629 lines
27 KiB
Python
629 lines
27 KiB
Python
"""
|
||
AI回复引擎模块 - 统一意图识别与回复生成
|
||
|
||
【重构版本】
|
||
- 将意图判断和回复生成合并为一次AI调用
|
||
- AI根据完整上下文自行判断意图并生成回复
|
||
- 避免关键词误判导致的不当回复
|
||
- 支持多种API类型：OpenAI / OpenAI Responses / Gemini / Anthropic / Azure OpenAI / Ollama / DashScope
|
||
"""
|
||
|
||
import json
|
||
import time
|
||
import requests
|
||
import threading
|
||
from typing import List, Dict, Optional
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from loguru import logger
|
||
from db_manager import db_manager
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||
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||
|
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class AIReplyEngine:
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"""AI回复引擎 - 统一意图识别与回复生成"""
|
||
|
||
def __init__(self):
|
||
self._init_default_prompts()
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||
# 用于控制同一chat_id消息的串行处理
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||
self._chat_locks = {}
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self._chat_locks_lock = threading.Lock()
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||
|
||
def _init_default_prompts(self):
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"""初始化默认提示词（用于构建统一提示词）"""
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||
self.default_prompts = {
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||
'price': '''【议价场景】
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策略：根据议价次数递减优惠
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||
- 第1次：可小幅优惠，表达诚意
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- 第2次：中等优惠，强调已是优惠价
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- 第3次及以后：最大优惠或坚持底线
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语气友好但坚定，突出商品价值和优势。''',
|
||
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||
'tech': '''【技术/产品问题】
|
||
基于商品信息回答，不要自行发挥。
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如果问题超出商品信息范围，回复："等等，这个我需要看一看"''',
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||
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'default': '''【一般咨询】
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基于商品信息回答物流、售后等问题。
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||
如果问题超出商品信息范围，回复："等等，这个我需要看一看"
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如果客户明确询问退款，回复："虚拟产品，一旦发出是不可以退款的"'''
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}
|
||
|
||
def _resolve_api_type(self, settings: dict) -> str:
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||
"""根据设置解析实际的API类型（支持显式设置和自动检测）"""
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||
api_type = (settings.get('api_type') or '').strip()
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||
if api_type:
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||
return api_type
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||
|
||
# 向后兼容：自动检测
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if self._is_dashscope_app_api(settings):
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return 'dashscope'
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||
if self._is_gemini_api(settings):
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||
return 'gemini'
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||
return 'openai'
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||
|
||
def _is_dashscope_app_api(self, settings: dict) -> bool:
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||
"""判断是否为DashScope应用API（/apps/模式）"""
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||
base_url = (settings.get('base_url') or '').strip().rstrip('/')
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return 'dashscope.aliyuncs.com' in base_url and '/apps/' in base_url
|
||
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||
def _is_gemini_api(self, settings: dict) -> bool:
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"""判断是否为Gemini API"""
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||
model_name = settings.get('model_name', '').lower()
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return 'gemini' in model_name
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||
|
||
def _build_unified_system_prompt(self, custom_prompts: dict, settings: dict) -> str:
|
||
"""
|
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构建统一的系统提示词
