117 lines
4.5 KiB
Python
117 lines
4.5 KiB
Python
import json
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import logging
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from typing import Any, Dict, Optional
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from sqlalchemy import text as sql_text
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from database.manager import get_session
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from services.hermes_analyst_service import HermesAnalystService
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from services.nemoton_dispatcher_service import NemotronDispatcher
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sys_log = logging.getLogger(__name__)
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# SQLAlchemy text() 需從 sqlalchemy 導入，避免 F821
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def _make_text(sql: str):
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return sql_text(sql)
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class AIOrchestrator:
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"""
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協調流程：
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1) 從 session_id 載入 agent_context
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2) 依 event 類型決定 L1 或 L2
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3) 合併上下文與 event 後調用對應 Agent
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4) 寫回更新後的上下文
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"""
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def __init__(self):
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self.hermes = HermesAnalystService()
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self.nemotron = NemotronDispatcher()
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async def handle_l1(self, event: Dict[str, Any], session_id: str) -> Dict[str, Any]:
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"""L1：Hermes 分析（負責翻譯與建議）"""
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ctx = await self._load_context(session_id, "hermes")
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enriched = self._merge_context(event, ctx)
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result = await self.hermes._batch_analyze([enriched], pchome_prices={})
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if result and result[0]:
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out = result[0]
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analysis = {
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"summary": out.get("risk", "UNKNOWN"),
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"probable_cause": out.get("recommended_action", ""),
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"actions": [out.get("recommended_action", "")],
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}
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else:
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analysis = {"summary": "資訊不足", "probable_cause": "", "actions": ["請人工確認"]}
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await self._save_context(session_id, "hermes", analysis)
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return analysis
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async def handle_l2(self, event: Dict[str, Any], session_id: str) -> Dict[str, Any]:
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"""L2：NemoTron 規劃 + 審核閘"""
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ctx = await self._load_context(session_id, "nemotron")
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enriched = self._merge_context(event, ctx)
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plan = await self.nemotron.dispatch([enriched], hermes_stats=None)
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analysis = {
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"plan": {
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"type": "price_adjust",
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"sku": enriched.get("payload", {}).get("sku", ""),
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"actions_taken": plan.get("dispatched", 0),
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"summary": f"已提交 {plan.get('dispatched', 0)} 筄審核建議",
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},
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"actions_taken": [],
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}
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await self._save_context(session_id, "nemotron", analysis)
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return analysis
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# ── 內部工具 ────────────────────────────────────────────────
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async def _load_context(self, session_id: str, agent: str) -> Dict[str, Any]:
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session = get_session()
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try:
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sql = _make_text("""
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SELECT context_val FROM agent_context
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WHERE session_id = :sid AND agent_name = :ag
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ORDER BY created_at DESC LIMIT 1
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""")
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row = session.execute(sql, {"sid": session_id, "ag": agent}).fetchone()
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if row:
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return json.loads(row[0]) if row[0] else {}
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return {}
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except Exception as e:
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sys_log.warning(f"[Orchestrator] 載入 context 失敗: {e}")
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return {}
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finally:
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session.close()
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async def _save_context(self, session_id: str, agent: str, data: Dict[str, Any]) -> None:
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session = get_session()
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try:
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session.execute(
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_make_text("""
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INSERT INTO agent_context
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(session_id, agent_name, context_key, context_val, created_at, ttl_minutes)
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VALUES
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(:sid, :ag, :ck, :cv, NOW(), :ttl)
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ON CONFLICT (session_id, agent_name, context_key)
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DO UPDATE SET context_val = :cv, updated_at = NOW()
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"""),
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{
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"sid": session_id,
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"ag": agent,
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"ck": "latest",
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"cv": json.dumps(data, ensure_ascii=False),
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"ttl": 1440, # 24h
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},
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)
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session.commit()
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except Exception as e:
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session.rollback()
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sys_log.warning(f"[Orchestrator] 寫入 context 失敗: {e}")
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finally:
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session.close()
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def _merge_context(self, event: Dict[str, Any], ctx: Dict[str, Any]) -> Dict[str, Any]:
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"""簡單合併：event 優先，ctx 作為額外資訊"""
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merged = dict(event)
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if ctx:
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merged["_ctx"] = ctx
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return merged
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