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- telegram_ai_integration.py 移至 services/ 解決 ModuleNotFoundError （momo-telegram-bot 只掛載 services/，根目錄檔案進不了容器） - import 路徑更新為 from services.telegram_ai_integration - 所有英文回覆字串改為繁體中文： · 歡迎訊息、fallback 訊息、錯誤提示 · _enhanced_keyword_matching 全英文段落 · _handle_complex_ai_response / _handle_simple_ai_response · Cancel 按鈕改「❌ 取消」、callback 改 menu:main Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
283 lines
12 KiB
Python
283 lines
12 KiB
Python
#!/usr/bin/env python3
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"""
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Telegram Bot AI Integration
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Integrate existing AI Orchestrator for natural language processing
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All responses in Traditional Chinese
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"""
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import asyncio
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import logging
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from typing import Dict, Any, Optional
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from services.ai_orchestrator import AIOrchestrator
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from services import openclaw_strategist_service
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from datetime import datetime
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logger = logging.getLogger(__name__)
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class TelegramAIIntegration:
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"""Telegram Bot AI Integration for natural language understanding"""
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def __init__(self):
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self.orchestrator = AIOrchestrator()
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async def process_natural_language_query(self, user_message: str, user_id: int, chat_id: int) -> Dict[str, Any]:
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"""
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Process natural language query using existing AI infrastructure
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Args:
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user_message: User's message in Traditional Chinese
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user_id: Telegram user ID
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chat_id: Telegram chat ID
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Returns:
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Response dictionary with Traditional Chinese content
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"""
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try:
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# Create session ID based on user and chat
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session_id = f"tg_{user_id}_{chat_id}"
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# Prepare event for AI processing
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event = {
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"type": "telegram_query",
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"source": "telegram_bot",
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"timestamp": datetime.now().isoformat(),
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"user_id": user_id,
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"chat_id": chat_id,
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"message": user_message,
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"language": "zh-TW", # Traditional Chinese
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"context": "telegram_group_chat"
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}
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# L1: Semantic understanding (Hermes)
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l1_result = await self.orchestrator.handle_l1(event, session_id)
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if not l1_result or l1_result.get("metadata", {}).get("source") != "hermes_llm":
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logger.warning(
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f"[TelegramAIIntegration] Hermes LLM 未回應，走規則引擎降級"
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f"（session={session_id} source={( l1_result or {}).get('metadata', {}).get('source', 'none')}）"
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)
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# Check if this is a complex query requiring L2 processing
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if self._is_complex_query(user_message, l1_result):
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# L2: Planning and execution (Nemotron)
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l2_result = await self.orchestrator.handle_l2(event, session_id)
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dispatch_to = (l2_result or {}).get("dispatch_to", "direct_response")
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complexity = float((l1_result or {}).get("complexity_score", 0.0) or 0.0)
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# 若 L2 判定走 OpenClaw 或複雜度 >= 0.7 → 呼叫策略師補真實繁中洞察
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if dispatch_to == "openclaw" or complexity >= 0.7:
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strategist_text = ""
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try:
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# 同步呼叫 Gemini 可能耗時數秒，丟到 executor 避免阻塞 event loop
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loop = asyncio.get_running_loop()
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strategist_text = await loop.run_in_executor(
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None,
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openclaw_strategist_service.generate_strategy_response,
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user_message,
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{
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"intent": (l1_result or {}).get("intent"),
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"user_id": user_id,
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"chat_id": chat_id,
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},
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)
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except Exception as e:
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logger.error(
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f"[TelegramAIIntegration] OpenClaw 策略師呼叫失敗"
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f"（{type(e).__name__}: {e}）",
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exc_info=True,
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)
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strategist_text = ""
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response = self._format_complex_response(l1_result, l2_result, user_message)
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if strategist_text:
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response["response_text"] = strategist_text
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response["strategist_used"] = True
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return response
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return self._format_complex_response(l1_result, l2_result, user_message)
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else:
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# Simple query, handle directly
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return self._format_simple_response(l1_result, user_message)
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except Exception as e:
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logger.error(f"[TelegramAIIntegration] Error processing query: {e}", exc_info=True)
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return self._format_error_response(user_message)
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def _is_complex_query(self, message: str, l1_result: Dict[str, Any]) -> bool:
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"""Determine if query requires complex processing"""
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complex_indicators = [
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"momo", " momo", "momo ",
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"2026", "2025", "2024", # Date ranges
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"brand", "brands", "brand:", "brands:", # Brand queries
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"category", "categories", "category:", # Category queries
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"report", "analysis", "ppt", "presentation", # Report generation
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"compare", "comparison", "vs", "versus" # Comparison queries
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]
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message_lower = message.lower()
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# Check for complex indicators
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for indicator in complex_indicators:
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if indicator in message_lower:
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return True
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# Check L1 analysis result
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if l1_result.get("complexity_score", 0) > 0.7:
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return True
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if l1_result.get("requires_data_fetch", False):
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return True
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return False
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def _format_simple_response(self, l1_result: Dict[str, Any], original_message: str) -> Dict[str, Any]:
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"""Format response for simple queries"""
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intent = l1_result.get("intent", "unknown")
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confidence = l1_result.get("confidence", 0.0)
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# 繁體中文（台灣）回應模板
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responses = {
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"greeting": {
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"zh_tw": "您好！我是 MOMO Pro 智能助理，今天需要什麼協助？",
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"suggestions": ["查看今日業績", "商品排行榜", "市場情報摘要"],
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},
