280 lines
12 KiB
Python
280 lines
12 KiB
Python
"""
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AWOOOI Chat Manager - 雙 AI 對話核心
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======================================
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Phase 21.5 初版: 2026-03-31 ogt
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Phase 22.6 重寫: 2026-04-03 ogt (老闆需求: 雙 AI 互動對話)
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Phase 22.7 更新: 2026-04-03 ogt (老闆指示: OpenClaw→Gemini, NemoClaw→Ollama llama3.2:3b)
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Phase 22.8 更新: 2026-04-09 ogt (老闆指示: NemoClaw→Ollama 111 deepseek-r1:14b，SRE 推理更強)
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Phase 33 更新: 2026-05-05 ogt (ADR-110: OpenClaw chat 改走 GCP-A Ollama interactive lane)
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架構:
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- OpenClaw (Ollama GCP-A interactive lane): SRE 首席顧問，精準分析
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- NemoClaw (Ollama interactive lane deepseek-r1:14b): 戰術參謀，深度推理
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費用控管:
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- OpenClaw/NemoClaw chat 預設免費 Ollama；Gemini 不再作為 ChatManager 預設路徑
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- 每次回覆顯示 token 用量
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"""
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import asyncio
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import re
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import httpx
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import structlog
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from src.core.config import get_settings
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from src.repositories.incident_repository import get_incident_repository
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from src.repositories.k8s_repository import get_k8s_repository
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from src.services.ollama_endpoint_resolver import resolve_ollama_order
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from src.utils.timezone import now_taipei
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logger = structlog.get_logger(__name__)
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OPENCLAW_PERSONA = """你是 OpenClaw，AWOOOI 平台的 SRE AI 首席顧問。
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個性: 精準、果斷、專業，像老將一樣直接給出建議。
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語氣: 簡短有力，不廢話。繁體中文回應。不超過 300 字。
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稱呼用戶為「老闆」。
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"""
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NEMOCLAW_PERSONA = """你是 NemoClaw，AWOOOI 平台的 AI 戰術參謀。
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個性: 分析型、從不同角度思考，會質疑假設。
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語氣: 帶點挑釁但建設性。不超過 200 字。
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稱呼用戶為「老闆」。評論 OpenClaw 的回應時，直接說「我補充」或「我有不同看法」。
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強制規則：
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1. 全程使用繁體中文，禁止使用簡體中文、英文或其他語言。
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2. 禁止自稱 DeepSeek 或透露底層模型資訊。你的名字就是 NemoClaw。
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3. 專注於 SRE/DevOps/Kubernetes/可觀測性領域。
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"""
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class ChatManager:
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"""AWOOOI 雙 AI 對話管理器"""
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def __init__(self):
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pass # 2026-04-03 ogt: 移除 repo 實例化，leWOOOgo 規範禁止 Service 持有 repository
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async def get_system_context(self) -> str:
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"""收集系統即時上下文"""
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now = now_taipei()
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k8s = get_k8s_repository()
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incidents = get_incident_repository()
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try:
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k8s_status = await k8s.get_pod_status_summary(namespace="awoooi-prod")
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cluster_info = f"Cluster: {k8s_status['running']}/{k8s_status['total']} Pods Running"
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if k8s_status.get('problem_pods'):
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cluster_info += f", {len(k8s_status['problem_pods'])} 異常"
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except Exception:
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cluster_info = "Cluster: 無法取得狀態"
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try:
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active_incidents = await incidents.get_active()
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if active_incidents:
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lines = [f"- {inc.incident_id}: {inc.status.value} (SEV {inc.severity.value})"
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for inc in active_incidents[:3]]
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incident_summary = "\n".join(lines)
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else:
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incident_summary = "無活躍告警"
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except Exception:
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incident_summary = "無法取得告警"
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return (
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f"## 系統狀態 ({now.strftime('%Y-%m-%d %H:%M')} 台北)\n"
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f"- {cluster_info}\n"
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f"- 活躍告警: {incident_summary}\n"
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)
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async def _call_openclaw(self, system_prompt: str, user_message: str) -> str | None:
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"""
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呼叫 OpenClaw 對話 — Ollama interactive lane
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2026-04-10 Claude Code: 強制合併 OPENCLAW_PERSONA，確保字數限制與格式規範
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2026-05-05 Codex: 改走 ADR-110 GCP-A/GCP-B/111 Ollama topology，避免個人聊天直打 Gemini
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"""
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# 強制在 system_prompt 前置 persona，確保 LLM 遵守字數與格式
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system_prompt = f"{OPENCLAW_PERSONA}\n{system_prompt}"
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settings = get_settings()
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model = settings.OPENCLAW_DEFAULT_MODEL
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async with httpx.AsyncClient(timeout=40.0) as client:
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for endpoint in resolve_ollama_order("interactive"):
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if not endpoint.url:
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continue
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try:
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resp = await client.post(
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f"{endpoint.url}/api/chat",
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json={
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"model": model,
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"stream": False,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_message},
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],
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"options": {"num_predict": 900, "temperature": 0.2},
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},
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)
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resp.raise_for_status()
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data = resp.json()
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raw = data.get("message", {}).get("content", "").strip()
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text = re.sub(r"<think>.*?</think>", "", raw, flags=re.DOTALL).strip() or raw
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eval_count = data.get("eval_count", 0)
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prompt_eval_count = data.get("prompt_eval_count", 0)
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total_tokens = eval_count + prompt_eval_count
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logger.info(
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"openclaw_ollama_chat_usage",
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model=model,
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endpoint=endpoint.url,
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provider=endpoint.provider_name,
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prompt_tokens=prompt_eval_count,
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output_tokens=eval_count,
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)
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return f"{text}\n\n<i>🦙 {model} | {total_tokens} tokens | 免費</i>"
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except Exception as e:
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logger.warning(
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"openclaw_chat_endpoint_failed",
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provider=endpoint.provider_name,
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endpoint=endpoint.url,
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error=str(e),
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)
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logger.warning("openclaw_chat_failed_all_endpoints", model=model)
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return None
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async def _call_nemotron(self, system_prompt: str, user_message: str) -> str | None:
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"""
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呼叫 NemoClaw 對話 — Ollama 111 deepseek-r1:14b
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2026-04-09 ogt: 改接 192.168.0.111 Ollama deepseek-r1:14b，SRE 推理能力最強
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deepseek-r1 含 <think> 標籤，需過濾後才回傳
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2026-04-10 Claude Code: 強制合併 NEMOCLAW_PERSONA，確保字數限制與格式規範
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"""
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# 強制在 system_prompt 前置 persona
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system_prompt = f"{NEMOCLAW_PERSONA}\n{system_prompt}"
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# 2026-05-05 Codex: ADR-110 interactive lane，由 resolver 管理 GCP-A/GCP-B/111 拓撲
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MODEL = "deepseek-r1:14b"
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async with httpx.AsyncClient(timeout=120.0) as client:
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for endpoint in resolve_ollama_order("interactive"):
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if not endpoint.url:
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continue
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try:
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resp = await client.post(
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f"{endpoint.url}/api/chat",
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json={
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"model": MODEL,
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"stream": False,
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# Ollama 0.24 separates deepseek-r1 thinking from final text.