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||
将意图判断和回复生成整合到一个提示词中
|
||
"""
|
||
# 获取各场景的指导（优先使用用户自定义）
|
||
price_guide = custom_prompts.get('price', self.default_prompts['price'])
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||
tech_guide = custom_prompts.get('tech', self.default_prompts['tech'])
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||
default_guide = custom_prompts.get('default', self.default_prompts['default'])
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||
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||
# 获取议价设置
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||
max_bargain_rounds = settings.get('max_bargain_rounds', 3)
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||
max_discount_percent = settings.get('max_discount_percent', 10)
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||
max_discount_amount = settings.get('max_discount_amount', 100)
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||
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unified_prompt = f"""你是一位专业的电商客服AI助手。请根据用户消息和上下文，直接生成合适的回复。
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||
|
||
## 核心原则
|
||
1. **准确理解意图**：只根据用户实际说的内容判断，不要过度解读
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2. **不要主动提及敏感话题**：用户没提到的（如退款、砍价）不要主动提
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||
3. **基于商品信息回答**：只回答商品信息中有的内容
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||
4. **避免重复**：结合对话历史，不要重复之前说过的话
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5. **语言简洁友好**：回复要自然、简短，尽量别超过20个字
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||
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## 场景处理指南
|
||
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### 当用户明确要求降价/优惠/砍价时
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{price_guide}
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||
- 议价限制：最多{max_bargain_rounds}轮，最大优惠{max_discount_percent}%或{max_discount_amount}元
|
||
|
||
### 当用户询问产品技术/功能/使用问题时
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{tech_guide}
|
||
|
||
### 其他一般咨询（物流、售后、商品介绍等）
|
||
{default_guide}
|
||
|
||
## 特别注意
|
||
- 用户只是问价格≠用户在砍价，正常回答价格即可
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- 用户咨询售后≠用户要退款，正常解答即可
|
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- 如果用户的问题超过你的回答范围，比如发图片，可以说"等等，这个问题我需要看看"，不要自己回答
|
||
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请直接输出回复内容，不要输出分析过程。"""
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||
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return unified_prompt
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||
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def _call_dashscope_api(self, settings: dict, messages: list, max_tokens: int = 100, temperature: float = 0.7) -> str:
|
||
"""调用DashScope API"""
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base_url = settings['base_url']
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||
if '/apps/' in base_url:
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||
app_id = base_url.split('/apps/')[-1].split('/')[0]
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||
else:
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||
raise ValueError("DashScope API URL中未找到app_id")
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||
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||
url = f"https://dashscope.aliyuncs.com/api/v1/apps/{app_id}/completion"
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||
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||
system_content = ""
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||
user_content = ""
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for msg in messages:
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||
if msg['role'] == 'system':
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||
system_content = msg['content']
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||
elif msg['role'] == 'user':
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user_content = msg['content'] # 假设 user prompt 已在 generate_reply 中构建好
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||
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if system_content and user_content:
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prompt = f"{system_content}\n\n用户问题：{user_content}\n\n请直接回答用户的问题："
