diff --git a/docker-compose.yml b/docker-compose.yml index bd7bd4e..446cfaf 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -40,7 +40,7 @@ services: # 支援兩種模式: # 1. 本地構建: 不設定 MOMO_IMAGE 環境變數 (使用 build 區塊) # 2. Registry 拉取: MOMO_IMAGE=registry.wooo.work/wooo/momo-pro-system - image: ${MOMO_IMAGE:-registry.wooo.work/wooo/momo-pro-system}:${VERSION:-latest} + image: ${MOMO_IMAGE:-registry.wooo.work/wooo/momo-pro-system}:${VERSION:-stable} container_name: momo-pro-system restart: unless-stopped labels: @@ -206,7 +206,7 @@ services: build: context: . dockerfile: Dockerfile - image: ${MOMO_IMAGE:-registry.wooo.work/wooo/momo-pro-system}:${VERSION:-latest} + image: ${MOMO_IMAGE:-registry.wooo.work/wooo/momo-pro-system}:${VERSION:-stable} container_name: momo-scheduler restart: unless-stopped labels: @@ -234,6 +234,12 @@ services: env_file: - .env command: ["python", "run_scheduler.py"] + healthcheck: + test: ["CMD", "python3", "-c", "import sys; sys.exit(0)"] + interval: 60s + timeout: 10s + retries: 3 + start_period: 30s depends_on: - momo-app - postgres @@ -253,7 +259,7 @@ services: build: context: . dockerfile: Dockerfile - image: ${MOMO_IMAGE:-registry.wooo.work/wooo/momo-pro-system}:${VERSION:-latest} + image: ${MOMO_IMAGE:-registry.wooo.work/wooo/momo-pro-system}:${VERSION:-stable} container_name: momo-telegram-bot restart: unless-stopped labels: @@ -278,6 +284,12 @@ services: env_file: - .env command: ["python", "run_telegram_bot.py"] + healthcheck: + test: ["CMD", "python3", "-c", "import sys; sys.exit(0)"] + interval: 60s + timeout: 10s + retries: 3 + start_period: 30s depends_on: - postgres networks: diff --git a/migrations/014_code_fix_playbook.sql b/migrations/020_code_fix_playbook.sql similarity index 100% rename from migrations/014_code_fix_playbook.sql rename to migrations/020_code_fix_playbook.sql diff --git a/telegram_ai_integration.py b/telegram_ai_integration.py deleted file mode 100644 index f88d940..0000000 --- a/telegram_ai_integration.py +++ /dev/null @@ -1,322 +0,0 @@ -#!/usr/bin/env python3 -""" -Telegram Bot AI Integration -Integrate existing AI Orchestrator for natural language processing -All responses in Traditional Chinese -""" - -import asyncio -import logging -from typing import Dict, Any, Optional -from services.ai_orchestrator import AIOrchestrator -from services import openclaw_strategist_service -from datetime import datetime - -logger = logging.getLogger(__name__) - -class TelegramAIIntegration: - """Telegram Bot AI Integration for natural language understanding""" - - def __init__(self): - self.orchestrator = AIOrchestrator() - - async def process_natural_language_query(self, user_message: str, user_id: int, chat_id: int) -> Dict[str, Any]: - """ - Process natural language query using existing AI infrastructure - - Args: - user_message: User's message in Traditional Chinese - user_id: Telegram user ID - chat_id: Telegram chat ID - - Returns: - Response dictionary with Traditional Chinese content - """ - try: - # Create session ID based on user and chat - session_id = f"tg_{user_id}_{chat_id}" - - # Prepare event for AI processing - event = { - "type": "telegram_query", - "source": "telegram_bot", - "timestamp": datetime.now().isoformat(), - "user_id": user_id, - "chat_id": chat_id, - "message": user_message, - "language": "zh-TW", # Traditional Chinese - "context": "telegram_group_chat" - } - - # L1: Semantic understanding (Hermes) - l1_result = await self.orchestrator.handle_l1(event, session_id) - if not l1_result or l1_result.get("metadata", {}).get("source") != "hermes_llm": - logger.warning( - f"[TelegramAIIntegration] Hermes LLM 未回應，走規則引擎降級" - f"（session={session_id} source={( l1_result or {}).get('metadata', {}).get('source', 'none')}）" - ) - - # Check if this is a complex query requiring L2 processing - if self._is_complex_query(user_message, l1_result): - # L2: Planning and execution (Nemotron) - l2_result = await self.orchestrator.handle_l2(event, session_id) - - dispatch_to = (l2_result or {}).get("dispatch_to", "direct_response") - complexity = float((l1_result or {}).get("complexity_score", 0.0) or 0.0) - - # 若 L2 判定走 OpenClaw 或複雜度 >= 0.7 → 呼叫策略師補真實繁中洞察 - if dispatch_to == "openclaw" or complexity >= 0.7: - strategist_text = "" - try: - # 同步呼叫 Gemini 可能耗時數秒，丟到 executor 避免阻塞 event loop - loop = asyncio.get_running_loop() - strategist_text = await loop.run_in_executor( - None, - openclaw_strategist_service.generate_strategy_response, - user_message, - { - "intent": (l1_result or {}).get("intent"), - "user_id": user_id, - "chat_id": chat_id, - }, - ) - except Exception as e: - logger.error( - f"[TelegramAIIntegration] OpenClaw 策略師呼叫失敗" - f"（{type(e).__name__}: {e}）", - exc_info=True, - ) - strategist_text = "" - - response = self._format_complex_response(l1_result, l2_result, user_message) - if strategist_text: - response["response_text"] = strategist_text - response["strategist_used"] = True - return response - - return self._format_complex_response(l1_result, l2_result, user_message) - else: - # Simple query, handle directly - return self._format_simple_response(l1_result, user_message) - - except Exception as e: - logger.error(f"[TelegramAIIntegration] Error processing query: {e}", exc_info=True) - return self._format_error_response(user_message) - - def _is_complex_query(self, message: str, l1_result: Dict[str, Any]) -> bool: - """Determine if query requires complex processing""" - complex_indicators = [ - "momo", " momo", "momo ", - "2026", "2025", "2024", # Date ranges - "brand", "brands", "brand:", "brands:", # Brand queries - "category", "categories", "category:", # Category queries - "report", "analysis", "ppt", "presentation", # Report generation - "compare", "comparison", "vs", "versus" # Comparison queries - ] - - message_lower = message.lower() - - # Check for complex indicators - for indicator in complex_indicators: - if indicator in message_lower: - return True - - # Check L1 analysis result - if l1_result.get("complexity_score", 0) > 0.7: - return True - - if l1_result.get("requires_data_fetch", False): - return True - - return False - - def _format_simple_response(self, l1_result: Dict[str, Any], original_message: str) -> Dict[str, Any]: - """Format response for simple queries""" - intent = l1_result.get("intent", "unknown") - confidence = l1_result.get("confidence", 0.0) - - # 繁體中文（台灣）回應模板 - responses = { - "greeting": { - "zh_tw": "您好！我是 MOMO Pro 智能助理，今天需要什麼協助？", - "suggestions": ["查看今日業績", "商品排行榜", "市場情報摘要"], - }, - "help": { - "zh_tw": ( - "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" - "請直接輸入問題，或使用選單選擇功能。" - ), - "suggestions": ["業績表現", "商品趨勢", "市場分析"], - }, - "unknown": { - "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。", - "suggestions": ["顯示主選單", "查看業績數據", "商品分析"], - }, - } - - # 若 L1 已帶 preliminary_answer（Hermes 生成或規則引擎），優先採用 - preliminary = (l1_result or {}).get("preliminary_answer", "") or "" - response_data = responses.get(intent, responses["unknown"]) - if preliminary: - response_data = {**response_data, "zh_tw": preliminary} - - return { - "success": True, - "type": "simple_response", - "intent": intent, - "confidence": confidence, - "response_text": response_data["zh_tw"], - "suggestions": response_data["suggestions"], - "show_menu": intent == "unknown" - } - - def _format_complex_response(self, l1_result: Dict[str, Any], l2_result: Dict[str, Any], original_message: str) -> Dict[str, Any]: - """Format response for complex queries requiring data fetching""" - action_plan = l2_result.get("action_plan", {}) - - # Extract relevant information - query_type = self._extract_query_type(original_message) - date_range = self._extract_date_range(original_message) - brands = self._extract_brands(original_message) - - # 繁體中文（台灣）回應 - query_type_zh = { - "sales analysis": "業績分析", - "product analysis": "商品分析", - "market intelligence": "市場情報", - "report generation": "報告產製", - "comparative analysis": "比較分析", - "general query": "一般查詢", - }.get(query_type, query_type) - - response_text = f"正在處理您的「{query_type_zh}」需求" - if date_range: - response_text += f"，期間：{date_range}" - if brands: - response_text += f"，品牌：{', '.join(brands)}" - response_text += "。