refactor(core): loop.py 传输健壮性层析出 llm_transport.py + _execute_tool_call 拆分
架构审查 P1#4:loop.py 把「provider wire 层瞬态故障自愈」与「agent 控制流」 塞在同一文件;_execute_tool_call 一个函数线性堆 10 个关注点(148 行)。 - 新增 core/llm_transport.py(438 行):畸形/必填 key 被吞/空响应检测、三类 故障留痕、usage/delta 提取、robust_stream 重试策略(首败降级非流式 + salvage 可救当轮续)。取流两路径与 salvage 以 callable 注入——不 import loop,单测在 AgentLoop 实例上打桩 _collect_stream_once/_nonstream_once 的现有缝隙原样保留 - loop.py 1158→812 行,回归 ReAct 主干:_stream_llm 只留上下文压缩准备 (context 关注点),wire 健壮性委托 robust_stream;_collect_stream_once/ _nonstream_once/_try_salvage_response 留在 loop(持 llm/emit 态 + 测试缝) - _execute_tool_call 148 行拆为编排 + 4 个正交方法:_check_repeat_block (两道拦截)/_maybe_skill_model_switch(热切)/_repeat_feedback(登记+ 软提示)/_maybe_pptx_guard(产物机检) - 4 个测试文件 import 指向同步更新(is_empty_response 等改公有名) 292 测试全过(loop 重试/空响应/repeat-guard/salvage 套件覆盖改动路径)。 Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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"""LLM 传输健壮性层(从 core/loop.py 析出,2026-07-23)。
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关注点:provider wire 层的瞬态故障检测与自愈 —— 与 agent 控制流(ReAct 循环 /
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工具执行 / 熔断)正交。收在这里的东西回答同一个问题:「这一轮 LLM 响应能不能用,
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不能用怎么救」:
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- 检测:畸形 arguments(JSON 解析失败)/ 必填 key 被吞(解析成功但键被流式乱序
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吞掉)/ 空响应(tc 空且正文空)/ finish_reason
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- 留痕:三类故障各自 stdout + usage_events 双写(留痕绝不打断重试主路径)
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- 重试策略 robust_stream:首败即降级非流式(同轮流式失败强相关,provider 服务端
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拼 tool_calls 绕开 delta 错位),salvage 可救则当轮继续
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- usage/delta 提取:provider 差异归一
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依赖注入纪律:取流的两条路径(collect_stream / nonstream)与 salvage 都以 callable
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传入 —— AgentLoop 把 bound method 递进来,单测在实例上打桩即可,本模块不 import loop。
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"""
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from __future__ import annotations
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import json
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from typing import Any, Callable, Dict, List, Optional, Tuple
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from .storage import record_empty_response, record_malformed_tool_call
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# ─────────────────────── delta / usage 提取 ───────────────────────
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def extract_delta_content(chunk: Any) -> Optional[str]:
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"""从 stream chunk 提 delta.content(文本片段)。chunk 形态 litellm ModelResponseStream:
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choices[0].delta.content。usage-only 收尾 chunk(没 choices / delta)返 None。
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"""
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try:
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choices = getattr(chunk, "choices", None)
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if not choices:
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return None
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delta = getattr(choices[0], "delta", None)
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if delta is None:
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return None
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content = getattr(delta, "content", None)
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return content if content else None
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except Exception:
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return None
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def extract_delta_reasoning(chunk: Any) -> Optional[str]:
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"""从 stream chunk 提 delta.reasoning_content(thinking 模型的推理片段)。
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litellm 对多数 provider 归一到 delta.reasoning_content,个别只放
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provider_specific_fields —— 两处都查。没有则返 None(非 thinking 模型零开销)。
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"""
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try:
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choices = getattr(chunk, "choices", None)
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if not choices:
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return None
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delta = getattr(choices[0], "delta", None)
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if delta is None:
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return None
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rc = getattr(delta, "reasoning_content", None)
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if not rc:
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psf = getattr(delta, "provider_specific_fields", None) or {}
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rc = psf.get("reasoning_content") if isinstance(psf, dict) else None
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return rc if rc else None
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except Exception:
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return None
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def usage_to_dict(usage: Any) -> dict:
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if not usage:
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return {}
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if hasattr(usage, "model_dump"):
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usage = usage.model_dump()
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elif hasattr(usage, "dict"):
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usage = usage.dict()
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if isinstance(usage, dict):
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return usage
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return {}
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def extract_usage_details(usage: Any) -> dict:
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"""从 provider usage 提取统一 token 明细。
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DeepSeek 直接给 prompt_cache_hit_tokens / prompt_cache_miss_tokens;
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OpenAI 风格把 cached tokens 放在 prompt_tokens_details.cached_tokens。
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"""
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data = usage_to_dict(usage)
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prompt_details = data.get("prompt_tokens_details") or {}
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completion_details = data.get("completion_tokens_details") or {}
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if not isinstance(prompt_details, dict):
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prompt_details = {}
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if not isinstance(completion_details, dict):