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||
elif user_content:
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prompt = user_content
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else:
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||
prompt = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
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||
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||
data = {
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||
"input": {"prompt": prompt},
|
||
"parameters": {"max_tokens": max_tokens, "temperature": temperature},
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||
"debug": {}
|
||
}
|
||
headers = {
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||
"Authorization": f"Bearer {settings['api_key']}",
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||
"Content-Type": "application/json"
|
||
}
|
||
|
||
logger.info(f"DashScope API请求: {url}")
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||
logger.info(f"发送的prompt: {prompt[:100]}...") # 避免 prompt 过长
|
||
logger.debug(f"请求数据: {json.dumps(data, ensure_ascii=False)}")
|
||
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||
response = requests.post(url, headers=headers, json=data, timeout=30)
|
||
|
||
if response.status_code != 200:
|
||
logger.error(f"DashScope API请求失败: {response.status_code} - {response.text}")
|
||
raise Exception(f"DashScope API请求失败: {response.status_code} - {response.text}")
|
||
|
||
result = response.json()
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||
logger.debug(f"DashScope API响应: {json.dumps(result, ensure_ascii=False)}")
|
||
|
||
if 'output' in result and 'text' in result['output']:
|
||
return result['output']['text'].strip()
|
||
else:
|
||
raise Exception(f"DashScope API响应格式错误: {result}")
|
||
|
||
def _call_gemini_api(self, settings: dict, messages: list, max_tokens: int = 100, temperature: float = 0.7) -> str:
|
||
"""
|
||
调用Google Gemini REST API (v1beta)
|
||
"""
|
||
api_key = settings['api_key']
|
||
model_name = settings['model_name']
|
||
|
||
url = f"https://generativelanguage.googleapis.com/v1beta/models/{model_name}:generateContent?key={api_key}"
|
||
|
||
headers = {"Content-Type": "application/json"}
|
||
|
||
# --- 转换消息格式 (修复 P1-3: 增强健壮性) ---
|
||
system_instruction = ""
|
||
user_content_parts = []
|
||
|
||
# 遍历消息，找到 system 和所有的 user parts
|
||
for msg in messages:
|
||
if msg['role'] == 'system':
|
||
system_instruction = msg['content']
|
||
elif msg['role'] == 'user':
|
||
# 我们只关心 user content
|
||
user_content_parts.append(msg['content'])
|
||
|
||
# 将所有 user parts 合并为最后的 user_content
|
||
# 在我们的使用场景中 (generate_reply)，只会有一个 user part，但这样更安全
|
||
user_content = "\n".join(user_content_parts)
|
||
|
||
if not user_content:
|
||
logger.warning(f"Gemini API 调用: 未在消息中找到 'user' 角色内容。Messages: {messages}")
|
||
raise ValueError("未在消息中找到用户内容 (user content)")
|
||
# --- 消息格式转换结束 ---
|
||
|
||
payload = {
|
||
"contents": [
|
||
{
|
||
"role": "user",
|
||
"parts": [{"text": user_content}]
|
||
}
|
||
],
|
||
"generationConfig": {
|
||
"temperature": temperature,
|
||
"maxOutputTokens": max_tokens
|
||
}
|
||
}
|
||
|
||
if system_instruction:
|
||
payload["systemInstruction"] = {
|
||
"parts": [{"text": system_instruction}]
|
||
}
|
||
|
||
logger.info(f"Calling Gemini REST API: {url.split('?')[0]}")
|
||
logger.debug(f"Gemini Payload: {json.dumps(payload, ensure_ascii=False)}")
|
||
|
||
response = requests.post(url, headers=headers, json=payload, timeout=30)
|
||
|
||
if response.status_code != 200:
|
||
logger.error(f"Gemini API 请求失败: {response.status_code} - {response.text}")
|
||
raise Exception(f"Gemini API 请求失败: {response.status_code} - {response.text}")
|
||
|
||
result = response.json()
|
||
logger.debug(f"Gemini API 响应: {json.dumps(result, ensure_ascii=False)}")
|
||
|
||
try:
|
||
reply_text = result['candidates'][0]['content']['parts'][0]['text']
|
||
return reply_text.strip()
|
||
except (KeyError, IndexError, TypeError) as e:
|
||
logger.error(f"Gemini API 响应格式错误: {result} - {e}")
|
||
raise Exception(f"Gemini API 响应格式错误: {result}")
|
||
|
||
def _call_openai_chat_api(self, settings: dict, messages: list, max_tokens: int = 100, temperature: float = 0.7) -> str:
|
||
"""调用OpenAI Chat Completions API（兼容OpenAI / Ollama / 其他兼容服务）"""
