跳过沙箱，进入 facts/结构检查）")
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# 2) facts 对照
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if kb is not None:
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fact_hits = self._check_facts(domain, artifact_text, kb)
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if fact_hits:
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details.append(f"facts 对照命中 {fact_hits}")
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return True, details
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# 3) 结构检查
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text = artifact_text.strip()
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if len(text) < 20:
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details.append(f"工件过短（{len(text)} 字符）")
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return False, details
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if text.lower() in ("pass", "none", "todo", "待实现", "略"):
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details.append("工件为占位内容")
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return False, details
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details.append("结构检查通过（非空、长度达标）")
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return True, details
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def _check_facts(self, domain: str, text: str, kb: Any) -> int:
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"""统计工件文本命中知识库事实的数量。"""
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facts = kb.facts(domain) or []
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if not facts:
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return 0
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hits = 0
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for f in facts:
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kw = f.get("keywords") or []
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if any(str(k) in text for k in kw):
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hits += 1
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return hits
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def build_verifier(cfg: Dict[str, Any]) -> Verifier:
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"""cfg 为 config.worker 段。"""
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return Verifier(code_timeout_s=float(cfg.get("code_timeout_s", 10)))
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"""WorkerLoop —— 小模型（本地 llama.cpp）的"实现/自验证"循环（端云协同的执行者）。
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流程（对齐《实现方案_v2》5.1 T5 / 4.4）：
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读 brief+当前步 -> 模型生成工件 -> 接地验证（D4 分层）
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-> 通过：写 progress(done)
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-> 失败：自修 <= max_fix_attempts 次（把验证错误回喂重新生成）
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-> 仍失败：写 issue（增量、带锚点）
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- generate 为可注入的文本生成器（真实为 llama-server 端点；测试用假实现）。
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- 工件落盘：runs/<request_id>/artifacts/<step>.py（由 pipeline 负责写盘，本模块只产出文本）。
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"""
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from __future__ import annotations
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import time
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from dataclasses import dataclass, field
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from typing import Any, Awaitable, Callable, Dict, List, Optional
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from .verifier import Verifier, detect_artifact_language, extract_code_block
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from .workspace import Workspace, build_anchor
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# 按领域推断默认工件扩展名
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_DOMAIN_EXT = {
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"code": ".py",
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"math": ".md",
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"legal": ".md",
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"medical": ".md",
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"finance": ".md",
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"life": ".md",
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"education": ".md",
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"general": ".md",
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}
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def artifact_name_for(step_id: str, domain: str) -> str:
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"""为 step 生成工件文件名。"""
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ext = _DOMAIN_EXT.get(domain, ".md")
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return f"{step_id}{ext}"
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@dataclass
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class StepOutcome:
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"""单步执行结果。"""
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step_id: str
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status: str # done | issue
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summary: str = ""
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model_used: str = "local"
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attempts: int = 0
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issue_id: Optional[str] = None
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artifact_name: Optional[str] = None
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artifact_text: str = ""
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details: List[str] = field(default_factory=list)
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class WorkerLoop:
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"""小模型 Worker：实现 -> 验证 -> 自修 -> issue。"""
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def __init__(
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self,
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generate: Callable[[str], Awaitable[str]],
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verifier: Optional[Verifier] = None,
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kb: Any = None,
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max_fix_attempts: int = 2,
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model_used: str = "local-llama",
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):
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self.generate = generate
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self.verifier = verifier or Verifier()
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self.kb = kb
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self.max_fix_attempts = max_fix_attempts
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self.model_used = model_used
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def _domain_from(self, ws: Workspace) -> str:
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tags = (ws.get("brief") or {}).get("tags") or []
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for t in tags:
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if t != "safety":
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return t
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return "general"
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async def run_step(self, ws: Workspace, step_id: str,
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existing_artifact: str = "") -> StepOutcome:
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"""执行单个 step。existing_artifact 为该步当前已有工件全文（若有）。"""
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domain = self._domain_from(ws)
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brief = ws.get("brief") or {}
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plan = brief.get("plan") or []