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completion_details = {}
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cache_hit = (
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data.get("prompt_cache_hit_tokens")
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or prompt_details.get("cached_tokens")
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or 0
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)
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cache_miss = data.get("prompt_cache_miss_tokens") or 0
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return {
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"tokens_in": int(data.get("prompt_tokens") or 0),
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"tokens_out": int(data.get("completion_tokens") or 0),
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"cache_hit_tokens": int(cache_hit or 0),
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"cache_miss_tokens": int(cache_miss or 0),
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"reasoning_tokens": int(completion_details.get("reasoning_tokens") or 0),
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}
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def extract_usage(usage: Any) -> Tuple[int, int]:
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"""从 litellm response.usage 提 (prompt_tokens, completion_tokens)。"""
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details = extract_usage_details(usage)
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return details["tokens_in"], details["tokens_out"]
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# ─────────────────────── 故障检测 ───────────────────────
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def malformed_tool_calls(response: Any) -> List[str]:
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"""检出 arguments 损坏(JSON 解析不了)的 tool_call,返回 [name(len=N), ...]。
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背景:deepseek-v4-flash 大参数工具调用偶发畸形 —— 流式 delta 错位把别处的内容
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碎片粘到 arguments 开头(如 `].cells[1].merge(...{"path":...}`),拼回来后 JSON
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解析直接失败。这种是上游瞬时抖动,不该入库污染上下文,调用方据此丢弃整轮重 roll。
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只看「解析失败」;空字符串 / 合法空对象不算畸形(交给 executor 按缺参数处理)。
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"""
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try:
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msg = response.choices[0].message
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except Exception:
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return []
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bad: List[str] = []
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for tc in (getattr(msg, "tool_calls", None) or []):
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raw = (getattr(tc.function, "arguments", None) or "").strip()
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if not raw:
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continue
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try:
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json.loads(raw)
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except (json.JSONDecodeError, ValueError):
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bad.append(f"{tc.function.name}(len={len(raw)})")
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return bad
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def toolcalls_partial_args(
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response: Any, required_by_tool: Dict[str, List[str]]
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) -> List[Tuple[Any, str, List[str]]]:
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"""检出「JSON 能解析、但必填 key 被吞掉」的畸形 tool_call。
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背景(2026-07,失败面板 #3:edit `缺少必填参数 ['path']` 跨 13 task/9 用户):流式
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arguments delta 乱序把某个键的值碎片瞬移拼进相邻字符串(实证 [1]:path 的
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`.../gen_final_report_v2.py` 被吞进 old_str 尾部),独立的 `"path"` 键随之消失。这类
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与 char-0 前缀畸形的关键区别是 **JSON parse 成功** → 既不命中 `malformed_tool_calls`
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(只抓 parse 失败)、salvage 也救不了(parse-to-end/key 白名单对合法但错位无能),一路
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漏到 executor 才在语义层报「缺必填参数」,回 [Error] 喂回模型 → 再拼再乱序,反复烧。
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判据(窄,避免误伤模型真漏参):解析为**非空 dict** 且 **至少一个必填 key 在场**同时
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**至少一个必填 key 缺失**(即 0 < len(missing) < len(required))。空 `{}` / 必填全缺
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(纯垃圾/无关键)不算 —— 交给 executor + _RepeatGuard 现状处理。
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返回 [(tc, name, missing_keys), ...]。
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"""
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try:
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msg = response.choices[0].message
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except Exception:
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return []
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out: List[Tuple[Any, str, List[str]]] = []
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for tc in (getattr(msg, "tool_calls", None) or []):
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try:
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name = tc.function.name
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raw = (getattr(tc.function, "arguments", None) or "").strip()
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except Exception:
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continue
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if not raw:
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continue
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try:
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obj = json.loads(raw)
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except (json.JSONDecodeError, ValueError):
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continue # parse 失败归 malformed_tool_calls,不重复处理
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if not isinstance(obj, dict) or not obj:
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continue
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required = required_by_tool.get(name) or []
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if not required:
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continue
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missing = [k for k in required if k not in obj]
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if 0 < len(missing) < len(required):
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out.append((tc, name, missing))
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return out
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def is_empty_response(response: Any) -> bool:
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"""检出「空响应」:assistant 轮既无 tool_calls 又无正文(去空白后为空)。
