299 lines
9.2 KiB
Python
299 lines
9.2 KiB
Python
"""
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任务相关的业务逻辑服务
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"""
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import re
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from typing import Dict, Tuple
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from django.conf import settings
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class AITitleGenerator:
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"""AI标题生成服务"""
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@staticmethod
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def generate_title_and_description(requirement: str) -> Tuple[str, str]:
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"""
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根据用户需求生成任务标题和描述
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Args:
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requirement: 用户需求描述
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Returns:
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tuple: (标题, 描述)
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"""
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# 简化版本的关键词匹配（实际应该调用OpenAI API）
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keywords = requirement.lower()
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# 默认值
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title = '信息监控任务'
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description = '智能监控和分析任务'
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# 关键词匹配规则
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if any(keyword in keywords for keyword in ['openai', 'ai', '人工智能', '机器学习']):
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title = 'AI技术动态监控'
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description = '实时监控AI技术发展动态和产品更新'
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elif any(keyword in keywords for keyword in ['产品', '功能', '发布', '更新']):
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title = '产品功能监控'
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description = '跟踪产品功能更新和发布动态'
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elif any(keyword in keywords for keyword in ['新闻', '资讯', '动态', '消息']):
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title = '新闻资讯监控'
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description = '收集和分析相关新闻资讯'
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elif any(keyword in keywords for keyword in ['市场', '行业', '竞争', '分析']):
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title = '市场行业分析'
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description = '监控市场动态和行业发展趋势'
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elif any(keyword in keywords for keyword in ['股票', '金融', '投资', '财经']):
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title = '金融投资监控'
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description = '跟踪金融市场和投资机会'
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elif any(keyword in keywords for keyword in ['政策', '法规', '监管', '政府']):
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title = '政策法规监控'
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description = '监控政策变化和法规更新'
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# 从需求中提取关注对象，添加到标题中
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object_match = re.search(r'关注对象[：:]\s*([^\n\r，,。.]+)', requirement)
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if object_match:
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focus_object = object_match.group(1).strip()
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if focus_object and len(focus_object) < 20:
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title = f'{focus_object} - {title}'
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return title, description
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@staticmethod
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def generate_with_openai(requirement: str) -> Tuple[str, str]:
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"""
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使用OpenAI API生成标题和描述（备用方案）
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Args:
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requirement: 用户需求描述
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Returns:
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tuple: (标题, 描述)
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"""
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try:
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import openai
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if not settings.OPENAI_API_KEY:
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return AITitleGenerator.generate_title_and_description(requirement)
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openai.api_key = settings.OPENAI_API_KEY
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prompt = f"""
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根据以下用户需求，生成一个简洁的任务标题和一句话描述：
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用户需求：
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{requirement}
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请返回JSON格式：
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{{
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"title": "任务标题（不超过20字）",
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"description": "任务描述（不超过50字）"
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}}
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"""
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response = openai.ChatCompletion.create(
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model="gpt-3.5-turbo",
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messages=[
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{"role": "system", "content": "你是一个专业的任务管理助手，擅长根据用户需求生成简洁明确的任务标题和描述。"},
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{"role": "user", "content": prompt}
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],
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max_tokens=200,
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temperature=0.7
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)
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result = response.choices[0].message.content
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# 解析JSON响应
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import json
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parsed = json.loads(result)
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return parsed.get('title', '信息监控任务'), parsed.get('description', '智能监控和分析任务')
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except Exception as e:
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print(f"OpenAI API调用失败: {e}")
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# 降级到本地生成
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return AITitleGenerator.generate_title_and_description(requirement)
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class TaskService:
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"""任务业务逻辑服务"""
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@staticmethod
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def create_task(user, task_data: Dict):
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"""
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创建新任务
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Args:
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user: 用户对象
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task_data: 任务数据
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Returns:
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Task: 创建的任务对象
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"""
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# 生成AI标题和描述
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title, description = AITitleGenerator.generate_title_and_description(
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task_data['requirement']
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)
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# 构建信息源配置
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sources_config = {
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'presetSources': task_data.get('presetSources', []),
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'customSources': task_data.get('customSources', []),
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'webSearchEnabled': task_data.get('webSearchEnabled', True)
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}
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# 导入模型
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from .models import Task, TaskLog
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# 创建任务
