135 lines
5.1 KiB
Python
135 lines
5.1 KiB
Python
"""定层 glm.pro52 空响应(失败面板 kind=empty_response 主角,11/15/近7天)。
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背景:assistant 轮既无 tool_calls 又无正文,被 run loop 当正常收尾静默 done。
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与 opus48 的「tool_use 漏成正文」(narrated,见 diag_narrated_toolcall_2a1bc25d)
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是不同现象——要判 glm 到底是①真吐空(tc=0 且 content=0)、②漏成正文(narrated)、
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还是③正常(能回结构化 tool_calls,线上那次只是瞬态)。
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做法:build_agent(resume=True) 复原真实 system prompt + 全套工具 schema + **完整多轮
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上下文**(空响应发生在深层,单条 user 消息复现不了 —— 这是与 2a1bc25d 探针的关键差别),
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直接打 glm.pro52,stream / non-stream 各打一发。只读 DB,不跑 loop 不写库。
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输出 ASCII(Windows GBK 安全),完整正文写文件。
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用法(生产机跑,本机 import litellm 卡 ~20min):
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.venv/Scripts/python.exe scripts/diag_glm_empty_probe.py [task_id] [user_id] [model_profile]
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默认 = 35744bea(近7天 empty 5 次、最可复现)。model_profile 缺省用 task 自身档(glm.pro52),
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传第三参可换档做对照(如 deepseek_v4.pro 看是否 glm 独有)。
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"""
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import os
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import sys
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from pathlib import Path
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from uuid import UUID
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ROOT = Path(__file__).resolve().parent.parent
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env = ROOT / ".env"
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if env.exists():
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for line in env.read_text(encoding="utf-8").splitlines():
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line = line.strip()
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if line and not line.startswith("#") and "=" in line:
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k, v = line.split("=", 1)
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os.environ.setdefault(k.strip(), v.strip().strip('"').strip("'"))
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os.environ.setdefault("LITELLM_LOCAL_MODEL_COST_MAP", "True")
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sys.path.insert(0, str(ROOT))
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import litellm # noqa: E402
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from core.agent_builder import build_agent # noqa: E402
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# 默认锁定 35744bea(近7天 empty 5 次、最可复现);可 argv 覆盖。
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TASK_ID = sys.argv[1] if len(sys.argv) > 1 else "35744bea-49c0-41c8-86be-8d0c5c05bd32"
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USER_ID = UUID(sys.argv[2]) if len(sys.argv) > 2 else UUID("3cc07a22-f667-47f3-9617-e7d8a1a60cf0")
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MODEL_OVERRIDE = sys.argv[3] if len(sys.argv) > 3 else None
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_tag = (MODEL_OVERRIDE or "self").replace(".", "_").replace("/", "_")
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OUT = open(ROOT / "scripts" / f"_glm_empty_probe_{TASK_ID[:8]}_{_tag}.txt", "w", encoding="utf-8")
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def w(*a):
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print(*a, file=OUT)
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agent, session, sid, tstate, wd = build_agent(
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user_id=USER_ID, session_id=TASK_ID, resume=True,
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model_name=MODEL_OVERRIDE,
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)
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schemas = agent.executor.schemas()
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caps = agent.caps
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llm = agent.llm
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# 复原 loop 下一轮真会送的完整消息(system + 全部历史)—— 空响应就发生在这个上下文上。
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messages = list(session.messages)
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sys_txt = next((m["content"] for m in messages if m.get("role") == "system"), "")
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roles = [m.get("role") for m in messages]
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w("[SETUP]")
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w(f" task={TASK_ID} model_id={caps.model_id} profile={caps.family}.{caps.variant}")
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w(f" api_base={llm.api_base} temp={caps.optimal_temperature} "
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f"parallel_tools={caps.parallel_tools}")
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w(f" system_prompt chars={len(sys_txt)} tools={len(schemas)}")
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w(f" messages count={len(messages)} last_role={roles[-1] if roles else None}")
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w(f" role tail={roles[-6:]}")
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w("")
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kwargs_base = dict(
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model=caps.model_id,
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api_base=llm.api_base,
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api_key=llm.api_key,
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messages=messages,
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tools=schemas,
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temperature=caps.optimal_temperature,
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timeout=300,
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)
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if caps.parallel_tools:
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kwargs_base["parallel_tool_calls"] = True
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stdout_lines = []
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def probe(tag, stream):
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w(f"========== {tag} ==========")
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try:
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if stream:
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chunks = list(litellm.completion(
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**kwargs_base, stream=True,
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stream_options={"include_usage": True}))
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resp = litellm.stream_chunk_builder(chunks)
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else:
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resp = litellm.completion(**kwargs_base)
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except Exception as e:
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w(f"[FAIL] {type(e).__name__}: {str(e)[:300]}")
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stdout_lines.append(f"{tag}: FAIL {type(e).__name__}")
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return
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msg = resp.choices[0].message
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tc = msg.tool_calls or []
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content = msg.content or ""
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fr = resp.choices[0].finish_reason
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u = resp.usage
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w(f" finish_reason={fr}")
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w(f" usage prompt={u.prompt_tokens} completion={u.completion_tokens}")
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w(f" tool_calls count={len(tc)}")
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for i, t in enumerate(tc):
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w(f" [{i}] {t.function.name}({(t.function.arguments or '')[:200]})")
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w(f" content chars={len(content)}")
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w(" content:")
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w(content[:2000])
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w("")
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# 三态判定:EMPTY(真吐空)/ NARRATED(工具意图漏成正文)/ STRUCTURED / plain-text
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narrated = (not tc) and ("`" in content) and ("\n- " in content or "No result received" in content)
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if tc:
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verdict = "STRUCTURED tool_calls"
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elif not content.strip():
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verdict = "EMPTY (tc=0 content=0) <-- 复现空响应"
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elif narrated:
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verdict = "NARRATED-as-text"
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else:
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verdict = "plain-text-no-tools"
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stdout_lines.append(f"{tag}: {verdict} (finish={fr}, tc={len(tc)}, content={len(content)}c)")
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probe("NON-STREAM", stream=False)
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probe("STREAM", stream=True)
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OUT.close()
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for ln in stdout_lines:
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print(ln)
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print(f"full -> scripts/_glm_empty_probe_{TASK_ID[:8]}_{_tag}.txt")
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