0bbf68d5bb
- 新增 AgentSchedule 模型、CRUD API、调度服务 - 集成 Celery Beat 每分钟检查到期任务并自动触发执行 - 支持手动触发、cron 表达式解析、执行状态跟踪 - 依赖: croniter (cron 表达式解析) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
55 lines
1.5 KiB
Python
55 lines
1.5 KiB
Python
"""
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数据库配置
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"""
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from sqlalchemy import create_engine
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from sqlalchemy.ext.declarative import declarative_base
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from sqlalchemy.orm import sessionmaker
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from app.core.config import settings
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# 创建数据库引擎（MySQL）
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engine = create_engine(
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settings.DATABASE_URL,
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pool_pre_ping=True,
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pool_size=10,
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max_overflow=20,
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echo=settings.DEBUG # 开发环境显示SQL
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)
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# 创建会话工厂
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# expire_on_commit=False：commit 后仍可读取已加载标量，避免 FastAPI 在序列化 ExecutionResponse 时
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# 因会话已关闭而再次触发懒加载，从而出现「仅 HTTP 报 DATABASE_ERROR、TestClient 正常」的现象。
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SessionLocal = sessionmaker(
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autocommit=False, autoflush=False, bind=engine, expire_on_commit=False
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)
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# 创建基础模型类
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Base = declarative_base()
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def get_db():
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"""获取数据库会话"""
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db = SessionLocal()
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try:
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yield db
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finally:
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db.close()
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def init_db():
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"""初始化数据库，创建所有表"""
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# 导入所有模型，确保它们被注册
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import app.models.user
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import app.models.workflow
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import app.models.agent
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import app.models.execution
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import app.models.model_config
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import app.models.workflow_template
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import app.models.permission
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import app.models.alert_rule
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import app.models.agent_llm_log
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import app.models.agent_vector_memory
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import app.models.agent_learning_pattern
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import app.models.agent_schedule
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import app.models.knowledge_base
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Base.metadata.create_all(bind=engine)
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