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"help": {
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"zh_tw": (
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"我可以協助您處理業績查詢、商品資訊、市場情報、產出報告等需求。\n"
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"請直接輸入問題，或使用選單選擇功能。"
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),
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"suggestions": ["業績表現", "商品趨勢", "市場分析"],
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},
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"unknown": {
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"zh_tw": "收到您的訊息，若需特定功能，請從選單選擇或使用 /help 查看可用指令。",
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"suggestions": ["顯示主選單", "查看業績數據", "商品分析"],
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},
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}
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# 若 L1 已帶 preliminary_answer（Hermes 生成或規則引擎），優先採用
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preliminary = (l1_result or {}).get("preliminary_answer", "") or ""
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response_data = responses.get(intent, responses["unknown"])
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if preliminary:
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response_data = {**response_data, "zh_tw": preliminary}
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return {
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"success": True,
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"type": "simple_response",
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"intent": intent,
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"confidence": confidence,
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"response_text": response_data["zh_tw"],
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"suggestions": response_data["suggestions"],
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"show_menu": intent == "unknown"
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}
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def _format_complex_response(self, l1_result: Dict[str, Any], l2_result: Dict[str, Any], original_message: str) -> Dict[str, Any]:
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"""Format response for complex queries requiring data fetching"""
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action_plan = l2_result.get("action_plan", {})
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# Extract relevant information
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query_type = self._extract_query_type(original_message)
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date_range = self._extract_date_range(original_message)
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brands = self._extract_brands(original_message)
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# 繁體中文（台灣）回應
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query_type_zh = {
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"sales analysis": "業績分析",
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"product analysis": "商品分析",
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"market intelligence": "市場情報",
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"report generation": "報告產製",
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"comparative analysis": "比較分析",
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"general query": "一般查詢",
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}.get(query_type, query_type)
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response_text = f"正在處理您的「{query_type_zh}」需求"
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if date_range:
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response_text += f"，期間：{date_range}"
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if brands:
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response_text += f"，品牌：{', '.join(brands)}"
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response_text += "。分析準備中，稍候片刻…"
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return {
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"success": True,
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"type": "complex_response",
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"query_type": query_type,
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"date_range": date_range,
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"brands": brands,
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"action_plan": action_plan,
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"response_text": response_text,
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"requires_processing": True,
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"processing_status": "queued"
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}
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def _format_error_response(self, original_message: str) -> Dict[str, Any]:
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"""Format error response in Traditional Chinese"""
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return {
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"success": False,
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"type": "error_response",
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"response_text": "抱歉，處理您的訊息時發生問題，請改用選單功能或稍後再試。",
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"error_suggestions": [
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"查看今日業績",
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"商品排行榜",
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"市場情報摘要",
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"使用 /help 查看可用指令",
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],
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"show_menu": True,
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}
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def _extract_query_type(self, message: str) -> str:
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"""Extract type of query from message"""
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if any(word in message.lower() for word in ["sales", "revenue", "performance"]):
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return "sales analysis"
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elif any(word in message.lower() for word in ["product", "brand", "item"]):
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return "product analysis"
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elif any(word in message.lower() for word in ["market", "trend", "intelligence"]):
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return "market intelligence"
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elif any(word in message.lower() for word in ["report", "ppt", "presentation"]):
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return "report generation"
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elif any(word in message.lower() for word in ["compare", "comparison", "vs"]):
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return "comparative analysis"
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else:
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return "general query"
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def _extract_date_range(self, message: str) -> Optional[str]:
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"""Extract date range from message"""
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import re
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date_pattern = r'(\d{4}[./-]\d{2}[./-]\d{2})\s*[-~]\s*(\d{4}[./-]\d{2}[./-]\d{2})'
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match = re.search(date_pattern, message)
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if match:
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start = match.group(1).replace('/', '-').replace('.', '-')
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end = match.group(2).replace('/', '-').replace('.', '-')
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return f"{start} 至 {end}"
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return None
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def _extract_brands(self, message: str) -> list:
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"""Extract brand names from message (Chinese and English)"""
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brand_mapping = {
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"nivea": "Nivea", "loreal": "Loreal", "sk-ii": "SK-II",
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"kiehls": "Kiehls", "clinique": "Clinique", "dior": "Dior",
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"chanel": "Chanel", "ysl": "YSL", "givenchy": "Givenchy",
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"gucci": "Gucci", "prada": "Prada", "versace": "Versace",
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"armani": "Armani", "coach": "Coach", "michael kors": "Michael Kors",
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"neutrogena": "Neutrogena", "aveeno": "Aveeno",
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"estee lauder": "Estee Lauder", "lancome": "Lancome",
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"biotherm": "Biotherm", "clarins": "Clarins", "nars": "NARS",
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"bobbi brown": "Bobbi Brown", "mac": "MAC", "tumi": "Tumi",
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"samsonite": "Samsonite", "longchamp": "Longchamp", "shiseido": "Shiseido"
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}
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message_lower = message.lower()
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found_brands = []
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for brand_key, brand_name in brand_mapping.items():
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if brand_key in message_lower and brand_name not in found_brands:
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found_brands.append(brand_name)
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return found_brands
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# Global instance for use in telegram bot service
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telegram_ai_integration = TelegramAIIntegration()
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async def process_telegram_query(user_message: str, user_id: int, chat_id: int) -> Dict[str, Any]:
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"""Convenience function for processing telegram queries"""
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return await telegram_ai_integration.process_natural_language_query(user_message, user_id, chat_id)
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