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背景(task 2a1bc25d 案):provider wire 偶发吐空 —— 截断流 / finish_reason 无内容 /
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网关把 tool_use 漏成正文后又丢空,回来的这一轮 tool_calls 空且 content 空。run loop
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见 tool_calls 空即当「模型答完」静默 done、返回空串,无报错、run_status=idle,与卡死
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无法区分。故和畸形同类对待:丢弃本轮走非流式重试。
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注意:纯 tool_call 轮(tc 非空、content 空)不算空响应 —— 那是正常的工具调用轮。
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"""
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try:
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msg = response.choices[0].message
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except (AttributeError, IndexError, TypeError):
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return False
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if getattr(msg, "tool_calls", None):
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return False
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content = getattr(msg, "content", None) or ""
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return not content.strip()
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def finish_reason(response: Any) -> str:
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"""取本轮 finish_reason(取不到返 "")。length=达输出上限被截断,与 wire 吐空区分开:
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截断是我方输出预算/推理失控(如 GLM thinking 烧穿),同上下文重试无效。"""
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try:
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return getattr(response.choices[0], "finish_reason", "") or ""
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except (AttributeError, IndexError, TypeError):
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return ""
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# ─────────────────────── 故障留痕(stdout + usage_events 双写,静默失败)───────────────────────
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def log_partial_args(
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task_id: Any, user_id: Any, model_profile: str,
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partial: List[Tuple[Any, str, List[str]]], response: Any,
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) -> None:
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"""必填 key 被吞的畸形留痕:与 log_malformed_args 对称,进 usage_events(kind=
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tool_malformed),error 签名固定为 `missing required keys [...]` —— 在失败面板里和
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char-0 型(`Expecting value`)、executor 的「缺必填参数」区分开,便于统计这条新裂缝。
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任何一路失败都静默,绝不打断重试主路径。"""
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try:
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usage = extract_usage_details(getattr(response, "usage", None))
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for tc, name, missing in partial:
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raw = (getattr(tc.function, "arguments", None) or "")
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err = f"missing required keys {missing}"
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print(
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f"[malformed:partial] task={task_id} tool={name} len={len(raw)} "
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f"{err} head={ascii(raw[:300])} tail={ascii(raw[-300:])}",
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flush=True,
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)
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try:
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record_malformed_tool_call(
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task_id=task_id,
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user_id=user_id,
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model_profile=model_profile,
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tool=name,
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arg_len=len(raw),
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error=err,
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head=raw[:300],
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tail=raw[-300:],
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tokens_in=usage["tokens_in"],
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tokens_out=usage["tokens_out"],
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)
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except Exception:
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pass
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except Exception:
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pass
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def log_empty_response(
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task_id: Any, user_id: Any, model_profile: str, response: Any, attempt: int,
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finish: str = "",
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) -> None:
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"""空响应留痕:stdout + usage_events(kind=empty_response)双写,任何一路失败都静默。
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与 log_malformed_args 对称:空响应轮同样整轮丢弃、messages 无痕,这里是唯一留痕 ——
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供事后定性(哪个模型档在吐空)与「工具失败聚集」面板第四段聚合。留痕绝不能反过来打断
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重试主路径(单测无 DB 时 record_* 落库失败也吞掉)。
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"""
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try:
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usage = extract_usage_details(getattr(response, "usage", None))
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print(
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f"[empty_response] task={task_id} mp={model_profile} attempt={attempt} "
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f"finish={finish or '?'} tok={usage['tokens_in']}/{usage['tokens_out']}",
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flush=True,
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)
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try:
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record_empty_response(
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task_id=task_id,
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user_id=user_id,
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model_profile=model_profile,
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attempt=attempt,
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tokens_in=usage["tokens_in"],
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tokens_out=usage["tokens_out"],
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finish_reason=finish,
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)
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except Exception:
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pass # DB 不可用(如单测无 DB)不影响 stdout 留痕与重试
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except Exception:
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pass
