363 lines
14 KiB
Python
363 lines
14 KiB
Python
"""
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AI服务 - 封装大模型调用
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仅支持华为大模型
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"""
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import os
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import re
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import time
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import requests
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import json
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from typing import Dict, List, Optional
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class AIService:
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"""AI服务类"""
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def __init__(self):
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# 华为大模型配置（必需）
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self.huawei_api_endpoint = os.getenv('HUAWEI_API_ENDPOINT', 'http://10.100.31.26:3001/v1/chat/completions')
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self.huawei_api_key = os.getenv('HUAWEI_API_KEY', 'sk-PoeiV3qwyTIRqcVc84E8E24cD2904872859a87922e0d9186')
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self.huawei_model = os.getenv('HUAWEI_MODEL', 'DeepSeek-R1-Distill-Llama-70B')
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# 确定使用的AI服务
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self.ai_provider = self._determine_ai_provider()
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def _determine_ai_provider(self) -> str:
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"""确定使用的AI服务提供商（仅支持华为大模型）"""
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if self.huawei_api_endpoint and self.huawei_api_key:
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return 'huawei'
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else:
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return 'none'
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def extract_fields(self, prompt: str, output_fields: List[Dict]) -> Optional[Dict]:
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"""
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从提示词中提取结构化字段
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Args:
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prompt: AI提示词
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output_fields: 输出字段列表
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Returns:
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提取的字段字典，格式: {field_code: field_value}
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"""
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if self.ai_provider == 'none':
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raise Exception("未配置华为大模型服务，请设置 HUAWEI_API_KEY 和 HUAWEI_API_ENDPOINT")
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if self.ai_provider == 'huawei':
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return self._extract_with_huawei(prompt, output_fields)
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else:
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raise Exception(f"未知的AI服务提供商: {self.ai_provider}")
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def _extract_with_siliconflow(self, prompt: str, output_fields: List[Dict]) -> Optional[Dict]:
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"""
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使用硅基流动API提取字段（已不再使用，仅保留用于参考）
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系统仅支持华为大模型，不再支持自动回退
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"""
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try:
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payload = {
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"model": self.siliconflow_model,
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"messages": [
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{
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"role": "system",
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"content": "你是一个专业的数据提取助手，能够从文本中准确提取结构化信息。请严格按照JSON格式返回结果。"
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},
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{
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"role": "user",
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"content": prompt
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}
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],
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"temperature": 0.3,
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"max_tokens": 2000
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}
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headers = {
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"Authorization": f"Bearer {self.siliconflow_api_key}",
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"Content-Type": "application/json"
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}
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response = requests.post(
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self.siliconflow_url,
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json=payload,
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headers=headers,
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timeout=30
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)
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if response.status_code != 200:
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raise Exception(f"API调用失败: {response.status_code} - {response.text}")
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result = response.json()
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# 提取AI返回的内容
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if 'choices' in result and len(result['choices']) > 0:
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content = result['choices'][0]['message']['content']
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# 尝试解析JSON
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try:
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# 如果返回的是代码块，提取JSON部分
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if '```json' in content:
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json_start = content.find('```json') + 7
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json_end = content.find('```', json_start)
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content = content[json_start:json_end].strip()
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elif '```' in content:
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json_start = content.find('```') + 3
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json_end = content.find('```', json_start)
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content = content[json_start:json_end].strip()
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extracted_data = json.loads(content)
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return extracted_data
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except json.JSONDecodeError:
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# 如果不是JSON，尝试从文本中提取
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return self._parse_text_response(content, output_fields)
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else:
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raise Exception("API返回格式异常")
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except requests.exceptions.Timeout:
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raise Exception("AI服务调用超时")
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except Exception as e:
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raise Exception(f"AI服务调用失败: {str(e)}")
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def _extract_with_huawei(self, prompt: str, output_fields: List[Dict]) -> Optional[Dict]:
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"""
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使用华为大模型API提取字段（带重试机制）
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至少重试3次，总共最多尝试4次
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"""
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max_retries = 3 # 最多重试3次，总共4次尝试
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retry_delay = 1 # 重试延迟（秒），每次重试延迟递增
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last_exception = None
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for attempt in range(max_retries + 1): # 0, 1, 2, 3 (总共4次)
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try:
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if attempt > 0:
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# 重试前等待，延迟时间递增（1秒、2秒、3秒）
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wait_time = retry_delay * attempt
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print(f"[AI服务] 第 {attempt} 次重试，等待 {wait_time} 秒后重试...")