|
||
base_url = settings['base_url'].rstrip('/')
|
||
if not base_url.endswith('/v1'):
|
||
base_url = base_url + '/v1'
|
||
url = f"{base_url}/chat/completions"
|
||
|
||
headers = {"Content-Type": "application/json"}
|
||
api_key = settings.get('api_key', '')
|
||
if api_key:
|
||
headers["Authorization"] = f"Bearer {api_key}"
|
||
|
||
data = {
|
||
"model": settings['model_name'],
|
||
"messages": messages,
|
||
"max_tokens": max_tokens,
|
||
"temperature": temperature
|
||
}
|
||
|
||
logger.info(f"OpenAI Chat API请求: {url}")
|
||
response = requests.post(url, headers=headers, json=data, timeout=30)
|
||
|
||
if response.status_code != 200:
|
||
logger.error(f"OpenAI Chat API请求失败: {response.status_code} - {response.text}")
|
||
raise Exception(f"OpenAI Chat API请求失败: {response.status_code} - {response.text}")
|
||
|
||
result = response.json()
|
||
return result['choices'][0]['message']['content'].strip()
|
||
|
||
def _call_openai_responses_api(self, settings: dict, messages: list, max_tokens: int = 100, temperature: float = 0.7) -> str:
|
||
"""调用OpenAI Responses API"""
|
||
base_url = settings['base_url'].rstrip('/')
|
||
if not base_url.endswith('/v1'):
|
||
base_url = base_url + '/v1'
|
||
url = f"{base_url}/responses"
|
||
|
||
headers = {
|
||
"Authorization": f"Bearer {settings['api_key']}",
|
||
"Content-Type": "application/json"
|
||
}
|
||
data = {
|
||
"model": settings['model_name'],
|
||
"input": messages,
|
||
"max_output_tokens": max_tokens,
|
||
"temperature": temperature
|
||
}
|
||
|
||
logger.info(f"OpenAI Responses API请求: {url}")
|
||
response = requests.post(url, headers=headers, json=data, timeout=30)
|
||
|
||
if response.status_code != 200:
|
||
logger.error(f"OpenAI Responses API请求失败: {response.status_code} - {response.text}")
|
||
raise Exception(f"OpenAI Responses API请求失败: {response.status_code} - {response.text}")
|
||
|
||
result = response.json()
|
||
# Responses API 返回 output_text 字段
|
||
if 'output_text' in result:
|
||
return result['output_text'].strip()
|
||
# 兼容解析 output 数组
|
||
for item in result.get('output', []):
|
||
if item.get('type') == 'message':
|
||
for content in item.get('content', []):
|
||
if content.get('type') == 'output_text':
|
||
return content['text'].strip()
|
||
raise Exception(f"OpenAI Responses API响应格式错误: {result}")
|
||
|
||
def _call_anthropic_api(self, settings: dict, messages: list, max_tokens: int = 100, temperature: float = 0.7) -> str:
|
||
"""调用Anthropic Claude Messages API"""
|
||
base_url = settings['base_url'].rstrip('/')
|
||
if base_url.endswith('/v1'):
|
||
url = f"{base_url}/messages"
|
||
else:
|
||
url = f"{base_url}/v1/messages"
|
||
|
||
headers = {
|
||
"x-api-key": settings['api_key'],
|
||
"anthropic-version": "2023-06-01",
|
||
"Content-Type": "application/json"
|
||
}
|
||
|
||
# Anthropic 格式：system 单独提取，messages 只包含 user/assistant
|
||
system_content = ""
|
||
api_messages = []
|
||
for msg in messages:
|
||
if msg['role'] == 'system':
|
||
system_content = msg['content']
|
||
else:
|
||
api_messages.append({"role": msg['role'], "content": msg['content']})
|
||
|
||
data = {
|
||
"model": settings['model_name'],
|
||
"max_tokens": max_tokens,
|
||
"temperature": temperature,
|
||
"messages": api_messages
|
||
}
|
||
if system_content:
|
||
data["system"] = system_content
|
||
|
||
logger.info(f"Anthropic API请求: {url}")
|
||
response = requests.post(url, headers=headers, json=data, timeout=30)
|
||
|
||
if response.status_code != 200:
|
||
logger.error(f"Anthropic API请求失败: {response.status_code} - {response.text}")
|
||
raise Exception(f"Anthropic API请求失败: {response.status_code} - {response.text}")
|
||
|
||
result = response.json()
|
||
return result['content'][0]['text'].strip()
|
||
|
||
def _call_azure_openai_api(self, settings: dict, messages: list, max_tokens: int = 100, temperature: float = 0.7) -> str:
|
||
"""调用Azure OpenAI API"""
|
||
base_url = settings['base_url'].rstrip('/')
|
||
# Azure URL 格式: https://{resource}.openai.azure.com/openai/deployments/{deployment}/chat/completions?api-version=xxx
|
||
# 用户应在 base_url 中填入完整的 deployment URL
|
||
if '/chat/completions' in base_url:
|
||
url = base_url
|
||
else:
|
||
url = f"{base_url}/chat/completions"
|
||
|
||
if 'api-version' not in url:
|
||
separator = '&' if '?' in url else '?'
