feat(v2): T5 WorkerLoop + 接地验证（代码沙箱/facts对照/结构检查）

2026-08-30 21:08:45 +08:00
parent b3143e2914
commit 4fbbdb5290
5 changed files with 502 additions and 2 deletions
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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）。
+
+流程（对齐《实现方案_v2》5.1 T5 / 4.4）：
+  读 brief+当前步 -> 模型生成工件 -> 接地验证（D4 分层）
+    -> 通过：写 progress(done)
+    -> 失败：自修 <= max_fix_attempts 次（把验证错误回喂重新生成）
+    -> 仍失败：写 issue（增量、带锚点）
+
+- generate 为可注入的文本生成器（真实为 llama-server 端点；测试用假实现）。
+- 工件落盘：runs/<request_id>/artifacts/<step>.py（由 pipeline 负责写盘，本模块只产出文本）。
+"""
+from __future__ import annotations
+
+import time
+from dataclasses import dataclass, field
+from typing import Any, Awaitable, Callable, Dict, List, Optional
+
+from .verifier import Verifier, detect_artifact_language, extract_code_block
+from .workspace import Workspace, build_anchor
+
+# 按领域推断默认工件扩展名
+_DOMAIN_EXT = {
+    "code": ".py",
+    "math": ".md",
+    "legal": ".md",
+    "medical": ".md",
+    "finance": ".md",
+    "life": ".md",
+    "education": ".md",
+    "general": ".md",
+}
+
+
+def artifact_name_for(step_id: str, domain: str) -> str:
+    """为 step 生成工件文件名。"""
+    ext = _DOMAIN_EXT.get(domain, ".md")
+    return f"{step_id}{ext}"
+
+
+@dataclass
+class StepOutcome:
+    """单步执行结果。"""
+    step_id: str
+    status: str                    # done | issue
+    summary: str = ""
+    model_used: str = "local"
+    attempts: int = 0
+    issue_id: Optional[str] = None
+    artifact_name: Optional[str] = None
+    artifact_text: str = ""
+    details: List[str] = field(default_factory=list)
+
+
+class WorkerLoop:
+    """小模型 Worker：实现 -> 验证 -> 自修 -> issue。"""
+
+    def __init__(
+        self,
+        generate: Callable[[str], Awaitable[str]],
+        verifier: Optional[Verifier] = None,
+        kb: Any = None,
+        max_fix_attempts: int = 2,
+        model_used: str = "local-llama",
+    ):
+        self.generate = generate
+        self.verifier = verifier or Verifier()
+        self.kb = kb
+        self.max_fix_attempts = max_fix_attempts
+        self.model_used = model_used
+
+    def _domain_from(self, ws: Workspace) -> str:
+        tags = (ws.get("brief") or {}).get("tags") or []
+        for t in tags:
+            if t != "safety":
+                return t
+        return "general"
+
+    async def run_step(self, ws: Workspace, step_id: str,
+                       existing_artifact: str = "") -> StepOutcome:
+        """执行单个 step。existing_artifact 为该步当前已有工件全文（若有）。"""
+        domain = self._domain_from(ws)
+        brief = ws.get("brief") or {}
+        plan = brief.get("plan") or []
+        step_def = next((p for p in plan if p.get("id") == step_id), {})
+        done_criteria = step_def.get("done_criteria", "")
+
+        artifact_name = artifact_name_for(step_id, domain)
+        current = existing_artifact
+        details: List[str] = []
+
+        for attempt in range(1, self.max_fix_attempts + 1):
+            prompt = self._build_prompt(ws, step_id, current, attempt, done_criteria)
+            out = await self.generate(prompt)
+            if domain == "code":
+                candidate = extract_code_block(out)
+            else:
+                candidate = out.strip()
+            details.append(f"attempt{attempt}: 生成 {len(candidate)} 字符")
+
+            passed, v_details = self.verifier.verify(
+                domain, artifact_name, candidate, ws["query"], kb=self.kb)
+            details.extend(f"  - {d}" for d in v_details)
+            if passed:
+                # 写回交流文本：progress(done) + 摘要
+                ws.add_progress(step_id, "done", f"步骤完成（{attempt} 次尝试）",
+                                artifact=build_anchor(artifact_name, 1))
+                return StepOutcome(
+                    step_id=step_id, status="done",
+                    summary=f"步骤完成（{attempt} 次尝试）",
+                    model_used=self.model_used, attempts=attempt,
+                    artifact_name=artifact_name, artifact_text=candidate,
+                    details=details,
+                )
+            # 未通过：带错误反馈重新生成（自修）
+            current = candidate
+            feedback = "；".join(v_details)
+            details.append(f"attempt{attempt} 未通过，进入自修")
+
+        # 全部尝试失败 -> 写 issue
+        anchor = build_anchor(artifact_name, 1, 30)
+        iid = ws.add_issue(
+            step=step_id,
+            anchor=anchor,
+            observed=f"验证未通过：{'；'.join(d for d in details if d.startswith('  - ')) or '未知'}",
+            expected=done_criteria or "满足该步 done_criteria",
+            tried=f"已自修 {self.max_fix_attempts} 次",
+            ask="请裁决该步的实现方向或提供兜底实现",
+        )
+        return StepOutcome(
+            step_id=step_id, status="issue", summary="未能通过验证，已上报 issue",
+            model_used=self.model_used, attempts=self.max_fix_attempts,
+            issue_id=iid, artifact_name=artifact_name, artifact_text=current,
+            details=details,
+        )
+
+    def _build_prompt(self, ws: Workspace, step_id: str, current: str,
+                      attempt: int, done_criteria: str) -> str:
+        base = ws.render_for_worker(step_id, artifact_text=current or None)
+        if attempt > 1:
+            base += (
+                "\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"
+                f"完整工件。本次为第 {attempt} 次尝试。"
+            )
+        return base
+
+
+def build_worker(cfg: Dict[str, Any], kb: Any = None,
+                 generate: Optional[Callable[[str], Awaitable[str]]] = None) -> WorkerLoop:
+    """cfg 为 config.worker 段。generate 缺省时用 llama-server 端点客户端（惰性）。"""
+    if generate is None:
+        generate = _make_llama_generate(cfg)
+    verifier = Verifier(code_timeout_s=float(cfg.get("code_timeout_s", 10)))
+    return WorkerLoop(
+        generate=generate,
+        verifier=verifier,
+        kb=kb,
+        max_fix_attempts=int(cfg.get("max_fix_attempts", 2)),
+        model_used=cfg.get("backend", "llama_server"),
+    )
+
+
+def _make_llama_generate(cfg: Dict[str, Any]) -> Callable[[str], Awaitable[str]]:
+    """返回调用本地 llama-server（OpenAI 兼容 /v1/chat/completions）的生成器。"""
+    base_url = cfg.get("base_url", f"http://127.0.0.1:{cfg.get('port', 8901)}/v1")
+    model = cfg.get("model", "local")
+    temperature = float(cfg.get("temperature", 0.3))
+    timeout_s = float(cfg.get("per_step_timeout_s", 300))
+
+    async def _gen(prompt: str) -> str:
+        import httpx
+        async with httpx.AsyncClient(timeout=timeout_s) as client:
+            resp = await client.post(
+                f"{base_url}/chat/completions",
+                json={"model": model, "messages": [{"role": "user", "content": prompt}],
+                      "temperature": temperature, "max_tokens": 4096},
+            )
+            resp.raise_for_status()
+            return resp.json()["choices"][0]["message"]["content"]
+
+    return _gen