分析準備中，稍候片刻…" - - return { - "success": True, - "type": "complex_response", - "query_type": query_type, - "date_range": date_range, - "brands": brands, - "action_plan": action_plan, - "response_text": response_text, - "requires_processing": True, - "processing_status": "queued" - } - - def _format_error_response(self, original_message: str) -> Dict[str, Any]: - """Format error response in Traditional Chinese""" - return { - "success": False, - "type": "error_response", - "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。", - "error_suggestions": [ - "查看今日業績", - "商品排行榜", - "市場情報摘要", - "使用 /help 查看可用指令", - ], - "show_menu": True, - } - - def _extract_query_type(self, message: str) -> str: - """Extract type of query from message""" - if any(word in message.lower() for word in ["sales", "revenue", "performance"]): - return "sales analysis" - elif any(word in message.lower() for word in ["product", "brand", "item"]): - return "product analysis" - elif any(word in message.lower() for word in ["market", "trend", "intelligence"]): - return "market intelligence" - elif any(word in message.lower() for word in ["report", "ppt", "presentation"]): - return "report generation" - elif any(word in message.lower() for word in ["compare", "comparison", "vs"]): - return "comparative analysis" - else: - return "general query" - - def _extract_date_range(self, message: str) -> Optional[str]: - """Extract date range from message""" - import re - date_pattern = r'(\d{4}[./-]\d{2}[./-]\d{2})\s*[-~]\s*(\d{4}[./-]\d{2}[./-]\d{2})' - match = re.search(date_pattern, message) - - if match: - start = match.group(1).replace('/', '-').replace('.', '-') - end = match.group(2).replace('/', '-').replace('.', '-') - return f"{start} 至 {end}" - - return None - - def _extract_brands(self, message: str) -> list: - """Extract brand names from message (Chinese and English)""" - # Brand mapping: Chinese name -> English name - brand_mapping = { - # Chinese -> English mapping - "nivea": "Nivea", - "loreal": "Loreal", - "sk-ii": "SK-II", - "kiehls": "Kiehls", - "clinique": "Clinique", - "dior": "Dior", - "chanel": "Chanel", - "ysl": "YSL", - "givenchy": "Givenchy", - "hermes": "Hermes", - "gucci": "Gucci", - "prada": "Prada", - "versace": "Versace", - "armani": "Armani", - "coach": "Coach", - "michael kors": "Michael Kors", - # Specific Chinese brand names from user query - "neutrogena": "Neutrogena", # English name used in Chinese - "aveeno": "Aveeno", # English name used in Chinese - "estee lauder": "Estee Lauder", - "lancome": "Lancome", - "biotherm": "Biotherm", - "clarins": "Clarins", - "nars": "NARS", - "bobbi brown": "Bobbi Brown", - "mac": "MAC", - "tumi": "Tumi", - "samsonite": "Samsonite", - "longchamp": "Longchamp", - "shiseido": "Shiseido" - } - - # Also include direct Chinese variations - chinese_variations = { - "nivea": "Nivea", - "loreal": "Loreal", - "sk-ii": "SK-II", - "kiehls": "Kiehls", - "clinique": "Clinique", - "dior": "Dior", - "chanel": "Chanel", - "neutrogena": "Neutrogena", - "aveeno": "Aveeno" - } - - # Combine all brand mappings - all_brands = {**brand_mapping, **chinese_variations} - - message_lower = message.lower() - found_brands = [] - - for brand_key, brand_name in all_brands.items(): - if brand_key in message_lower: - if brand_name not in found_brands: - found_brands.append(brand_name) - - return found_brands - -# Global instance for use in telegram bot service -telegram_ai_integration = TelegramAIIntegration() - -async def process_telegram_query(user_message: str, user_id: int, chat_id: int) -> Dict[str, Any]: - """Convenience function for processing telegram queries""" - return await telegram_ai_integration.process_natural_language_query(user_message, user_id, chat_id)