feat: 优化 FAQ 处理和系统稳定性
- 添加本地 FAQ 库快速路径（问候语等社交响应） - 修复 Chatwoot 重启循环问题（PID 文件清理） - 添加 LLM 响应缓存（Redis 缓存，提升性能） - 添加智能推理模式（根据查询复杂度自动启用） - 添加订单卡片消息功能（Chatwoot 富媒体） - 增加 LLM 超时时间至 60 秒 Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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wangliang
121
agent/utils/faq_library.py
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121
agent/utils/faq_library.py
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"""
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Local FAQ Library for instant responses
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Common questions can be answered immediately without API calls
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"""
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import re
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from typing import Optional, Dict
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from .logger import get_logger
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logger = get_logger(__name__)
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class FAQLibrary:
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"""Local FAQ library for instant common question responses"""
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def __init__(self):
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"""Initialize FAQ library with ONLY common greetings and social responses
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Note: Business-related FAQs (register, order, payment, shipment, return, etc.)
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should be handled by Strapi MCP to ensure accuracy and consistency.
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This library only contains instant social responses for better UX.
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"""
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self.faqs = {
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# ========== 问候类 Greetings ==========
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"你好": "你好！我是您的B2B客户服务助手，很高兴为您服务。我可以帮您处理订单查询、产品咨询、售后问题等。请问有什么可以帮到您的吗？",
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"您好": "您好！我是您的B2B客户服务助手，很高兴为您服务。我可以帮您处理订单查询、产品咨询、售后问题等。请问有什么可以帮到您的吗？",
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"hi": "Hello! I'm your B2B customer service assistant. How can I help you today?",
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"hello": "Hello! I'm here to assist you. How can I help you today?",
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"hey": "Hey there! How can I help you today?",
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# ========== 感谢类 Gratitude ==========
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"谢谢": "不客气！如果还有其他问题，随时可以问我。祝您购物愉快！",
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"感谢": "感谢您的支持！如有任何问题，随时联系我们。",
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"thank you": "You're welcome! If you have any other questions, feel free to ask. Have a great day!",
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"thanks": "You're welcome! Let me know if you need anything else.",
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# ========== 再见类 Farewell ==========
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"再见": "再见！如有需要，随时联系。祝您生活愉快！",
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"bye": "Goodbye! Feel free to reach out anytime. Have a great day!",
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"goodbye": "Goodbye! Have a wonderful day!",
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# ========== 社交礼貌类 Social Politeness ==========
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"早上好": "早上好！很高兴为您服务。请问有什么可以帮到您的吗？",
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"下午好": "下午好！很高兴为您服务。请问有什么可以帮到您的吗？",
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"晚上好": "晚上好！很高兴为您服务。请问有什么可以帮到您的吗？",
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"good morning": "Good morning! How can I assist you today?",
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"good afternoon": "Good afternoon! How can I assist you today?",
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"good evening": "Good evening! How can I assist you today?",
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}
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# Compile regex patterns for fuzzy matching
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self._compile_patterns()
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def _compile_patterns(self):
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"""Compile regex patterns for fuzzy FAQ matching"""
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self.patterns = []
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for keyword, response in self.faqs.items():
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# Case-insensitive pattern with word boundaries
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pattern = re.compile(re.escape(keyword), re.IGNORECASE)
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self.patterns.append((pattern, response))
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def find_match(self, query: str) -> Optional[str]:
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"""Find matching FAQ response
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Args:
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query: User query text
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Returns:
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Matching FAQ response or None if no match found
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"""
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# Remove HTML tags and extra whitespace
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clean_query = re.sub(r'<[^>]+>', '', query)
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clean_query = ' '.join(clean_query.split())
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# Try exact match first
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if clean_query.lower() in (k.lower() for k in self.faqs.keys()):
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for key, response in self.faqs.items():
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if key.lower() == clean_query.lower():
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logger.info("FAQ exact match", key=key, query=clean_query[:50])
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return response
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# Try fuzzy match (contains keyword)
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for pattern, response in self.patterns:
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if pattern.search(clean_query):
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logger.info("FAQ fuzzy match", pattern=pattern.pattern, query=clean_query[:50])
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return response
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# No match found
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logger.debug("No FAQ match found", query=clean_query[:50])
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return None
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def add_faq(self, keyword: str, response: str) -> None:
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"""Add or update FAQ entry
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Args:
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keyword: Question keyword
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response: Answer text
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"""
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self.faqs[keyword] = response
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pattern = re.compile(re.escape(keyword), re.IGNORECASE)
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self.patterns.append((pattern, response))
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logger.info("FAQ added", keyword=keyword)
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def get_all_keywords(self) -> list[str]:
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"""Get all FAQ keywords
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Returns:
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List of FAQ keywords
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"""
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return list(self.faqs.keys())
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# Global FAQ library instance
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faq_library: Optional[FAQLibrary] = None
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def get_faq_library() -> FAQLibrary:
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"""Get or create global FAQ library instance"""
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global faq_library
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if faq_library is None:
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faq_library = FAQLibrary()
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return faq_library
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194
agent/utils/response_cache.py
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194
agent/utils/response_cache.py
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"""
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LLM Response Cache for FAQ and common queries
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"""
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import hashlib
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import json
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from typing import Any, Optional
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from datetime import timedelta
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from .cache import CacheManager
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from .logger import get_logger
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logger = get_logger(__name__)
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class ResponseCache:
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"""Cache LLM responses for common queries"""
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def __init__(
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self,
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cache_manager: Optional[CacheManager] = None,
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default_ttl: int = 3600 # 1 hour default
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):
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"""Initialize response cache
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Args:
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cache_manager: Cache manager instance
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default_ttl: Default TTL in seconds for cached responses
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"""
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self.cache = cache_manager
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self.default_ttl = default_ttl
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def _generate_key(
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self,
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model: str,
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messages: list[dict[str, str]],
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temperature: float = 0.7,
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**kwargs: Any
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) -> str:
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"""Generate cache key from request parameters
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Args:
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model: Model name
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messages: List of messages
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temperature: Temperature parameter
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**kwargs: Additional parameters
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Returns:
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Cache key string
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"""
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# Create a normalized representation of the request
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cache_input = {
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"model": model,
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"messages": messages,
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"temperature": temperature,
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**{k: v for k, v in kwargs.items() if v is not None}
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}
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# Hash the input to create a short, unique key
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cache_str = json.dumps(cache_input, sort_keys=True, ensure_ascii=False)
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cache_hash = hashlib.sha256(cache_str.encode()).hexdigest()[:16]
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return f"llm_response:{model}:{cache_hash}"
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async def get(
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self,
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model: str,
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messages: list[dict[str, str]],
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temperature: float = 0.7,
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**kwargs: Any
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) -> Optional[str]:
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"""Get cached response if available
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Args:
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model: Model name
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messages: List of messages
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temperature: Temperature parameter
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**kwargs: Additional parameters
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Returns:
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Cached response content or None
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"""
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if not self.cache:
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return None
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key = self._generate_key(model, messages, temperature, **kwargs)
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cached = await self.cache.get(key)
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if cached:
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logger.info(
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"Cache hit",
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model=model,
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key=key,
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response_length=len(cached)
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)
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try:
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data = json.loads(cached)
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return data.get("response")
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except json.JSONDecodeError:
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logger.warning("Invalid cached data", key=key)
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return None
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logger.debug("Cache miss", model=model, key=key)
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return None
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async def set(
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self,
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model: str,
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messages: list[dict[str, str]],
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response: str,
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temperature: float = 0.7,
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ttl: Optional[int] = None,
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**kwargs: Any
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) -> None:
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"""Cache LLM response
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Args:
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model: Model name
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messages: List of messages
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response: Response content to cache
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temperature: Temperature parameter
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ttl: Time-to-live in seconds
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**kwargs: Additional parameters
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"""
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if not self.cache:
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return
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key = self._generate_key(model, messages, temperature, **kwargs)
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ttl = ttl or self.default_ttl
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# Store response with metadata
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data = {
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"response": response,
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"model": model,
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"response_length": len(response),
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"temperature": temperature
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}
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await self.cache.set(
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key,
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json.dumps(data, ensure_ascii=False),
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ttl=ttl
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)
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logger.info(
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"Response cached",
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model=model,
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key=key,
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response_length=len(response),
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ttl=ttl
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)
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async def invalidate(self, pattern: str = "llm_response:*") -> int:
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"""Invalidate cached responses matching pattern
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Args:
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pattern: Redis key pattern to match
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Returns:
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Number of keys deleted
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"""
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if not self.cache:
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return 0
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# This would need scan/delete operation
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# For now, just log
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logger.info("Cache invalidation requested", pattern=pattern)
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return 0
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def get_cache_stats(self) -> dict[str, Any]:
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"""Get cache statistics
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Returns:
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Dictionary with cache stats
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"""
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return {
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"enabled": self.cache is not None,
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"default_ttl": self.default_ttl
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}
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# Global response cache instance
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response_cache: Optional[ResponseCache] = None
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def get_response_cache() -> ResponseCache:
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"""Get or create global response cache instance"""
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global response_cache
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if response_cache is None:
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from .cache import get_cache_manager
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response_cache = ResponseCache(
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cache_manager=get_cache_manager(),
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default_ttl=3600 # 1 hour
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)
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return response_cache
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