|
||
url = f"{url}{separator}api-version=2024-02-01"
|
||
|
||
headers = {
|
||
"api-key": settings['api_key'],
|
||
"Content-Type": "application/json"
|
||
}
|
||
data = {
|
||
"messages": messages,
|
||
"max_tokens": max_tokens,
|
||
"temperature": temperature
|
||
}
|
||
|
||
logger.info(f"Azure OpenAI API请求: {url.split('?')[0]}")
|
||
response = requests.post(url, headers=headers, json=data, timeout=30)
|
||
|
||
if response.status_code != 200:
|
||
logger.error(f"Azure OpenAI API请求失败: {response.status_code} - {response.text}")
|
||
raise Exception(f"Azure OpenAI API请求失败: {response.status_code} - {response.text}")
|
||
|
||
result = response.json()
|
||
return result['choices'][0]['message']['content'].strip()
|
||
|
||
def is_ai_enabled(self, cookie_id: str) -> bool:
|
||
"""检查指定账号是否启用AI回复"""
|
||
settings = db_manager.get_ai_reply_settings(cookie_id)
|
||
return settings['ai_enabled']
|
||
|
||
def _get_chat_lock(self, chat_id: str) -> threading.Lock:
|
||
"""获取指定chat_id的锁，如果不存在则创建"""
|
||
with self._chat_locks_lock:
|
||
if chat_id not in self._chat_locks:
|
||
self._chat_locks[chat_id] = threading.Lock()
|
||
return self._chat_locks[chat_id]
|
||
|
||
def generate_reply(self, message: str, item_info: dict, chat_id: str,
|
||
cookie_id: str, user_id: str, item_id: str,
|
||
skip_wait: bool = False) -> Optional[str]:
|
||
"""
|
||
生成AI回复 - 统一意图识别与回复生成
|
||
AI会自动判断用户意图并生成合适的回复，避免关键词误判
|
||
"""
|
||
if not self.is_ai_enabled(cookie_id):
|
||
return None
|
||
|
||
try:
|
||
# 先保存用户消息到数据库（意图暂时设为None，后续可根据需要更新）
|
||
message_created_at = self.save_conversation(
|
||
chat_id, cookie_id, user_id, item_id, "user", message, intent=None
|
||
)
|
||
|
||
# 消息去抖处理
|
||
if not skip_wait:
|
||
logger.info(f"【{cookie_id}】消息已保存，等待10秒收集后续消息: {message[:20]}...")
|
||
time.sleep(10)
|
||
else:
|
||
logger.info(f"【{cookie_id}】消息已保存（外部防抖已启用）: {message[:20]}...")
|
||
|
||
# 获取该chat_id的锁，确保同一对话的消息串行处理
|
||
chat_lock = self._get_chat_lock(chat_id)
|
||
|
||
with chat_lock:
|
||
# 检查是否有更新的消息
|
||
query_seconds = 6 if skip_wait else 25
|
||
recent_messages = self._get_recent_user_messages(chat_id, cookie_id, seconds=query_seconds)
|
||
|
||
if recent_messages and len(recent_messages) > 0:
|
||
latest_message = recent_messages[-1]
|
||
if message_created_at != latest_message['created_at']:
|
||
logger.info(f"【{cookie_id}】检测到更新消息，跳过当前消息")
|
||
return None
|
||
|
||
# 1. 获取AI设置
|
||
settings = db_manager.get_ai_reply_settings(cookie_id)
|
||
custom_prompts = json.loads(settings['custom_prompts']) if settings['custom_prompts'] else {}
|
||
|
||
# 2. 获取对话历史
|
||
context = self.get_conversation_context(chat_id, cookie_id)
|
||
|
||
# 3. 获取对话轮数和议价设置（供AI参考）
|
||
conversation_rounds = self.get_conversation_rounds(chat_id, cookie_id)
|
||
max_bargain_rounds = settings.get('max_bargain_rounds', 3)
|
||
max_discount_percent = settings.get('max_discount_percent', 10)
|
||
max_discount_amount = settings.get('max_discount_amount', 100)
|
||
|
||
# 4. 构建统一的系统提示词（整合意图判断和回复生成）
|
||
system_prompt = self._build_unified_system_prompt(custom_prompts, settings)
|
||
|
||
# 5. 构建商品信息
|
||
item_desc = f"商品标题: {item_info.get('title', '未知')}\n"
|
||
item_desc += f"商品价格: {item_info.get('price', '未知')}元\n"
|
||
item_desc += f"商品描述: {item_info.get('desc', '无')}"
|
||
|
||
# 6. 构建对话历史字符串
|
||
context_str = ""
|
||