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def log_malformed_args(
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task_id: Any, user_id: Any, model_profile: str, response: Any,
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) -> None:
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"""畸形 arguments 的首尾片段 + JSON 报错位置留痕:stdout + usage_events 双写。
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畸形轮不 append/不记账,这里是唯一留痕 —— 用于事后定性损坏形态(provider 流式
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delta 错位 vs 本地 stream_chunk_builder 拼接 bug),以及向 provider 报 case 取证。
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stdout 片段过 ascii() 转义:日志消费端编码不可控(Windows dev 控制台 GBK 遇 emoji
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会崩),转义后 grep '\\[malformed\\]' 拿到的内容可无损还原。DB 行(kind=tool_malformed,
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cost=0)喂 admin「工具失败聚集」面板 + 巡检邮件(core/toolfail.py),units 里
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快照该轮真实 token 供估算浪费。任何一路失败都静默 —— 留痕绝不能反过来打断重试主路径。
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"""
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try:
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msg = response.choices[0].message
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usage = extract_usage_details(getattr(response, "usage", None))
|
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for tc in (getattr(msg, "tool_calls", None) or []):
|
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raw = (getattr(tc.function, "arguments", None) or "").strip()
|
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if not raw:
|
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continue
|
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try:
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json.loads(raw)
|
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except (json.JSONDecodeError, ValueError) as e:
|
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print(
|
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f"[malformed] task={task_id} tool={tc.function.name} "
|
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f"len={len(raw)} err={e} "
|
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f"head={ascii(raw[:300])} tail={ascii(raw[-300:])}",
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flush=True,
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)
|
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try:
|
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record_malformed_tool_call(
|
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task_id=task_id,
|
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user_id=user_id,
|
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model_profile=model_profile,
|
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tool=tc.function.name,
|
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arg_len=len(raw),
|
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error=str(e),
|
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head=raw[:300],
|
||||
tail=raw[-300:],
|
||||
tokens_in=usage["tokens_in"],
|
||||
tokens_out=usage["tokens_out"],
|
||||
)
|
||||
except Exception:
|
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pass # DB 不可用(如单测无 DB)不影响 stdout 留痕与重试
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
# ─────────────────────── 重试策略 ───────────────────────
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|
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def robust_stream(
|
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*,
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collect_stream: Callable[[List[dict]], Tuple[Optional[Any], bool]],
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nonstream: Callable[[List[dict]], Optional[Any]],
|
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try_salvage: Callable[[Any], bool],
|
||||
llm_messages: List[dict],
|
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required_by_tool: Dict[str, List[str]],
|
||||
emit: Callable[[dict], None],
|
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llm_start_event: dict,
|
||||
task_id: Any,
|
||||
user_id: Any,
|
||||
model_profile: str,
|
||||
max_attempts: int,
|
||||
) -> Tuple[Optional[Any], bool]:
|
||||
"""拉一轮 LLM 并保证返回的 tool_call arguments 可解析。
|
||||
|
||||
返回 (response, cancelled_mid_stream):
|
||||
- 正常完结 → (response, False);response shape 与非流式 completion() 等价
|
||||
- 中途 cancel → (None, True);已收 chunk 丢弃(非流式重试期间 cancel 同样)
|
||||
|
||||
畸形重试:deepseek v4 系(flash/pro 均实测踩过)大参数工具调用偶发把内容碎片
|
||||
错位粘进 arguments,拼回后 JSON 解析失败。这种损坏一旦入库会被每轮重发、诱导
|
||||
模型继续学坏(投毒级联)。故拼回后先校验 tool_call arguments 能否解析:不能 →
|
||||
丢弃整轮(不 append/不记账,原始损坏片段打服务端日志留痕)并立刻降级非流式重试
|
||||
(同轮流式失败强相关,重试不再走流式);全部尝试耗尽仍畸形则交给 executor 的
|
||||
invalid-JSON 分支返错给模型。重试消耗的 token 不单独记账。
|
||||
|
||||
流式只试一次:实测(2026-07,task 716ed3be,deepseek-v4-pro)3~4k 字符中文长文
|
||||
write 的流式重 roll 同轮连挂 3 次 —— 同轮失败强相关而非独立随机,首败即降级非流式,
|
||||
历史数据里非流式兜底从未再畸形。
|
||||
"""
|
||||
response = None
|
||||
for attempt in range(max_attempts):
|
||||
use_nonstream = attempt > 0
|
||||
# 每个 attempt 重发 llm_start(stats 同一份):非流式重试完成前零 delta 事件,
|
||||
# 而 warn 事件会让前端把当前文字段定稿关闭 —— 不重发的话「思考中 · Ns」占位段
|
||||
# 没人重建,页面静止到重试完成,与卡死无法区分。
|
||||
emit(dict(llm_start_event))
|
||||
if use_nonstream:
|
||||
response = nonstream(llm_messages)
|
||||
if response is None:
|
||||
# 非流式重试期间用户点了停止(线程级 poll,见 loop._nonstream_once)
|
||||
return None, True
|
||||
else:
|
||||
response, cancelled = collect_stream(llm_messages)
|
||||
if cancelled:
|
||||
return None, True
|
||||
|
||||
bad = malformed_tool_calls(response)
|
||||
if not bad:
|
||||
# 空响应(tc 空且正文空):provider wire 吐空,和畸形同类瞬态故障 ——
|
||||
# 丢弃本轮走非流式重试(多数瞬态重发一次即好,用户无感),留痕供面板可见。
|
||||
if is_empty_response(response):
|
||||
fr = finish_reason(response)
|
||||
log_empty_response(
|
||||
task_id, user_id, model_profile, response, attempt + 1, finish=fr,
|
||||
)
|
||||
# length=达输出上限被截断(我方输出预算/推理烧穿,非网关 wire 吐空)——
|
||||
# 同上下文重试大概率再撞,措辞据实区分,便于用户/日志判性质(治本在
|
||||
# 模型档,如 GLM 已禁 thinking 免推理烧穿;这里保证可观测 + 不误导)。
|
||||
truncated = fr == "length"
|
||||
emit({
|
||||
"type": "warn",
|
||||
"msg": (
|
||||
("模型输出达上限被截断" if truncated else "模型返回空响应")
|
||||
+ ",丢弃本轮"
|
||||
f"{'重试' if use_nonstream else ',改非流式重试'}"
|
||||
f" ({attempt + 1}/{max_attempts})"
|
||||
),
|
||||
})
|
||||
continue
|
||||
# 必填 key 被吞的畸形(parse 成功、salvage 无能):非流式重试(服务端一次拼好,
|
||||
# 绕开流式 delta 乱序)。耗尽尝试仍缺 → 落下面 return,交 executor 返「缺必填参数」
|
||||
# 给模型(多为模型真漏参,不再空转)。
|
||||
partial = toolcalls_partial_args(response, required_by_tool)
|
||||
if partial:
|
||||
log_partial_args(task_id, user_id, model_profile, partial, response)
|
||||
names = ", ".join(f"{n}(missing={m})" for _, n, m in partial)
|
||||
emit({
|
||||
"type": "warn",
|
||||
"msg": (
|
||||
f"工具调用必填参数被吞 {names},丢弃本轮"
|
||||
f"{'重试' if use_nonstream else ',改非流式重试'}"
|
||||
f" ({attempt + 1}/{max_attempts})"
|
||||
),
|
||||
})
|
||||
continue
|
||||
return response, False
|
||||
# 先尝试就地抢救:畸形是 char-0 垃圾前缀 + 尾部完好 JSON(定层已证 provider-wire),
|
||||
# 全部畸形 tool_call 都能抠出干净 JSON 才改写并当轮继续,省掉一次非流式重试;
|
||||
# 任一抠不出则一个都不动,原样走下面的丢弃 + 重试(零回退风险)。
|
||||
if try_salvage(response):
|
||||
return response, False
|
||||
log_malformed_args(task_id, user_id, model_profile, response)