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step_def = next((p for p in plan if p.get("id") == step_id), {})
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done_criteria = step_def.get("done_criteria", "")
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artifact_name = artifact_name_for(step_id, domain)
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current = existing_artifact
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details: List[str] = []
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for attempt in range(1, self.max_fix_attempts + 1):
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prompt = self._build_prompt(ws, step_id, current, attempt, done_criteria)
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out = await self.generate(prompt)
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if domain == "code":
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candidate = extract_code_block(out)
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else:
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candidate = out.strip()
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details.append(f"attempt{attempt}: 生成 {len(candidate)} 字符")
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passed, v_details = self.verifier.verify(
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domain, artifact_name, candidate, ws["query"], kb=self.kb)
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details.extend(f" - {d}" for d in v_details)
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if passed:
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# 写回交流文本：progress(done) + 摘要
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ws.add_progress(step_id, "done", f"步骤完成（{attempt} 次尝试）",
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artifact=build_anchor(artifact_name, 1))
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return StepOutcome(
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step_id=step_id, status="done",
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summary=f"步骤完成（{attempt} 次尝试）",
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model_used=self.model_used, attempts=attempt,
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artifact_name=artifact_name, artifact_text=candidate,
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details=details,
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)
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# 未通过：带错误反馈重新生成（自修）
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current = candidate
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feedback = "；".join(v_details)
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details.append(f"attempt{attempt} 未通过，进入自修")
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# 全部尝试失败 -> 写 issue
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anchor = build_anchor(artifact_name, 1, 30)
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iid = ws.add_issue(
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step=step_id,
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anchor=anchor,
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observed=f"验证未通过：{'；'.join(d for d in details if d.startswith(' - ')) or '未知'}",
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expected=done_criteria or "满足该步 done_criteria",
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tried=f"已自修 {self.max_fix_attempts} 次",
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ask="请裁决该步的实现方向或提供兜底实现",
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)
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return StepOutcome(
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step_id=step_id, status="issue", summary="未能通过验证，已上报 issue",
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model_used=self.model_used, attempts=self.max_fix_attempts,
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issue_id=iid, artifact_name=artifact_name, artifact_text=current,
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details=details,
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)
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def _build_prompt(self, ws: Workspace, step_id: str, current: str,
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attempt: int, done_criteria: str) -> str:
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base = ws.render_for_worker(step_id, artifact_text=current or None)
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if attempt > 1:
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base += (
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"\n\n[注意] 上次生成的工件未通过接地验证。请修正以下问题后重新输出"
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f"完整工件。本次为第 {attempt} 次尝试。"
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)
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return base
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def build_worker(cfg: Dict[str, Any], kb: Any = None,
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generate: Optional[Callable[[str], Awaitable[str]]] = None) -> WorkerLoop:
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"""cfg 为 config.worker 段。generate 缺省时用 llama-server 端点客户端（惰性）。"""
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if generate is None:
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generate = _make_llama_generate(cfg)
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verifier = Verifier(code_timeout_s=float(cfg.get("code_timeout_s", 10)))
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return WorkerLoop(
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generate=generate,
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verifier=verifier,
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kb=kb,
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max_fix_attempts=int(cfg.get("max_fix_attempts", 2)),
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model_used=cfg.get("backend", "llama_server"),
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)
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def _make_llama_generate(cfg: Dict[str, Any]) -> Callable[[str], Awaitable[str]]:
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"""返回调用本地 llama-server（OpenAI 兼容 /v1/chat/completions）的生成器。"""
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base_url = cfg.get("base_url", f"http://127.0.0.1:{cfg.get('port', 8901)}/v1")
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model = cfg.get("model", "local")
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temperature = float(cfg.get("temperature", 0.3))
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timeout_s = float(cfg.get("per_step_timeout_s", 300))
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async def _gen(prompt: str) -> str:
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import httpx
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async with httpx.AsyncClient(timeout=timeout_s) as client:
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resp = await client.post(
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f"{base_url}/chat/completions",
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json={"model": model, "messages": [{"role": "user", "content": prompt}],
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"temperature": temperature, "max_tokens": 4096},
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)
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resp.raise_for_status()
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return resp.json()["choices"][0]["message"]["content"]
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return _gen
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+7
-1
@@ -10,5 +10,11 @@ from router_system.router import build_router
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@pytest.fixture()
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def router():
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"""????????? mock ?????????????"""