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time.sleep(wait_time)
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print(f"[AI服务] 正在调用华为大模型API (尝试 {attempt + 1}/{max_retries + 1})...")
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result = self._call_huawei_api_once(prompt, output_fields)
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if result is not None:
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if attempt > 0:
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print(f"[AI服务] 重试成功！")
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return result
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except requests.exceptions.Timeout as e:
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last_exception = e
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error_msg = f"AI服务调用超时 (尝试 {attempt + 1}/{max_retries + 1})"
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print(f"[AI服务] {error_msg}")
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if attempt < max_retries:
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continue
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else:
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raise Exception(f"{error_msg}: {str(e)}")
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except requests.exceptions.ConnectionError as e:
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last_exception = e
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error_msg = f"连接错误 (尝试 {attempt + 1}/{max_retries + 1})"
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print(f"[AI服务] {error_msg}: {str(e)}")
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if attempt < max_retries:
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continue
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else:
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raise Exception(f"{error_msg}: {str(e)}")
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except requests.exceptions.RequestException as e:
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last_exception = e
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error_msg = f"请求异常 (尝试 {attempt + 1}/{max_retries + 1})"
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print(f"[AI服务] {error_msg}: {str(e)}")
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if attempt < max_retries:
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continue
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else:
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raise Exception(f"{error_msg}: {str(e)}")
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except Exception as e:
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last_exception = e
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error_msg = f"AI服务调用失败 (尝试 {attempt + 1}/{max_retries + 1})"
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print(f"[AI服务] {error_msg}: {str(e)}")
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# 对于其他类型的错误，也进行重试
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if attempt < max_retries:
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continue
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else:
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raise Exception(f"{error_msg}: {str(e)}")
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# 如果所有重试都失败了
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if last_exception:
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raise Exception(f"AI服务调用失败，已重试 {max_retries} 次: {str(last_exception)}")
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else:
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raise Exception(f"AI服务调用失败，已重试 {max_retries} 次")
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def _call_huawei_api_once(self, prompt: str, output_fields: List[Dict]) -> Optional[Dict]:
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"""
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单次调用华为大模型API（不包含重试逻辑）
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"""
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payload = {
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"model": self.huawei_model,
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"messages": [
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{
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"role": "system",
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"content": "你是一个专业的数据提取助手，擅长从非结构化文本中准确提取结构化信息。你需要仔细分析输入文本，识别所有相关信息，并按照指定的JSON格式返回结果。请确保提取的信息准确、完整，对于能够从文本中推断出的信息也要尽量提取。"
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},
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{
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"role": "user",
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"content": prompt
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}
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],
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"stream": False,
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"presence_penalty": 1.03,
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"frequency_penalty": 1.0,
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"repetition_penalty": 1.0,
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"temperature": 0.3,
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"top_p": 0.95,
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"top_k": 1,
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"seed": 1,
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"max_tokens": 8192,
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"n": 1,
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"enable_thinking": True
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}
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headers = {
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"Authorization": f"Bearer {self.huawei_api_key}",
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"Content-Type": "application/json"
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}
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response = requests.post(
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self.huawei_api_endpoint,
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json=payload,
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headers=headers,
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timeout=60
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)
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if response.status_code != 200:
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raise Exception(f"API调用失败: {response.status_code} - {response.text}")
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result = response.json()
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# 提取AI返回的内容
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if 'choices' in result and len(result['choices']) > 0:
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content = result['choices'][0]['message']['content']
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# 处理思考过程标签（华为大模型可能返回思考过程）
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# 移除思考过程标签之前的内容，只保留实际回答
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# 根据用户提供的示例，华为大模型使用 </think> 标签
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if '</think>' in content:
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content = content.split('</think>')[-1].strip()
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# 尝试解析JSON
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extracted_data = self._extract_json_from_text(content)
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if extracted_data:
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return extracted_data