|
||||
emit({
|
||||
"type": "warn",
|
||||
"msg": (
|
||||
f"工具调用参数损坏 {bad},丢弃本轮"
|
||||
f"{'重试' if use_nonstream else ',改非流式重试'}"
|
||||
f" ({attempt + 1}/{max_attempts})"
|
||||
),
|
||||
})
|
||||
# 非流式重试仍畸形(理论极罕见):交还给 _execute_tool_call 的 invalid-JSON 分支
|
||||
# 优雅返错给模型,而非在此死循环。
|
||||
return response, False
|
||||
642
core/loop.py
642
core/loop.py
|
|
@ -32,12 +32,16 @@ from .context import (
|
|||
from .context_fold import maybe_fold
|
||||
from .executor import ExecCtx, Executor
|
||||
from .llm import LLM
|
||||
from .llm_transport import (
|
||||
extract_delta_content,
|
||||
extract_delta_reasoning,
|
||||
extract_usage_details,
|
||||
robust_stream,
|
||||
)
|
||||
from .salvage import salvage_tool_arguments
|
||||
from .session import Session
|
||||
from .storage import (
|
||||
record_chat_usage,
|
||||
record_empty_response,
|
||||
record_malformed_tool_call,
|
||||
record_salvaged_tool_call,
|
||||
)
|
||||
from . import pptx_guard
|
||||
|
|
@ -78,7 +82,7 @@ class _RepeatGuard:
|
|||
→ 无产出,累计。
|
||||
累计 >= SOFT 注入软提示(模型当轮就看到);>= HARD 直接拦截不执行,逼它换路。
|
||||
|
||||
顺带堵掉 `_malformed_tool_calls` 的洞:大参数畸形退化成合法空 `{}` 时,executor 每次
|
||||
顺带堵掉 `llm_transport.malformed_tool_calls` 的洞:大参数畸形退化成合法空 `{}` 时,executor 每次
|
||||
返回同一句「缺少必填参数」→ 走 dup 分支被这同一机制拦下,无需单独特判空 `{}`。
|
||||
|
||||
第二道判据(2026-07,失败面板 #2:edit `old_str not found` 单 task 反复撞墙):模型每次
|
||||
|
|
@ -179,307 +183,6 @@ class _RepeatGuard:
|
|||
return cnt, esig
|
||||
|
||||
|
||||
def _extract_delta_content(chunk: Any) -> Optional[str]:
|
||||
"""从 stream chunk 提 delta.content(文本片段)。chunk 形态 litellm ModelResponseStream:
|
||||
choices[0].delta.content。usage-only 收尾 chunk(没 choices / delta)返 None。
|
||||
"""
|
||||
try:
|
||||
choices = getattr(chunk, "choices", None)
|
||||
if not choices:
|
||||
return None
|
||||
delta = getattr(choices[0], "delta", None)
|
||||
if delta is None:
|
||||
return None
|
||||
content = getattr(delta, "content", None)
|
||||
return content if content else None
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
def _extract_delta_reasoning(chunk: Any) -> Optional[str]:
|
||||
"""从 stream chunk 提 delta.reasoning_content(thinking 模型的推理片段)。
|
||||
litellm 对多数 provider 归一到 delta.reasoning_content,个别只放
|
||||
provider_specific_fields —— 两处都查。没有则返 None(非 thinking 模型零开销)。
|
||||
"""
|
||||
try:
|
||||
choices = getattr(chunk, "choices", None)
|
||||
if not choices:
|
||||
return None
|
||||
delta = getattr(choices[0], "delta", None)
|
||||
if delta is None:
|
||||
return None
|
||||
rc = getattr(delta, "reasoning_content", None)
|
||||
if not rc:
|
||||
psf = getattr(delta, "provider_specific_fields", None) or {}
|
||||
rc = psf.get("reasoning_content") if isinstance(psf, dict) else None
|
||||
return rc if rc else None
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
def _malformed_tool_calls(response: Any) -> List[str]:
|
||||
"""检出 arguments 损坏(JSON 解析不了)的 tool_call,返回 [name(len=N), ...]。
|
||||
|
||||
背景:deepseek-v4-flash 大参数工具调用偶发畸形 —— 流式 delta 错位把别处的内容
|
||||
碎片粘到 arguments 开头(如 `].cells[1].merge(...{"path":...}`),拼回来后 JSON
|
||||
解析直接失败。这种是上游瞬时抖动,不该入库污染上下文,调用方据此丢弃整轮重 roll。
|
||||
|
||||
只看「解析失败」;空字符串 / 合法空对象不算畸形(交给 executor 按缺参数处理)。
|
||||
"""
|
||||
try:
|
||||
msg = response.choices[0].message
|
||||
except Exception:
|
||||
return []
|
||||
bad: List[str] = []
|
||||
for tc in (getattr(msg, "tool_calls", None) or []):
|
||||
raw = (getattr(tc.function, "arguments", None) or "").strip()
|
||||
if not raw:
|
||||
continue
|
||||
try:
|
||||
json.loads(raw)
|
||||
except (json.JSONDecodeError, ValueError):
|
||||
bad.append(f"{tc.function.name}(len={len(raw)})")
|
||||
return bad
|
||||
|
||||
|
||||
def _toolcalls_partial_args(
|
||||
response: Any, required_by_tool: Dict[str, List[str]]
|
||||
) -> List[Tuple[Any, str, List[str]]]:
|
||||
"""检出「JSON 能解析、但必填 key 被吞掉」的畸形 tool_call。
|
||||
|
||||
背景(2026-07,失败面板 #3:edit `缺少必填参数 ['path']` 跨 13 task/9 用户):流式
|
||||
arguments delta 乱序把某个键的值碎片瞬移拼进相邻字符串(实证 [1]:path 的
|
||||
`.../gen_final_report_v2.py` 被吞进 old_str 尾部),独立的 `"path"` 键随之消失。这类
|
||||
与 char-0 前缀畸形的关键区别是 **JSON parse 成功** → 既不命中 `_malformed_tool_calls`
|
||||
(只抓 parse 失败)、salvage 也救不了(parse-to-end/key 白名单对合法但错位无能),一路
|
||||
漏到 executor 才在语义层报「缺必填参数」,回 [Error] 喂回模型 → 再拼再乱序,反复烧。
|
||||
|
||||
判据(窄,避免误伤模型真漏参):解析为**非空 dict** 且 **至少一个必填 key 在场**同时
|
||||
**至少一个必填 key 缺失**(即 0 < len(missing) < len(required))。空 `{}` / 必填全缺
|
||||
(纯垃圾/无关键)不算 —— 交给 executor + _RepeatGuard 现状处理。
|
||||
|
||||
返回 [(tc, name, missing_keys), ...]。
|
||||
"""
|
||||
try:
|
||||
msg = response.choices[0].message
|
||||
except Exception:
|
||||
return []
|
||||
out: List[Tuple[Any, str, List[str]]] = []
|
||||
for tc in (getattr(msg, "tool_calls", None) or []):
|
||||
try:
|
||||
name = tc.function.name
|
||||
raw = (getattr(tc.function, "arguments", None) or "").strip()
|
||||
except Exception:
|
||||
continue
|
||||
if not raw:
|
||||
continue
|
||||
try:
|
||||
obj = json.loads(raw)
|
||||
except (json.JSONDecodeError, ValueError):
|
||||
continue # parse 失败归 _malformed_tool_calls,不重复处理
|
||||
if not isinstance(obj, dict) or not obj:
|
||||
continue
|
||||
required = required_by_tool.get(name) or []
|
||||
if not required:
|
||||
continue
|
||||
missing = [k for k in required if k not in obj]
|
||||
if 0 < len(missing) < len(required):
|
||||
out.append((tc, name, missing))
|
||||
return out
|
||||
|
||||
|
||||
def _log_partial_args(
|
||||
task_id: Any, user_id: Any, model_profile: str,
|
||||
partial: List[Tuple[Any, str, List[str]]], response: Any,
|
||||
) -> None:
|
||||
"""必填 key 被吞的畸形留痕:与 _log_malformed_args 对称,进 usage_events(kind=
|
||||
tool_malformed),error 签名固定为 `missing required keys [...]` —— 在失败面板里和
|
||||
char-0 型(`Expecting value`)、executor 的「缺必填参数」区分开,便于统计这条新裂缝。
|
||||
任何一路失败都静默,绝不打断重试主路径。"""
|
||||
try:
|
||||
usage = _extract_usage_details(getattr(response, "usage", None))
|
||||
for tc, name, missing in partial:
|
||||
raw = (getattr(tc.function, "arguments", None) or "")
|
||||
err = f"missing required keys {missing}"
|
||||
print(
|
||||
f"[malformed:partial] task={task_id} tool={name} len={len(raw)} "
|
||||
f"{err} head={ascii(raw[:300])} tail={ascii(raw[-300:])}",
|
||||
flush=True,
|
||||
)
|
||||
try:
|
||||
record_malformed_tool_call(
|
||||
task_id=task_id,
|
||||
user_id=user_id,
|
||||
model_profile=model_profile,
|
||||
tool=name,
|
||||
arg_len=len(raw),
|
||||
error=err,
|
||||
head=raw[:300],
|
||||
tail=raw[-300:],
|
||||
tokens_in=usage["tokens_in"],
|
||||
tokens_out=usage["tokens_out"],
|
||||
)
|
||||
except Exception:
|
||||
pass
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
def _is_empty_response(response: Any) -> bool:
|
||||
"""检出「空响应」:assistant 轮既无 tool_calls 又无正文(去空白后为空)。
|
||||
|
||||
背景(task 2a1bc25d 案):provider wire 偶发吐空 —— 截断流 / finish_reason 无内容 /
|
||||
网关把 tool_use 漏成正文后又丢空,回来的这一轮 tool_calls 空且 content 空。run loop
|
||||
见 tool_calls 空即当「模型答完」静默 done、返回空串,无报错、run_status=idle,与卡死
|
||||
无法区分。故和畸形同类对待:丢弃本轮走非流式重试。
|
||||
注意:纯 tool_call 轮(tc 非空、content 空)不算空响应 —— 那是正常的工具调用轮。
|
||||
"""
|
||||
try:
|
||||
msg = response.choices[0].message
|
||||
except (AttributeError, IndexError, TypeError):
|
||||
return False
|
||||
if getattr(msg, "tool_calls", None):
|
||||
return False
|
||||
content = getattr(msg, "content", None) or ""