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"""共享的 mock 全链路路由实例（零依赖、离线可跑）"""
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return build_router()
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@pytest.fixture()
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def kb():
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"""v2 验证接地用的知识库实例（facts 对照）。"""
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from router_system.knowledge import KnowledgeBase
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return KnowledgeBase()
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@@ -0,0 +1,179 @@
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"""T5 WorkerLoop + 接地验证单测（封闭，注入假 generate / 假沙箱 runner）。"""
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import pytest
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from router_system.knowledge import KnowledgeBase
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from router_system.verifier import (
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Verifier,
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detect_artifact_language,
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extract_code_block,
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run_code_sandbox,
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)
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from router_system.worker import WorkerLoop, artifact_name_for
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from router_system.workspace import Workspace
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def _brief(domain="code"):
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return {
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"goal": "实现快排",
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"constraints": ["标准库"],
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"tags": [domain],
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"acceptance": [{"id": "a1", "check": "可运行", "machine_checkable": True}],
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"plan": [
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{"id": "s1", "task": "实现快排", "deps": [], "done_criteria": "函数可运行"},
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{"id": "s2", "task": "写测试", "deps": ["s1"], "done_criteria": "3/3 通过"},
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],
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}
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def _ws(domain="code"):
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ws = Workspace.new(request_id="abc123def456", query="写个快排",
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api_token_cap=8000, rounds_cap=6)
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ws.apply_brief(_brief(domain))
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return ws
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def _fake_gen(texts):
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"""顺序返回预设生成文本的假 generate。"""
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it = iter(texts)
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async def _g(prompt):
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try:
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return next(it)
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except StopIteration:
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return ""
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return _g
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# ---------- verifier: 代码沙箱 ----------
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def test_verifier_code_pass(tmp_path):
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v = Verifier(sandbox_runner=lambda cmd, env, t: (0, "ok", ""))
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passed, det = v.verify("code", "s1.py", "def f():\n return 1\n", "q")
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assert passed is True
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assert any("沙箱运行通过" in d for d in det)
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def test_verifier_code_fail_then_structure():
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# 沙箱失败 + 无 facts + 文本过短 -> False
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v = Verifier(sandbox_runner=lambda cmd, env, t: (1, "", "boom"))
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passed, det = v.verify("code", "s1.py", "def f(): pass", "q")
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assert passed is False
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def test_verifier_facts_hit(kb):