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# Chat callers expect message.content to contain the answer.
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"think": False,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_message},
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],
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"options": {"num_predict": 1200},
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},
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)
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resp.raise_for_status()
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data = resp.json()
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raw = data.get("message", {}).get("content", "").strip()
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# 過濾 deepseek-r1 的 <think>...</think> 推理區塊
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text = re.sub(r"<think>.*?</think>", "", raw, flags=re.DOTALL).strip()
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if not text:
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text = raw # 萬一全是 think block，直接回傳原文
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eval_count = data.get("eval_count", 0)
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prompt_eval_count = data.get("prompt_eval_count", 0)
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total_tokens = eval_count + prompt_eval_count
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logger.info(
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"nemotron_ollama_usage",
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model=MODEL,
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provider=endpoint.provider_name,
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prompt_tokens=prompt_eval_count,
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output_tokens=eval_count,
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)
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return f"{text}\n\n<i>🦙 {MODEL} | {total_tokens} tokens | 免費</i>"
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except Exception as e:
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logger.warning(
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"nemotron_chat_endpoint_failed",
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model=MODEL,
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provider=endpoint.provider_name,
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endpoint=endpoint.url,
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error=str(e),
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)
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logger.warning("nemotron_chat_failed_all_endpoints", model=MODEL)
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return None
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async def generate_response(
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self,
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user_id: int, # noqa: ARG002
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username: str, # noqa: ARG002
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message_text: str,
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) -> str:
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"""
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根據訊息決定回應模式:
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@openclaw <msg> → 只有 OpenClaw 回應
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@nemo <msg> → 只有 NemoClaw 回應
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其他 → OpenClaw 先回，NemoClaw 異步補充
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"""
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context = await self.get_system_context()
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text = message_text.strip()
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# 模式 1: 指定 OpenClaw
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if text.lower().startswith("@openclaw"):
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msg = text[9:].strip() or text
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result = await self._call_openclaw(f"{OPENCLAW_PERSONA}\n{context}", msg)
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return f"🦞 <b>OpenClaw:</b>\n{result or '🔴 OpenClaw 無響應'}"
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# 模式 2: 指定 NemoClaw
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if text.lower().startswith("@nemo"):
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msg = text[5:].strip() or text
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result = await self._call_nemotron(f"{NEMOCLAW_PERSONA}\n{context}", msg)
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return f"🤖 <b>NemoClaw:</b>\n{result or '🔴 NemoClaw 無響應 (NIM 超時)'}"
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# 模式 3: 雙 AI — OpenClaw 先答，NemoClaw 並行
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openclaw_task = asyncio.create_task(
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self._call_openclaw(f"{OPENCLAW_PERSONA}\n{context}", text)
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)
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nemo_task = asyncio.create_task(
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self._call_nemotron(
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f"{NEMOCLAW_PERSONA}\n{context}",
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f"老闆問了: {text}\n\n請從 NemoClaw 角度補充或評論。",
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)
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)
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# OpenClaw 最多等 40s（含 context 取得時間），NemoClaw 最多等 60s
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# 2026-04-03 ogt: 移除 asyncio.shield — shield 會在超時後讓 task 繼續跑但無人等待，造成 silent leak
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try:
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openclaw_raw = await asyncio.wait_for(openclaw_task, timeout=40.0)
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except TimeoutError:
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openclaw_raw = None
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openclaw_block = f"🦞 <b>OpenClaw:</b>\n{openclaw_raw or '🔴 無響應'}"
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try:
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nemo_raw = await asyncio.wait_for(nemo_task, timeout=60.0)
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except TimeoutError:
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nemo_raw = None
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if nemo_raw:
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return f"{openclaw_block}\n\n🤖 <b>NemoClaw:</b>\n{nemo_raw}"
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return openclaw_block
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# Singleton
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_chat_manager: ChatManager | None = None
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def get_chat_manager() -> ChatManager:
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global _chat_manager
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if _chat_manager is None:
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_chat_manager = ChatManager()
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return _chat_manager
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