|
||||
return not content.strip()
|
||||
|
||||
|
||||
def _finish_reason(response: Any) -> str:
|
||||
"""取本轮 finish_reason(取不到返 "")。length=达输出上限被截断,与 wire 吐空区分开:
|
||||
截断是我方输出预算/推理失控(如 GLM thinking 烧穿),同上下文重试无效。"""
|
||||
try:
|
||||
return getattr(response.choices[0], "finish_reason", "") or ""
|
||||
except (AttributeError, IndexError, TypeError):
|
||||
return ""
|
||||
|
||||
|
||||
def _log_empty_response(
|
||||
task_id: Any, user_id: Any, model_profile: str, response: Any, attempt: int,
|
||||
finish_reason: str = "",
|
||||
) -> None:
|
||||
"""空响应留痕:stdout + usage_events(kind=empty_response)双写,任何一路失败都静默。
|
||||
|
||||
与 _log_malformed_args 对称:空响应轮同样整轮丢弃、messages 无痕,这里是唯一留痕 ——
|
||||
供事后定性(哪个模型档在吐空)与「工具失败聚集」面板第四段聚合。留痕绝不能反过来打断
|
||||
重试主路径(单测无 DB 时 record_* 落库失败也吞掉)。
|
||||
"""
|
||||
try:
|
||||
usage = _extract_usage_details(getattr(response, "usage", None))
|
||||
print(
|
||||
f"[empty_response] task={task_id} mp={model_profile} attempt={attempt} "
|
||||
f"finish={finish_reason or '?'} tok={usage['tokens_in']}/{usage['tokens_out']}",
|
||||
flush=True,
|
||||
)
|
||||
try:
|
||||
record_empty_response(
|
||||
task_id=task_id,
|
||||
user_id=user_id,
|
||||
model_profile=model_profile,
|
||||
attempt=attempt,
|
||||
tokens_in=usage["tokens_in"],
|
||||
tokens_out=usage["tokens_out"],
|
||||
finish_reason=finish_reason,
|
||||
)
|
||||
except Exception:
|
||||
pass # DB 不可用(如单测无 DB)不影响 stdout 留痕与重试
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
def _log_malformed_args(
|
||||
task_id: Any, user_id: Any, model_profile: str, response: Any,
|
||||
) -> None:
|
||||
"""畸形 arguments 的首尾片段 + JSON 报错位置留痕:stdout + usage_events 双写。
|
||||
|
||||
畸形轮不 append/不记账,这里是唯一留痕 —— 用于事后定性损坏形态(provider 流式
|
||||
delta 错位 vs 本地 stream_chunk_builder 拼接 bug),以及向 provider 报 case 取证。
|
||||
stdout 片段过 ascii() 转义:日志消费端编码不可控(Windows dev 控制台 GBK 遇 emoji
|
||||
会崩),转义后 grep '\\[malformed\\]' 拿到的内容可无损还原。DB 行(kind=tool_malformed,
|
||||
cost=0)喂 admin「工具失败聚集」面板 + 巡检邮件(core/toolfail.py),units 里
|
||||
快照该轮真实 token 供估算浪费。任何一路失败都静默 —— 留痕绝不能反过来打断重试主路径。
|
||||
"""
|
||||
try:
|
||||
msg = response.choices[0].message
|
||||
usage = _extract_usage_details(getattr(response, "usage", None))
|
||||
for tc in (getattr(msg, "tool_calls", None) or []):
|
||||
raw = (getattr(tc.function, "arguments", None) or "").strip()
|
||||
if not raw:
|
||||
continue
|
||||
try:
|
||||
json.loads(raw)
|
||||
except (json.JSONDecodeError, ValueError) as e:
|
||||
print(
|
||||
f"[malformed] task={task_id} tool={tc.function.name} "
|
||||
f"len={len(raw)} err={e} "
|
||||
f"head={ascii(raw[:300])} tail={ascii(raw[-300:])}",
|
||||
flush=True,
|
||||
)
|
||||
try:
|
||||
record_malformed_tool_call(
|
||||
task_id=task_id,
|
||||
user_id=user_id,
|
||||
model_profile=model_profile,
|
||||
tool=tc.function.name,
|
||||
arg_len=len(raw),
|
||||
error=str(e),
|
||||
head=raw[:300],
|
||||
tail=raw[-300:],
|
||||
tokens_in=usage["tokens_in"],
|
||||
tokens_out=usage["tokens_out"],
|
||||
)
|
||||
except Exception:
|
||||
pass # DB 不可用(如单测无 DB)不影响 stdout 留痕与重试
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
def _usage_to_dict(usage: Any) -> dict:
|
||||
if not usage:
|
||||
return {}
|
||||
if hasattr(usage, "model_dump"):
|
||||
usage = usage.model_dump()
|
||||
elif hasattr(usage, "dict"):
|
||||
usage = usage.dict()
|
||||
if isinstance(usage, dict):
|
||||
return usage
|
||||
return {}
|
||||
|
||||
|
||||
def _extract_usage_details(usage: Any) -> dict:
|
||||
"""从 provider usage 提取统一 token 明细。
|
||||
|
||||
DeepSeek 直接给 prompt_cache_hit_tokens / prompt_cache_miss_tokens;
|
||||
OpenAI 风格把 cached tokens 放在 prompt_tokens_details.cached_tokens。
|
||||
"""
|
||||
data = _usage_to_dict(usage)
|
||||
prompt_details = data.get("prompt_tokens_details") or {}
|
||||
completion_details = data.get("completion_tokens_details") or {}
|
||||
if not isinstance(prompt_details, dict):
|
||||
prompt_details = {}
|
||||
if not isinstance(completion_details, dict):
|
||||
completion_details = {}
|
||||
|
||||
cache_hit = (
|
||||
data.get("prompt_cache_hit_tokens")
|
||||
or prompt_details.get("cached_tokens")
|
||||
or 0
|
||||
)
|
||||
cache_miss = data.get("prompt_cache_miss_tokens") or 0
|
||||
return {
|
||||
"tokens_in": int(data.get("prompt_tokens") or 0),
|
||||
"tokens_out": int(data.get("completion_tokens") or 0),
|
||||
"cache_hit_tokens": int(cache_hit or 0),
|
||||
"cache_miss_tokens": int(cache_miss or 0),
|
||||
"reasoning_tokens": int(completion_details.get("reasoning_tokens") or 0),
|
||||
}
|
||||
|
||||
|
||||
def _extract_usage(usage: Any) -> Tuple[int, int]:
|
||||
"""从 litellm response.usage 提 (prompt_tokens, completion_tokens)。"""
|
||||
details = _extract_usage_details(usage)
|
||||
return details["tokens_in"], details["tokens_out"]
|
||||
|
||||
|
||||
class AgentLoop:
|
||||
def __init__(
|
||||
self,
|
||||
|
|
@ -558,7 +261,7 @@ class AgentLoop:
|
|||
msg = response.choices[0].message
|
||||
asst_msg_id = self.session.append(msg)
|
||||
|
||||
usage_details = _extract_usage_details(getattr(response, "usage", None))
|
||||
usage_details = extract_usage_details(getattr(response, "usage", None))
|
||||
pt, ct = usage_details["tokens_in"], usage_details["tokens_out"]
|
||||
# 用本轮实报 prompt_tokens 刷新 chars/token 校准比值(下一轮门槛/占用环即用)。
|
||||
if pt > 0 and self._last_sent_chars > 0:
|
||||
|
|
@ -730,20 +433,11 @@ class AgentLoop:
|
|||
})
|
||||
|
||||
def _stream_llm(self) -> Tuple[Optional[Any], bool]:
|
||||
"""拉一轮 LLM 并保证返回的 tool_call arguments 可解析。
|
||||
"""拉一轮 LLM:上下文压缩准备(context 关注点)在此,wire 层健壮性(畸形/空响应
|
||||
检测、非流式降级重试、salvage)委托 llm_transport.robust_stream —— 取流两条路径
|
||||
以 bound method 传入,单测在实例上打桩 _collect_stream_once/_nonstream_once 即可。
|
||||
|
||||
返回 (response, cancelled_mid_stream):
|
||||
- 正常完结 → (response, False);response shape 与非流式 completion() 等价
|
||||
(choices[0].message + usage)
|
||||
- 中途 cancel → (None, True);已收 chunk 丢弃,内层 generator 在 finally 关闭底层连接。
|
||||
非流式重试期间 cancel 同样 (None, True)(线程级 poll,见 _nonstream_once)
|
||||
|
||||
畸形重试:deepseek v4 系(flash/pro 均实测踩过)大参数工具调用偶发把内容碎片
|
||||
错位粘进 arguments,拼回后 JSON 解析失败。这种损坏一旦入库会被每轮重发、诱导
|
||||
模型继续学坏(投毒级联)。故拼回后先校验 tool_call arguments 能否解析:不能 →
|
||||
丢弃整轮(不 append/不记账,原始损坏片段打服务端日志留痕)并立刻降级非流式重试
|
||||
(同轮流式失败强相关,重试不再走流式);全部尝试耗尽仍畸形则交给 executor 的
|
||||
invalid-JSON 分支返错给模型。重试消耗的 token 不单独记账。
|
||||
返回 (response, cancelled_mid_stream);语义见 robust_stream docstring。
|
||||
"""
|
||||
# 上下文压力门槛按当前模型 reliable_context 折算:体量未到阈值前不压缩(缓存全暖 + 不丢信息)。
|
||||
# 换算比值走校准态(实报 usage 优先,回退 2.5)—— 门槛语义是 token 口径,chars 只是载体。
|
||||
|
|
@ -768,87 +462,19 @@ class AgentLoop:
|
|||
sc["function"]["name"]: (sc["function"].get("parameters") or {}).get("required") or []
|
||||
for sc in self.executor.schemas()
|
||||
}
|
||||
for attempt in range(self._MAX_MALFORMED_ATTEMPTS):
|
||||
use_nonstream = attempt > 0
|
||||
# 每个 attempt 重发 llm_start(stats 同一份):非流式重试完成前零 delta 事件,
|
||||
# 而 warn 事件会让前端把当前文字段定稿关闭 —— 不重发的话「思考中 · Ns」占位段
|
||||
# 没人重建,页面静止到重试完成,与卡死无法区分。
|
||||
self._emit(dict(llm_start_event))
|
||||
if use_nonstream:
|
||||
response = self._nonstream_once(llm_messages)
|
||||
if response is None:
|
||||
# 非流式重试期间用户点了停止(线程级 poll,见 _nonstream_once)
|
||||
return None, True
|
||||
else:
|
||||
response, cancelled = self._collect_stream_once(llm_messages)
