150 lines
5.1 KiB
Python
150 lines
5.1 KiB
Python
import requests
|
|
import json
|
|
import logging
|
|
from typing import Dict, List, Optional, Any
|
|
from datetime import datetime
|
|
|
|
from ..config.config import Config
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
class QwenClient:
|
|
"""阿里云千问API客户端"""
|
|
|
|
def __init__(self):
|
|
self.api_config = Config.get_api_config()
|
|
self.base_url = self.api_config["base_url"]
|
|
self.api_key = self.api_config["api_key"]
|
|
self.model_name = self.api_config["model_name"]
|
|
self.headers = {
|
|
"Authorization": f"Bearer {self.api_key}",
|
|
"Content-Type": "application/json"
|
|
}
|
|
|
|
def chat_completion(
|
|
self,
|
|
messages: List[Dict[str, str]],
|
|
temperature: float = 0.7,
|
|
max_tokens: int = 1000,
|
|
stream: bool = False
|
|
) -> Dict[str, Any]:
|
|
"""发送聊天请求"""
|
|
try:
|
|
url = f"{self.base_url}/chat/completions"
|
|
payload = {
|
|
"model": self.model_name,
|
|
"messages": messages,
|
|
"temperature": temperature,
|
|
"max_tokens": max_tokens,
|
|
"stream": stream
|
|
}
|
|
|
|
response = requests.post(
|
|
url,
|
|
headers=self.headers,
|
|
json=payload,
|
|
timeout=Config.RESPONSE_TIMEOUT
|
|
)
|
|
|
|
if response.status_code == 200:
|
|
result = response.json()
|
|
logger.info("API请求成功")
|
|
return result
|
|
else:
|
|
logger.error(f"API请求失败: {response.status_code} - {response.text}")
|
|
return {"error": f"API请求失败: {response.status_code}"}
|
|
|
|
except requests.exceptions.Timeout:
|
|
logger.error("API请求超时")
|
|
return {"error": "请求超时"}
|
|
except requests.exceptions.RequestException as e:
|
|
logger.error(f"API请求异常: {e}")
|
|
return {"error": f"请求异常: {str(e)}"}
|
|
except Exception as e:
|
|
logger.error(f"未知错误: {e}")
|
|
return {"error": f"未知错误: {str(e)}"}
|
|
|
|
def generate_response(
|
|
self,
|
|
user_message: str,
|
|
context: Optional[str] = None,
|
|
knowledge_base: Optional[List[str]] = None
|
|
) -> Dict[str, Any]:
|
|
"""生成回复"""
|
|
messages = []
|
|
|
|
# 系统提示词
|
|
system_prompt = "你是一个专业的客服助手，请根据用户问题提供准确、 helpful的回复。"
|
|
if context:
|
|
system_prompt += f"\n\n上下文信息: {context}"
|
|
if knowledge_base:
|
|
system_prompt += f"\n\n相关知识库: {' '.join(knowledge_base)}"
|
|
|
|
messages.append({"role": "system", "content": system_prompt})
|
|
messages.append({"role": "user", "content": user_message})
|
|
|
|
result = self.chat_completion(messages)
|
|
|
|
if "error" in result:
|
|
return result
|
|
|
|
try:
|
|
response_content = result["choices"][0]["message"]["content"]
|
|
return {
|
|
"response": response_content,
|
|
"usage": result.get("usage", {}),
|
|
"model": result.get("model", ""),
|
|
"timestamp": datetime.now().isoformat()
|
|
}
|
|
except (KeyError, IndexError) as e:
|
|
logger.error(f"解析API响应失败: {e}")
|
|
return {"error": f"解析响应失败: {str(e)}"}
|
|
|
|
def extract_entities(self, text: str) -> Dict[str, Any]:
|
|
"""提取文本中的实体信息"""
|
|
prompt = f"""
|
|
请从以下文本中提取关键信息，包括：
|
|
1. 问题类型/类别
|
|
2. 优先级（高/中/低）
|
|
3. 关键词
|
|
4. 情感倾向（正面/负面/中性）
|
|
|
|
文本: {text}
|
|
|
|
请以JSON格式返回结果。
|
|
"""
|
|
|
|
messages = [
|
|
{"role": "system", "content": "你是一个信息提取专家，请准确提取文本中的关键信息。"},
|
|
{"role": "user", "content": prompt}
|
|
]
|
|
|
|
result = self.chat_completion(messages, temperature=0.3)
|
|
|
|
if "error" in result:
|
|
return result
|
|
|
|
try:
|
|
response_content = result["choices"][0]["message"]["content"]
|
|
# 尝试解析JSON
|
|
import re
|
|
json_match = re.search(r'\{.*\}', response_content, re.DOTALL)
|
|
if json_match:
|
|
return json.loads(json_match.group())
|
|
else:
|
|
return {"raw_response": response_content}
|
|
except Exception as e:
|
|
logger.error(f"解析实体提取结果失败: {e}")
|
|
return {"error": f"解析失败: {str(e)}"}
|
|
|
|
def test_connection(self) -> bool:
|
|
"""测试API连接"""
|
|
try:
|
|
result = self.chat_completion([
|
|
{"role": "user", "content": "你好"}
|
|
], max_tokens=10)
|
|
return "error" not in result
|
|
except Exception as e:
|
|
logger.error(f"API连接测试失败: {e}")
|
|
return False
|