"""复现 task 2a1bc25d 的「工具意图漏成正文」现象。

用 build_agent(resume=True) 复原真实 system prompt + 全套工具 schema(只读 DB,不跑
loop 不写库),再拿原始用户消息 + 真实 tools 直接打 opus48,看网关回不回结构化
tool_calls。stream / non-stream 两条路径各打一发。输出 ASCII(Windows GBK 安全),
完整正文写文件。
"""
import os
import sys
from pathlib import Path
from uuid import UUID

ROOT = Path(__file__).resolve().parent.parent
env = ROOT / ".env"
if env.exists():
    for line in env.read_text(encoding="utf-8").splitlines():
        line = line.strip()
        if line and not line.startswith("#") and "=" in line:
            k, v = line.split("=", 1)
            os.environ.setdefault(k.strip(), v.strip().strip('"').strip("'"))
os.environ.setdefault("LITELLM_LOCAL_MODEL_COST_MAP", "True")

sys.path.insert(0, str(ROOT))
import litellm  # noqa: E402
from core.agent_builder import build_agent  # noqa: E402

_tag = (sys.argv[1] if len(sys.argv) > 1 else "default").replace(".", "_").replace("/", "_")
OUT = open(ROOT / "scripts" / f"_narrated_probe_{_tag}.txt", "w", encoding="utf-8")


def w(*a):
    print(*a, file=OUT)


USER_ID = UUID("5c8af5a4-48cb-4515-bf88-822816e383c9")
TASK_ID = "2a1bc25d-d045-4ecd-acc9-b3ee2c8ccf09"
USER_MSG = "基于中国建材总院对接景德镇市汇报材料 制作ppt"

# 可传档位覆盖(同 task 的 system prompt + 31 工具,只换模型层),默认用 task 自身档。
MODEL_OVERRIDE = sys.argv[1] if len(sys.argv) > 1 else None

agent, session, sid, tstate, wd = build_agent(
    user_id=USER_ID, session_id=TASK_ID, resume=True,
    model_name=MODEL_OVERRIDE,
)
schemas = agent.executor.schemas()
caps = agent.caps
llm = agent.llm

sys_msg = next((m for m in session.messages if m.get("role") == "system"), None)
sys_txt = sys_msg["content"] if sys_msg else ""

w("[SETUP]")
w(f"  model_id={caps.model_id} profile={caps.family}.{caps.variant}")
w(f"  api_base={llm.api_base} temp={caps.optimal_temperature} "
  f"parallel_tools={caps.parallel_tools}")
w(f"  system_prompt chars={len(sys_txt)}")
w(f"  tools count={len(schemas)}")
w(f"  tool names={[s['function']['name'] for s in schemas]}")
w("")

messages = [
    {"role": "system", "content": sys_txt},
    {"role": "user", "content": USER_MSG},
]

kwargs_base = dict(
    model=caps.model_id,
    api_base=llm.api_base,
    api_key=llm.api_key,
    messages=messages,
    tools=schemas,
    temperature=caps.optimal_temperature,
    timeout=300,
)
if caps.parallel_tools:
    kwargs_base["parallel_tool_calls"] = True

stdout_lines = []


def probe(tag, stream):
    w(f"========== {tag} ==========")
    try:
        if stream:
            chunks = list(litellm.completion(
                **kwargs_base, stream=True,
                stream_options={"include_usage": True}))
            resp = litellm.stream_chunk_builder(chunks)
        else:
            resp = litellm.completion(**kwargs_base)
    except Exception as e:
        w(f"[FAIL] {type(e).__name__}: {str(e)[:300]}")
        stdout_lines.append(f"{tag}: FAIL {type(e).__name__}")
        return
    msg = resp.choices[0].message
    tc = msg.tool_calls or []
    content = msg.content or ""
    u = resp.usage
    w(f"  usage prompt={u.prompt_tokens} completion={u.completion_tokens}")
    w(f"  tool_calls count={len(tc)}")
    for i, t in enumerate(tc):
        w(f"    [{i}] {t.function.name}({(t.function.arguments or '')[:200]})")
    w(f"  content chars={len(content)}")
    w("  content:")
    w(content[:2000])
    w("")
    # narration 特征:无结构化 tool_calls 但正文里出现反引号工具名 bullet 参数
    narrated = (not tc) and ("`" in content) and ("\n- " in content or "No result received" in content)
    verdict = ("STRUCTURED tool_calls" if tc
               else "NARRATED-as-text" if narrated
               else "plain-text-no-tools")
    stdout_lines.append(f"{tag}: {verdict} (tc={len(tc)}, content={len(content)}c)")


probe("NON-STREAM", stream=False)
probe("STREAM", stream=True)

OUT.close()
for ln in stdout_lines:
    print(ln)
print("full -> scripts/_narrated_probe.txt")