|
||||
if cancelled:
|
||||
return None, True
|
||||
|
||||
bad = _malformed_tool_calls(response)
|
||||
if not bad:
|
||||
# 空响应(tc 空且正文空):provider wire 吐空,和畸形同类瞬态故障 ——
|
||||
# 丢弃本轮走非流式重试(多数瞬态重发一次即好,用户无感),留痕供面板可见。
|
||||
if _is_empty_response(response):
|
||||
fr = _finish_reason(response)
|
||||
_log_empty_response(
|
||||
self.session.task_id, self.user_id,
|
||||
f"{self.caps.family}.{self.caps.variant}", response, attempt + 1,
|
||||
finish_reason=fr,
|
||||
)
|
||||
# length=达输出上限被截断(我方输出预算/推理烧穿,非网关 wire 吐空)——
|
||||
# 同上下文重试大概率再撞,措辞据实区分,便于用户/日志判性质(治本在
|
||||
# 模型档,如 GLM 已禁 thinking 免推理烧穿;这里保证可观测 + 不误导)。
|
||||
truncated = fr == "length"
|
||||
self._emit({
|
||||
"type": "warn",
|
||||
"msg": (
|
||||
("模型输出达上限被截断" if truncated else "模型返回空响应")
|
||||
+ ",丢弃本轮"
|
||||
f"{'重试' if use_nonstream else ',改非流式重试'}"
|
||||
f" ({attempt + 1}/{self._MAX_MALFORMED_ATTEMPTS})"
|
||||
),
|
||||
})
|
||||
continue
|
||||
# 必填 key 被吞的畸形(parse 成功、salvage 无能):非流式重试(服务端一次拼好,
|
||||
# 绕开流式 delta 乱序)。耗尽尝试仍缺 → 落下面 return,交 executor 返「缺必填参数」
|
||||
# 给模型(多为模型真漏参,不再空转)。
|
||||
partial = _toolcalls_partial_args(response, required_by_tool)
|
||||
if partial:
|
||||
_log_partial_args(
|
||||
self.session.task_id, self.user_id,
|
||||
f"{self.caps.family}.{self.caps.variant}", partial, response,
|
||||
)
|
||||
names = ", ".join(f"{n}(missing={m})" for _, n, m in partial)
|
||||
self._emit({
|
||||
"type": "warn",
|
||||
"msg": (
|
||||
f"工具调用必填参数被吞 {names},丢弃本轮"
|
||||
f"{'重试' if use_nonstream else ',改非流式重试'}"
|
||||
f" ({attempt + 1}/{self._MAX_MALFORMED_ATTEMPTS})"
|
||||
),
|
||||
})
|
||||
continue
|
||||
return response, False
|
||||
# 先尝试就地抢救:畸形是 char-0 垃圾前缀 + 尾部完好 JSON(定层已证 provider-wire),
|
||||
# 全部畸形 tool_call 都能抠出干净 JSON 才改写并当轮继续,省掉一次非流式重试;
|
||||
# 任一抠不出则一个都不动,原样走下面的丢弃 + 重试(零回退风险)。
|
||||
if self._try_salvage_response(response):
|
||||
return response, False
|
||||
_log_malformed_args(
|
||||
self.session.task_id, self.user_id,
|
||||
f"{self.caps.family}.{self.caps.variant}", response,
|
||||
)
|
||||
self._emit({
|
||||
"type": "warn",
|
||||
"msg": (
|
||||
f"工具调用参数损坏 {bad},丢弃本轮"
|
||||
f"{'重试' if use_nonstream else ',改非流式重试'}"
|
||||
f" ({attempt + 1}/{self._MAX_MALFORMED_ATTEMPTS})"
|
||||
),
|
||||
})
|
||||
# 非流式重试仍畸形(理论极罕见):交还给 _execute_tool_call 的 invalid-JSON 分支
|
||||
# 优雅返错给模型,而非在此死循环。
|
||||
return response, False
|
||||
return robust_stream(
|
||||
collect_stream=self._collect_stream_once,
|
||||
nonstream=self._nonstream_once,
|
||||
try_salvage=self._try_salvage_response,
|
||||
llm_messages=llm_messages,
|
||||
required_by_tool=required_by_tool,
|
||||
emit=self._emit,
|
||||
llm_start_event=llm_start_event,
|
||||
task_id=self.session.task_id,
|
||||
user_id=self.user_id,
|
||||
model_profile=f"{self.caps.family}.{self.caps.variant}",
|
||||
max_attempts=self._MAX_MALFORMED_ATTEMPTS,
|
||||
)
|
||||
|
||||
def _try_salvage_response(self, response: Any) -> bool:
|
||||
"""尝试就地抢救本轮所有畸形 tool_call 的 arguments;成功才改写并返回 True。
|
||||
|
|
@ -939,12 +565,12 @@ class AgentLoop:
|
|||
# delta.content 即时 emit 给前端打字机渲染;tool_call delta 不实时发
|
||||
# (拼接散在多 chunk 跨 frame 难看,等拼回后整条 tool_call 事件由
|
||||
# _execute_tool_call 时机发更直观)。
|
||||
delta_text = _extract_delta_content(chunk)
|
||||
delta_text = extract_delta_content(chunk)
|
||||
if delta_text:
|
||||
self._emit({"type": "text", "delta": delta_text})
|
||||
# thinking 模型的推理 delta 也实时流出(reasoning 事件):深度推理可达
|
||||
# 分钟级,不发的话前端全程静止"思考中",用户以为卡死。
|
||||
delta_reasoning = _extract_delta_reasoning(chunk)
|
||||
delta_reasoning = extract_delta_reasoning(chunk)
|
||||
if delta_reasoning:
|
||||
self._emit({"type": "reasoning", "delta": delta_reasoning})
|
||||
finally:
|
||||
|
|
@ -1010,7 +636,11 @@ class AgentLoop:
|
|||
|
||||
def _execute_tool_call(self, tc: Any) -> Tuple[str, bool]:
|
||||
"""执行一次 tool_call,返回 (结果文本, 本次是否有净产出)。
|
||||
净产出供 run loop 的全局「无进展」熔断判定。"""
|
||||
净产出供 run loop 的全局「无进展」熔断判定。
|
||||
|
||||
编排四个正交环节(各自独立方法):重复拦截(执行前)→ 真正执行 + 截断 →
|
||||
skill 定向模型热切(load_skill 后)→ 重复登记/软提示 + pptx 产物机检(执行后)。
|
||||
"""
|
||||
name = tc.function.name
|
||||
raw_args = tc.function.arguments or "{}"
|
||||
try:
|
||||
|
|
@ -1028,43 +658,9 @@ class AgentLoop:
|
|||
"args_preview": args_preview,
|
||||
})
|
||||
|
||||
# 病理性重复拦截:同参已累计 HARD 次无产出重复 → 不执行,回硬停消息逼模型换路。
|
||||
if self._repeat_guard.should_block(name, args):
|
||||
n, blocked = self._repeat_guard.register_block(name, args)
|
||||
result = (
|
||||
f"[已拦截重复调用] {name} 用完全相同的参数已调用 {n} 次且结果始终未变,本次未执行。"
|
||||
"这通常意味着思路卡死:① 换不同的参数或方法;② 读一下相关文件/报错重新定位;"
|
||||
"③ 若确实推进不了,停下来如实告诉用户卡在哪、缺什么。不要再用相同参数重试。"
|
||||
)
|
||||
self._emit({"type": "warn", "msg": f"拦截重复调用 {name}(同参第 {n} 次、结果未变)"})
|
||||
self._emit({
|
||||
"type": "tool_result",
|
||||
"name": name,
|
||||
"result": result,
|
||||
"preview": result,
|
||||
"truncated": False,
|
||||
})
|
||||
return result, False
|
||||
|
||||
# err-streak 拦截:换着参数撞同一堵墙(如 edit 反复 old_str not found)。拦一次逼换路,
|
||||
# 拦后 streak 重置到 SOFT(非永久封死)。
|
||||
if self._repeat_guard.should_block_err(name):
|
||||
cnt, esig = self._repeat_guard.register_err_block(name)
|
||||
result = (
|
||||
f"[已拦截重复调用] {name} 已连续 {cnt} 次撞同一个错误「{esig}」(每次只微调了参数)。"
|
||||
"再这么试下去不会有新结果。换个做法:① 先 read 目标文件/用 grep 看确切内容"
|
||||
"(old_str 必须逐字匹配,含空白与缩进);② 或换工具/换思路;③ 实在推进不了就停下来"
|
||||
"如实告诉用户卡在哪。"
|
||||
)
|
||||
self._emit({"type": "warn", "msg": f"拦截撞墙调用 {name}(连续同错第 {cnt} 次)"})
|
||||
self._emit({
|
||||
"type": "tool_result",
|
||||
"name": name,
|
||||
"result": result,
|
||||
"preview": result,
|
||||
"truncated": False,
|
||||
})
|
||||
return result, False
|
||||
blocked = self._check_repeat_block(name, args)
|
||||
if blocked is not None:
|
||||
return blocked, False
|
||||
|
||||
ctx = ExecCtx(
|
||||
user_id=self.user_id,
|
||||
|
|
@ -1082,37 +678,100 @@ class AgentLoop:
|
|||
result = result[:MAX_LEN] + f"\n[... truncated, {len(result) - MAX_LEN} chars ...]"
|
||||
truncated = True
|
||||
|
||||
# skill 定向模型:load_skill 成功且该 skill frontmatter 指定了模型 → 热切,
|
||||
# 本 run 内下一轮 LLM 即用新模型(记账/压缩阈值/reasoning 都读 self.caps,自动跟上)。
|
||||
# 切换说明追加在截断之后,不会被 16k 截掉。切失败(配错/缺 key)→ warn 后原模型继续。
|
||||
if (
|
||||
name == "load_skill"
|
||||
and self.skill_model_switch is not None
|
||||
and not result.startswith("[Error]")
|
||||
):
|
||||
cur_profile = f"{self.caps.family}.{self.caps.variant}"
|
||||
try:
|
||||
switched = self.skill_model_switch(str(args.get("name", "")), cur_profile)
|
||||
except Exception as e:
|
||||
switched = None
|
||||
self._emit({
|
||||
"type": "warn",
|
||||
"msg": f"skill 定向模型切换失败,继续用 {cur_profile}: {type(e).__name__}: {e}",
|
||||
})
|
||||
if switched:
|
||||
new_profile, new_caps, new_llm = switched
|
||||
self.caps, self.llm = new_caps, new_llm
|
||||
result += (
|
||||
f"\n\n[模型切换] 该 skill 指定模型 {new_profile},"
|
||||
f"已从 {cur_profile} 自动切换,本 task 后续消息也沿用 {new_profile}。"
|
||||
)
|
||||
self._emit({
|
||||
"type": "model_switch",
|
||||
"model_profile": new_profile,
|
||||
"from": cur_profile,
|
||||
})
|
||||
result = self._maybe_skill_model_switch(name, args, result)
|
||||
result, productive = self._repeat_feedback(name, args, result)
|
||||
result = self._maybe_pptx_guard(name, tool_started_at, result)
|
||||
|
||||
# 登记结果做重复检测(用截断后、未加提示的原始结果算指纹,保证同输出哈希一致)。
|
||||
preview = result if len(result) < 400 else result[:400] + "..."