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# 如果无法提取JSON，尝试从文本中提取
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return self._parse_text_response(content, output_fields)
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else:
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raise Exception("API返回格式异常：未找到choices字段或choices为空")
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def _extract_json_from_text(self, text: str) -> Optional[Dict]:
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"""
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从文本中提取JSON对象
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支持多种格式：
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1. 纯JSON对象
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2. 包裹在 ```json 代码块中的JSON
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3. 包裹在 ``` 代码块中的JSON
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4. 文本中包含的JSON对象
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"""
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# 方法1: 尝试提取代码块中的JSON
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if '```json' in text:
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json_start = text.find('```json') + 7
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json_end = text.find('```', json_start)
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if json_end != -1:
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json_str = text[json_start:json_end].strip()
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try:
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return json.loads(json_str)
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except json.JSONDecodeError:
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pass
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if '```' in text:
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json_start = text.find('```') + 3
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json_end = text.find('```', json_start)
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if json_end != -1:
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json_str = text[json_start:json_end].strip()
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# 如果不是json标记，尝试解析
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try:
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return json.loads(json_str)
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except json.JSONDecodeError:
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pass
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# 方法2: 尝试直接解析整个文本
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try:
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return json.loads(text.strip())
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except json.JSONDecodeError:
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pass
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# 方法3: 尝试查找文本中的JSON对象（以 { 开始，以 } 结束）
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# 使用正则表达式找到最外层的JSON对象
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json_pattern = r'\{[^{}]*(?:\{[^{}]*\}[^{}]*)*\}'
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matches = re.finditer(json_pattern, text, re.DOTALL)
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for match in matches:
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json_str = match.group(0)
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try:
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data = json.loads(json_str)
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# 验证是否包含预期的字段（至少有一个输出字段的key）
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if isinstance(data, dict) and len(data) > 0:
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return data
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except json.JSONDecodeError:
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continue
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# 方法4: 尝试查找嵌套的JSON对象（更复杂的匹配）
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# 找到第一个 { 和最后一个匹配的 }
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start_idx = text.find('{')
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if start_idx != -1:
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brace_count = 0
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end_idx = start_idx
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for i in range(start_idx, len(text)):
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if text[i] == '{':
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brace_count += 1
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elif text[i] == '}':
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brace_count -= 1
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if brace_count == 0:
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end_idx = i
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break
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if end_idx > start_idx:
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json_str = text[start_idx:end_idx + 1]
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try:
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return json.loads(json_str)
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except json.JSONDecodeError:
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pass
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return None
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def _parse_text_response(self, text: str, output_fields: List[Dict]) -> Dict:
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"""
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从文本响应中解析字段值（备用方案）
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"""
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result = {}
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for field in output_fields:
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field_code = field['field_code']
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field_name = field['name']
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# 尝试在文本中查找字段值
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# 这里使用简单的关键词匹配，实际可以更复杂
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if field_name in text:
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# 提取字段值（简单实现）
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start_idx = text.find(field_name)
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if start_idx != -1:
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# 查找冒号后的内容
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colon_idx = text.find(':', start_idx)
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if colon_idx != -1:
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value_start = colon_idx + 1
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value_end = text.find('\n', value_start)
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if value_end == -1:
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value_end = len(text)
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value = text[value_start:value_end].strip()
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result[field_code] = value
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else:
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result[field_code] = ''
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else:
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result[field_code] = ''
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else:
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result[field_code] = ''
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return result
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