if context:
|
||
context_str = "\n".join([
|
||
f"{'客户' if msg['role'] == 'user' else '客服'}: {msg['content']}"
|
||
for msg in context[-10:]
|
||
])
|
||
|
||
# 7. 构建用户消息（包含所有上下文）
|
||
user_prompt = f"""## 商品信息
|
||
{item_desc}
|
||
|
||
## 对话历史
|
||
{context_str if context_str else '(新对话，暂无历史)'}
|
||
|
||
## 对话状态
|
||
- 当前对话轮数：第{conversation_rounds + 1}轮
|
||
- 议价限制：最多{max_bargain_rounds}轮议价后需坚持底价
|
||
- 最大可优惠：{max_discount_percent}%或{max_discount_amount}元
|
||
|
||
## 当前用户消息
|
||
{message}
|
||
|
||
请根据以上信息，直接回复用户："""
|
||
|
||
# 8. 构建消息列表
|
||
messages = [
|
||
{"role": "system", "content": system_prompt},
|
||
{"role": "user", "content": user_prompt}
|
||
]
|
||
|
||
# 9. 根据API类型调用对应的AI接口
|
||
reply = None
|
||
api_type = self._resolve_api_type(settings)
|
||
logger.info(f"使用 {api_type} API生成回复")
|
||
|
||
if api_type == 'dashscope':
|
||
# DashScope有两种模式：
|
||
# 1) /apps/{app_id} 应用模式 -> 走百炼应用API
|
||
# 2) compatible-mode/v1 兼容模式 -> 走OpenAI Chat Completions
|
||
if self._is_dashscope_app_api(settings):
|
||
reply = self._call_dashscope_api(settings, messages, max_tokens=150, temperature=0.7)
|
||
else:
|
||
logger.info("DashScope检测为兼容模式（非/apps/），改走OpenAI兼容Chat API")
|
||
reply = self._call_openai_chat_api(settings, messages, max_tokens=150, temperature=0.7)
|
||
elif api_type == 'gemini':
|
||
reply = self._call_gemini_api(settings, messages, max_tokens=150, temperature=0.7)
|
||
elif api_type == 'openai_responses':
|
||
reply = self._call_openai_responses_api(settings, messages, max_tokens=150, temperature=0.7)
|
||
elif api_type == 'anthropic':
|
||
reply = self._call_anthropic_api(settings, messages, max_tokens=150, temperature=0.7)
|
||
elif api_type == 'azure_openai':
|
||
reply = self._call_azure_openai_api(settings, messages, max_tokens=150, temperature=0.7)
|
||
else:
|
||
# openai / ollama / 空值 均走 chat/completions
|
||
reply = self._call_openai_chat_api(settings, messages, max_tokens=150, temperature=0.7)
|
||
|
||
# 10. 保存AI回复到对话记录
|
||
self.save_conversation(chat_id, cookie_id, user_id, item_id, "assistant", reply, intent=None)
|
||
|
||
logger.info(f"AI回复生成成功 (账号: {cookie_id}): {reply}")
|
||
return reply
|
||
|
||
except Exception as e:
|
||
logger.error(f"AI回复生成失败 {cookie_id}: {e}")
|
||
if hasattr(e, 'response') and hasattr(e.response, 'url'):
|
||
logger.error(f"请求URL: {e.response.url}")
|
||
if hasattr(e, 'request') and hasattr(e.request, 'url'):
|
||
logger.error(f"请求URL: {e.request.url}")
|
||
return None
|
||
|
||
async def generate_reply_async(self, message: str, item_info: dict, chat_id: str,
|
||
cookie_id: str, user_id: str, item_id: str,
|
||
skip_wait: bool = False) -> Optional[str]:
|
||
"""
|
||
异步包装器：在独立线程池中执行同步的 `generate_reply`，并返回结果。
|
||
这样可以在异步代码中直接 await，而不阻塞事件循环。
|
||
"""
|
||
try:
|
||
import asyncio as _asyncio
|
||
return await _asyncio.to_thread(self.generate_reply, message, item_info, chat_id, cookie_id, user_id, item_id, skip_wait)
|
||
except Exception as e:
|
||
logger.error(f"异步生成回复失败: {e}")
|
||
return None
|
||
|
||
def get_conversation_context(self, chat_id: str, cookie_id: str, limit: int = 20) -> List[Dict]:
|
||
"""获取对话上下文"""
|
||
try:
|
||
with db_manager.lock:
|
||
cursor = db_manager.conn.cursor()
|
||
cursor.execute('''
|
||
SELECT role, content FROM ai_conversations
|
||
WHERE chat_id = ? AND cookie_id = ?