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task = Task.objects.create(
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title=title,
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description=description,
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requirement=task_data['requirement'],
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type=task_data['type'],
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schedule_config=task_data.get('schedule'),
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sources_config=sources_config,
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web_search_enabled=task_data.get('webSearchEnabled', True),
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user=user
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)
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# 记录创建日志
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TaskLog.objects.create(
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task=task,
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action_type='created',
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message=f'任务创建成功: {title}'
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)
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# 如果是单次任务，立即开始执行
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if task.type == 'single':
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TaskService.start_task_execution(task)
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return task
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@staticmethod
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def start_task_execution(task):
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"""
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开始执行任务
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Args:
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task: 任务对象
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"""
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# 更新任务状态
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task.status = 'running'
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task.save(update_fields=['status'])
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# 导入模型
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from .models import TaskLog
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# 记录开始执行日志
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TaskLog.objects.create(
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task=task,
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action_type='started',
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message='任务开始执行'
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)
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# 这里可以添加Celery异步任务调用
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# from .tasks import execute_task
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# execute_task.delay(task.id)
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@staticmethod
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def pause_task(task):
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"""暂停任务"""
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# 导入模型
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from .models import TaskLog
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task.status = 'paused'
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task.save(update_fields=['status'])
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TaskLog.objects.create(
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task=task,
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action_type='paused',
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message='任务已暂停'
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)
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@staticmethod
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def resume_task(task):
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"""恢复任务"""
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# 导入模型
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from .models import TaskLog
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task.status = 'running'
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task.save(update_fields=['status'])
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TaskLog.objects.create(
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task=task,
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action_type='resumed',
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message='任务已恢复'
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)
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class ReportService:
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"""报告业务逻辑服务"""
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@staticmethod
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def create_mock_report(task):
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"""
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创建模拟报告（用于演示）
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Args:
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task: 任务对象
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Returns:
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Report: 创建的报告对象
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"""
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# 根据任务需求生成模拟报告内容
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requirement = task.requirement.lower()
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if 'openai' in requirement or 'ai' in requirement:
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title = 'OpenAI GPT-4 Turbo重大更新'
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summary = 'OpenAI发布GPT-4 Turbo新版本，支持更长上下文，性能显著提升...'
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content = """# OpenAI GPT-4 Turbo重大更新
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## 主要更新内容
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1. **上下文长度提升**: 支持128K tokens的上下文长度
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2. **性能优化**: 推理速度提升2倍
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3. **成本降低**: API调用成本降低50%
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## 详细分析
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这次更新标志着AI技术的重要进展...
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"""
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source_tag = 'OpenAI官网'
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else:
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title = f'{task.title} - 最新动态报告'
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summary = f'基于您的需求「{task.requirement[:50]}...」生成的分析报告'
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content = f"""# {task.title}
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## 监控概要
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根据您的需求进行信息收集和分析。
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## 关键发现
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1. 相关信息已收集完成
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2. 数据分析正在进行中
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3. 将持续监控相关动态
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## 详细内容
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{task.requirement}
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## 下一步计划
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继续监控相关信息源，及时更新分析结果。
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"""
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source_tag = '联网搜索'
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# 导入模型
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from .models import Report, ReportSource
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report = Report.objects.create(
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task=task,
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title=title,
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summary=summary,
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content=content,
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source_tag=source_tag,
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status='completed'
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)
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# 创建报告来源记录
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ReportSource.objects.create(
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report=report,
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source_type='web-search',
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source_name='联网搜索',
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extracted_content=summary
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)
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return report
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