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v = Verifier()
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# medical 域：回答命中"高血压"事实关键词
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passed, det = v.verify("medical", "s1.md",
|
||||
"高血压患者应低盐低脂饮食、控制体重、规律运动", "高血压饮食", kb=kb)
|
||||
assert passed is True
|
||||
assert any("facts" in d for d in det)
|
||||
|
||||
|
||||
def test_verifier_structure_pass():
|
||||
v = Verifier()
|
||||
passed, _ = v.verify("general", "s1.md", "这是一段足够长的、非占位的正常回答内容。", "q")
|
||||
assert passed is True
|
||||
|
||||
|
||||
def test_verifier_placeholder_fail():
|
||||
v = Verifier()
|
||||
passed, det = v.verify("general", "s1.md", "待实现", "q")
|
||||
assert passed is False
|
||||
|
||||
|
||||
# ---------- run_code_sandbox ----------
|
||||
def test_run_code_sandbox_good_code():
|
||||
rc, out, err = run_code_sandbox("print(1 + 1)")
|
||||
assert rc == 0
|
||||
assert out.strip() == "2"
|
||||
|
||||
|
||||
def test_run_code_sandbox_bad_code():
|
||||
rc, out, err = run_code_sandbox("raise ValueError('x')")
|
||||
assert rc != 0
|
||||
assert "ValueError" in (err or "")
|
||||
|
||||
|
||||
# ---------- extract_code_block ----------
|
||||
def test_extract_code_block():
|
||||
bt = chr(96) * 3
|
||||
text = f"前言\n{bt}python\ndef f(): return 1\n{bt}\n后记"
|
||||
assert extract_code_block(text) == "def f(): return 1"
|
||||
|
||||
|
||||
def test_extract_code_block_no_fence():
|
||||
assert extract_code_block("print(1)") == "print(1)"
|
||||
|
||||
|
||||
# ---------- detect_artifact_language ----------
|
||||
def test_detect_language():
|
||||
assert detect_artifact_language("s1.py") == "python"
|
||||
assert detect_artifact_language("s1.json") == "json"
|
||||
assert detect_artifact_language("s1.md") == "text"
|
||||
|
||||
|
||||
# ---------- artifact_name ----------
|
||||
def test_artifact_name():
|
||||
assert artifact_name_for("s1", "code") == "s1.py"
|
||||
assert artifact_name_for("s1", "legal") == "s1.md"
|
||||
|
||||
|
||||
# ---------- WorkerLoop 三路径 ----------
|
||||
def test_worker_one_pass():
|
||||
code = "def quicksort(arr):\n return sorted(arr)"
|
||||
ws = _ws()
|
||||
w = WorkerLoop(_fake_gen([code]),
|
||||
verifier=Verifier(sandbox_runner=lambda c, e, t: (0, "", "")),
|
||||
max_fix_attempts=2)
|
||||
out = asyncio_run(w.run_step(ws, "s1"))
|
||||
assert out.status == "done"
|
||||
assert out.attempts == 1
|
||||
assert out.artifact_text == code
|
||||
assert len(ws["progress"]) == 1
|
||||
assert ws["progress"][0]["status"] == "done"
|
||||
|
||||
|
||||
def test_worker_self_fix_success():
|
||||
bad = "def quicksort(arr):\n return arr[0] # 错"
|
||||
good = "def quicksort(arr):\n return sorted(arr)"
|
||||
# 第一次沙箱失败，第二次通过
|
||||
results = iter([(1, "", "IndexError"), (0, "", "")])
|
||||
|
||||
def fake_run(cmd, env, t):
|
||||
return next(results)
|
||||
|
||||
ws = _ws()
|
||||
w = WorkerLoop(_fake_gen([bad, good]),
|
||||
verifier=Verifier(sandbox_runner=fake_run),
|
||||
max_fix_attempts=2)
|
||||
out = asyncio_run(w.run_step(ws, "s1"))
|
||||
assert out.status == "done"
|
||||
assert out.attempts == 2
|
||||
assert len(ws["progress"]) == 1
|
||||
|
||||
|
||||
def test_worker_issue_after_exhaust():
|
||||
bad = "def quicksort(arr):\n return arr[0]"
|
||||
ws = _ws()
|
||||
w = WorkerLoop(_fake_gen([bad, bad]),
|
||||
verifier=Verifier(sandbox_runner=lambda c, e, t: (1, "", "Err")),
|
||||
max_fix_attempts=2)
|
||||
out = asyncio_run(w.run_step(ws, "s1"))
|
||||
assert out.status == "issue"
|
||||
assert out.issue_id is not None
|
||||
assert len(ws["issues"]) == 1
|
||||
assert ws["issues"][0]["step"] == "s1"
|
||||
|
||||
|
||||
def test_worker_medical_facts_pass(kb):
|
||||
# 医疗域：模型给出含事实关键词的回答 -> facts 验证通过
|
||||
answer = "高血压患者应低盐低脂饮食、控制体重、规律运动、戒烟限酒"
|
||||
ws = _ws(domain="medical")
|
||||
w = WorkerLoop(_fake_gen([answer]), kb=kb, max_fix_attempts=2)
|
||||
out = asyncio_run(w.run_step(ws, "s1"))
|
||||
assert out.status == "done"
|
||||
|
||||
|
||||
def asyncio_run(coro):
|
||||
import asyncio
|
||||
return asyncio.run(coro)
|
||||
+1
-1
@@ -71,7 +71,7 @@ P0 完成后的能力：干净的后端抽象 + 可量化的评测 + 可追溯
|
||||
| T2 | 运维层：hw_profile + llama_server 进程管理 | ✅ 完成 | T2 |
|
||||
| T3 | ArchitectClient（DeepSeek API，JSON 约束） | ✅ 完成 | T3 |
|
||||
| T4 | Workspace（交流文本 schema/校验/渲染/rollup） | ✅ 完成 | T4 |
|
||||
| T5 | WorkerLoop + 接地验证 | ⬜ | |
|
||||
| T5 | WorkerLoop + 接地验证 | ✅ 完成 | T5 |
|
||||
| T6 | CollaborativePipeline 编排 | ⬜ | |
|
||||
| T7 | 网关扩展（/chat 切 v2，/chat/legacy） | ⬜ | |
|
||||
| T8 | 人工检验队列 ReviewQueue | ⬜ | |
|
||||
|
||||
Reference in New Issue
Block a user