|
||
ORDER BY created_at DESC LIMIT ?
|
||
''', (chat_id, cookie_id, limit))
|
||
|
||
results = cursor.fetchall()
|
||
context = [{"role": row[0], "content": row[1]} for row in reversed(results)]
|
||
return context
|
||
except Exception as e:
|
||
logger.error(f"获取对话上下文失败: {e}")
|
||
return []
|
||
|
||
def save_conversation(self, chat_id: str, cookie_id: str, user_id: str,
|
||
item_id: str, role: str, content: str, intent: str = None) -> Optional[str]:
|
||
"""保存对话记录，返回创建时间"""
|
||
try:
|
||
with db_manager.lock:
|
||
cursor = db_manager.conn.cursor()
|
||
cursor.execute('''
|
||
INSERT INTO ai_conversations
|
||
(cookie_id, chat_id, user_id, item_id, role, content, intent)
|
||
VALUES (?, ?, ?, ?, ?, ?, ?)
|
||
''', (cookie_id, chat_id, user_id, item_id, role, content, intent))
|
||
db_manager.conn.commit()
|
||
|
||
# 获取刚插入记录的created_at
|
||
cursor.execute('''
|
||
SELECT created_at FROM ai_conversations
|
||
WHERE rowid = last_insert_rowid()
|
||
''')
|
||
result = cursor.fetchone()
|
||
return result[0] if result else None
|
||
except Exception as e:
|
||
logger.error(f"保存对话记录失败: {e}")
|
||
return None
|
||
def get_conversation_rounds(self, chat_id: str, cookie_id: str) -> int:
|
||
"""获取对话轮数（用户消息数量）"""
|
||
try:
|
||
with db_manager.lock:
|
||
cursor = db_manager.conn.cursor()
|
||
cursor.execute('''
|
||
SELECT COUNT(*) FROM ai_conversations
|
||
WHERE chat_id = ? AND cookie_id = ? AND role = 'user'
|
||
''', (chat_id, cookie_id))
|
||
|
||
result = cursor.fetchone()
|
||
return result[0] if result else 0
|
||
except Exception as e:
|
||
logger.error(f"获取对话轮数失败: {e}")
|
||
return 0
|
||
|
||
def _get_recent_user_messages(self, chat_id: str, cookie_id: str, seconds: int = 2) -> List[Dict]:
|
||
"""获取最近seconds秒内的所有用户消息（包含内容和时间戳）"""
|
||
try:
|
||
with db_manager.lock:
|
||
cursor = db_manager.conn.cursor()
|
||
# 先查询所有该chat的user消息，用于调试
|
||
cursor.execute('''
|
||
SELECT content, created_at,
|
||
julianday('now') - julianday(created_at) as time_diff_days,
|
||
(julianday('now') - julianday(created_at)) * 86400.0 as time_diff_seconds
|
||
FROM ai_conversations
|
||
WHERE chat_id = ? AND cookie_id = ? AND role = 'user'
|
||
ORDER BY created_at DESC LIMIT 10
|
||
''', (chat_id, cookie_id))
|
||
|
||
all_messages = cursor.fetchall()
|
||
logger.info(f"【调试】chat_id={chat_id} 最近10条user消息: {[(msg[0][:10], msg[1], f'{msg[3]:.2f}秒前') for msg in all_messages]}")
|
||
|
||
# 正式查询
|
||
cursor.execute('''
|
||
SELECT content, created_at FROM ai_conversations
|
||
WHERE chat_id = ? AND cookie_id = ? AND role = 'user'
|
||
AND julianday('now') - julianday(created_at) < (? / 86400.0)
|
||
ORDER BY created_at ASC
|
||
''', (chat_id, cookie_id, seconds))
|
||
|
||
results = cursor.fetchall()
|
||
return [{"content": row[0], "created_at": row[1]} for row in results]
|
||
except Exception as e:
|
||
logger.error(f"获取最近用户消息列表失败: {e}")
|
||
return []
|
||
|
||
|
||
|
||
# 全局AI回复引擎实例
|
||
ai_reply_engine = AIReplyEngine()
|