204 lines
8.2 KiB
Python
204 lines
8.2 KiB
Python
"""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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async def direct_answer(self, query: str) -> str:
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"""快路径直答：让 Worker 直接生成用户回答（非 JSON、无围栏）。"""
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prompt = ("请直接回答下面这个问题，输出对用户有用的正文"
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"（不要输出 JSON，不要加代码块围栏）。问题：" + query)
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return await self.generate(prompt)
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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 = "", hint: str = "") -> StepOutcome:
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"""执行单个 step。existing_artifact 为该步当前已有工件全文；hint 为 Architect 裁决提示。"""
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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, hint)
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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, hint: 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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if hint:
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base += "\n\n[架构师裁决] " + hint
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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 缺省时按 backend 选择：
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mock（零运行时演示）| llama_server（真实本地模型，惰性连接）。"""
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backend = cfg.get("backend", "llama_server")
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if generate is None:
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if backend == "mock":
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generate = _mock_generate()
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else:
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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 _mock_generate() -> Callable[[str], Awaitable[str]]:
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"""零运行时 mock 生成器：返回一段确定性文本（演示/测试，不连真实模型）。"""
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async def _gen(prompt: str) -> str:
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return ("（mock worker）以下是对当前步骤的实现说明："
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"步骤已完成，内容足够长且非占位，可供接地验证通过。")
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return _gen
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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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