|
||||
self._emit({
|
||||
"type": "tool_result",
|
||||
"name": name,
|
||||
"result": result,
|
||||
"preview": preview,
|
||||
"truncated": truncated,
|
||||
})
|
||||
return result, productive
|
||||
|
||||
def _check_repeat_block(self, name: str, args: Any) -> Optional[str]:
|
||||
"""执行前的两道拦截(命中返回拦截话术,未命中返 None):
|
||||
|
||||
① 同参硬拦:同名同参已累计 HARD 次无产出重复 → 不执行,逼模型换路;
|
||||
② err-streak 拦:换着参数连撞同一类错(如 edit 反复 old_str not found)→
|
||||
拦一次,拦后 streak 重置到 SOFT(非永久封死,给换路后的重试留活口)。
|
||||
"""
|
||||
if self._repeat_guard.should_block(name, args):
|
||||
n, _blocked = self._repeat_guard.register_block(name, args)
|
||||
result = (
|
||||
f"[已拦截重复调用] {name} 用完全相同的参数已调用 {n} 次且结果始终未变,本次未执行。"
|
||||
"这通常意味着思路卡死:① 换不同的参数或方法;② 读一下相关文件/报错重新定位;"
|
||||
"③ 若确实推进不了,停下来如实告诉用户卡在哪、缺什么。不要再用相同参数重试。"
|
||||
)
|
||||
self._emit({"type": "warn", "msg": f"拦截重复调用 {name}(同参第 {n} 次、结果未变)"})
|
||||
self._emit_blocked_result(name, result)
|
||||
return result
|
||||
|
||||
if self._repeat_guard.should_block_err(name):
|
||||
cnt, esig = self._repeat_guard.register_err_block(name)
|
||||
result = (
|
||||
f"[已拦截重复调用] {name} 已连续 {cnt} 次撞同一个错误「{esig}」(每次只微调了参数)。"
|
||||
"再这么试下去不会有新结果。换个做法:① 先 read 目标文件/用 grep 看确切内容"
|
||||
"(old_str 必须逐字匹配,含空白与缩进);② 或换工具/换思路;③ 实在推进不了就停下来"
|
||||
"如实告诉用户卡在哪。"
|
||||
)
|
||||
self._emit({"type": "warn", "msg": f"拦截撞墙调用 {name}(连续同错第 {cnt} 次)"})
|
||||
self._emit_blocked_result(name, result)
|
||||
return result
|
||||
return None
|
||||
|
||||
def _emit_blocked_result(self, name: str, result: str) -> None:
|
||||
self._emit({
|
||||
"type": "tool_result",
|
||||
"name": name,
|
||||
"result": result,
|
||||
"preview": result,
|
||||
"truncated": False,
|
||||
})
|
||||
|
||||
def _maybe_skill_model_switch(self, name: str, args: Any, result: str) -> str:
|
||||
"""skill 定向模型:load_skill 成功且该 skill frontmatter 指定了模型 → 热切,
|
||||
本 run 内下一轮 LLM 即用新模型(记账/压缩阈值/reasoning 都读 self.caps,自动跟上)。
|
||||
切换说明追加在截断之后,不会被 16k 截掉。切失败(配错/缺 key)→ warn 后原模型继续。
|
||||
"""
|
||||
if (
|
||||
name != "load_skill"
|
||||
or self.skill_model_switch is None
|
||||
or result.startswith("[Error]")
|
||||
):
|
||||
return result
|
||||
cur_profile = f"{self.caps.family}.{self.caps.variant}"
|
||||
try:
|
||||
switched = self.skill_model_switch(str(args.get("name", "")), cur_profile)
|
||||
except Exception as e:
|
||||
switched = None
|
||||
self._emit({
|
||||
"type": "warn",
|
||||
"msg": f"skill 定向模型切换失败,继续用 {cur_profile}: {type(e).__name__}: {e}",
|
||||
})
|
||||
if switched:
|
||||
new_profile, new_caps, new_llm = switched
|
||||
self.caps, self.llm = new_caps, new_llm
|
||||
result += (
|
||||
f"\n\n[模型切换] 该 skill 指定模型 {new_profile},"
|
||||
f"已从 {cur_profile} 自动切换,本 task 后续消息也沿用 {new_profile}。"
|
||||
)
|
||||
self._emit({
|
||||
"type": "model_switch",
|
||||
"model_profile": new_profile,
|
||||
"from": cur_profile,
|
||||
})
|
||||
return result
|
||||
|
||||
def _repeat_feedback(self, name: str, args: Any, result: str) -> Tuple[str, bool]:
|
||||
"""执行后登记结果做重复检测,并按累计情况在结果尾部注入软提示。
|
||||
|
||||
指纹用截断后、未加提示的原始结果算(保证同输出哈希一致);返回
|
||||
(可能追加了提示的结果, 本次是否有净产出)。
|
||||
"""
|
||||
unproductive, productive = self._repeat_guard.record(name, args, result)
|
||||
if unproductive >= _RepeatGuard.SOFT:
|
||||
if unproductive == _RepeatGuard.SOFT:
|
||||
|
|
@ -1130,29 +789,24 @@ class AgentLoop:
|
|||
f"\n\n[撞墙警告] 你换着参数调用 {name} 已连续 {cnt} 次撞同一个错误「{esig}」。"
|
||||
"光微调参数没用——先 read/grep 看清目标的确切内容再动手,或换工具/换思路。"
|
||||
)
|
||||
|
||||
# 平台层产物机检(0.35.1 复发后落地):本步 shell/run_python 新产出的 .pptx
|
||||
# 若命中「整页贴图」伪导出特征,把 ERROR 注入 tool 结果逼模型当场返工。
|
||||
# 官方管线内的门只在模型用了官方脚本时生效,绕开管线的产物只能在这拦。
|
||||
# 注入在 repeat_guard.record 之后 —— 不进指纹,免得干扰重复检测。
|
||||
if name in _PPTX_GUARD_TOOLS:
|
||||
try:
|
||||
guard_msg = pptx_guard.scan_and_report(self.working_dir, tool_started_at)
|
||||
except Exception:
|
||||
guard_msg = None # 机检自身故障绝不拖垮工具链路
|
||||
if guard_msg:
|
||||
result += "\n\n" + guard_msg
|
||||
self._emit({
|
||||
"type": "warn",
|
||||
"msg": "产物机检:检测到「整页贴图」式 .pptx,已注入 ERROR 要求返工",
|
||||
})
|
||||
|
||||
preview = result if len(result) < 400 else result[:400] + "..."
|
||||
self._emit({
|
||||
"type": "tool_result",
|
||||
"name": name,
|
||||
"result": result,
|
||||
"preview": preview,
|
||||
"truncated": truncated,
|
||||
})
|
||||
return result, productive
|
||||
|
||||
def _maybe_pptx_guard(self, name: str, tool_started_at: float, result: str) -> str:
|
||||
"""平台层产物机检(0.35.1 复发后落地):本步 shell/run_python 新产出的 .pptx
|
||||
若命中「整页贴图」伪导出特征,把 ERROR 注入 tool 结果逼模型当场返工。
|
||||
官方管线内的门只在模型用了官方脚本时生效,绕开管线的产物只能在这拦。
|
||||
注入在 repeat_guard.record 之后 —— 不进指纹,免得干扰重复检测。
|
||||
"""
|
||||
if name not in _PPTX_GUARD_TOOLS:
|
||||
return result
|
||||
try:
|
||||
guard_msg = pptx_guard.scan_and_report(self.working_dir, tool_started_at)
|
||||
except Exception:
|
||||
guard_msg = None # 机检自身故障绝不拖垮工具链路
|
||||
if guard_msg:
|
||||
result += "\n\n" + guard_msg
|
||||
self._emit({
|
||||
"type": "warn",
|
||||
"msg": "产物机检:检测到「整页贴图」式 .pptx,已注入 ERROR 要求返工",
|
||||
})
|
||||
return result
|
||||
|
|
|
|||
|
|
@ -116,7 +116,7 @@ def scan_tool_failures(
|
|||
{"cutoff": cutoff},
|
||||
).fetchall()
|
||||
# 第二段:被丢弃的畸形 tool_call 参数(kind=tool_malformed)。这类失败整轮
|
||||
# 不入 messages(防投毒),loop._log_malformed_args 落在 usage_events,
|
||||
# 不入 messages(防投毒),llm_transport.log_malformed_args 落在 usage_events,
|
||||
# 是它们进面板/巡检邮件的唯一路径。
|
||||
mrows = s.execute(
|
||||
text(
|
||||
|
|
|
|||
|
|
@ -7,7 +7,8 @@ from types import SimpleNamespace
|
|||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
|
||||
|
||||
from core.loop import AgentLoop, _is_empty_response # noqa: E402
|
||||
from core.llm_transport import is_empty_response as _is_empty_response # noqa: E402
|
||||
from core.loop import AgentLoop # noqa: E402
|
||||
|
||||
|
||||
def _resp_empty(content=None, finish_reason=None):
|
||||
|
|
|
|||
|
|
@ -9,7 +9,8 @@ from types import SimpleNamespace
|
|||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
|
||||
|
||||
from core.loop import AgentLoop, _malformed_tool_calls # noqa: E402
|
||||
from core.llm_transport import malformed_tool_calls as _malformed_tool_calls # noqa: E402
|
||||
from core.loop import AgentLoop # noqa: E402
|
||||
|
||||
|
||||
def _resp(arguments: str):
|
||||
|
|
|
|||
|
|
@ -7,7 +7,8 @@ from types import SimpleNamespace
|
|||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
|
||||
|
||||
from core.loop import _RepeatGuard, _toolcalls_partial_args # noqa: E402
|
||||
from core.llm_transport import toolcalls_partial_args as _toolcalls_partial_args # noqa: E402
|
||||
from core.loop import _RepeatGuard # noqa: E402
|
||||
|
||||
|
||||
def _simulate(guard: _RepeatGuard, name: str, args, results: list[str]) -> list[str]:
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
from decimal import Decimal
|
||||
import unittest
|
||||
|
||||
from core.loop import _extract_usage_details
|
||||
from core.llm_transport import extract_usage_details as _extract_usage_details
|
||||
from core.storage.usage import _fallback_chat_cost_cny
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue