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https://github.com/bytedance/deer-flow.git
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feat(persistence): add SQLAlchemy 2.0 async ORM scaffold
Introduce a unified database configuration (DatabaseConfig) that controls both the LangGraph checkpointer and the DeerFlow application persistence layer from a single `database:` config section. New modules: - deerflow.config.database_config — Pydantic config with memory/sqlite/postgres backends - deerflow.persistence — async engine lifecycle, DeclarativeBase with to_dict mixin, Alembic skeleton - deerflow.runtime.runs.store — RunStore ABC + MemoryRunStore implementation Gateway integration initializes/tears down the persistence engine in the existing langgraph_runtime() context manager. Legacy checkpointer config is preserved for backward compatibility. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
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"""DeerFlow application persistence layer (SQLAlchemy 2.0 async ORM).
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This module manages DeerFlow's own application data -- runs metadata,
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thread ownership, cron jobs, users. It is completely separate from
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LangGraph's checkpointer, which manages graph execution state.
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Usage:
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from deerflow.persistence import init_engine, close_engine, get_session_factory
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"""
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from deerflow.persistence.engine import close_engine, get_engine, get_session_factory, init_engine
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__all__ = ["close_engine", "get_engine", "get_session_factory", "init_engine"]
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"""SQLAlchemy declarative base with automatic to_dict support.
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All DeerFlow ORM models inherit from this Base. It provides a generic
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to_dict() method via SQLAlchemy's inspect() so individual models don't
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need to write their own serialization logic.
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LangGraph's checkpointer tables are NOT managed by this Base.
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"""
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from __future__ import annotations
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from sqlalchemy import inspect as sa_inspect
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from sqlalchemy.orm import DeclarativeBase
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class Base(DeclarativeBase):
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"""Base class for all DeerFlow ORM models.
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Provides:
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- Automatic to_dict() via SQLAlchemy column inspection.
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- Standard __repr__() showing all column values.
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"""
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def to_dict(self, *, exclude: set[str] | None = None) -> dict:
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"""Convert ORM instance to plain dict.
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Uses SQLAlchemy's inspect() to iterate mapped column attributes.
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Args:
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exclude: Optional set of column keys to omit.
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Returns:
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Dict of {column_key: value} for all mapped columns.
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"""
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exclude = exclude or set()
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return {c.key: getattr(self, c.key) for c in sa_inspect(type(self)).mapper.column_attrs if c.key not in exclude}
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def __repr__(self) -> str:
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cols = ", ".join(f"{c.key}={getattr(self, c.key)!r}" for c in sa_inspect(type(self)).mapper.column_attrs)
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return f"{type(self).__name__}({cols})"
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"""Async SQLAlchemy engine lifecycle management.
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Initializes at Gateway startup, provides session factory for
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repositories, disposes at shutdown.
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When database.backend="memory", init_engine is a no-op and
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get_session_factory() returns None. Repositories must check for
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None and fall back to in-memory implementations.
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"""
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from __future__ import annotations
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import logging
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from sqlalchemy.ext.asyncio import AsyncEngine, AsyncSession, async_sessionmaker, create_async_engine
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logger = logging.getLogger(__name__)
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_engine: AsyncEngine | None = None
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_session_factory: async_sessionmaker[AsyncSession] | None = None
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async def init_engine(
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backend: str,
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*,
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url: str = "",
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echo: bool = False,
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pool_size: int = 5,
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sqlite_dir: str = "",
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) -> None:
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"""Create the async engine and session factory, then auto-create tables.
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Args:
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backend: "memory", "sqlite", or "postgres".
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url: SQLAlchemy async URL (for sqlite/postgres).
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echo: Echo SQL to log.
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pool_size: Postgres connection pool size.
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sqlite_dir: Directory to create for SQLite (ensured to exist).
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"""
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global _engine, _session_factory
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if backend == "memory":
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logger.info("Persistence backend=memory -- ORM engine not initialized")
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return
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if backend == "postgres":
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try:
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import asyncpg # noqa: F401
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except ImportError:
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raise ImportError("database.backend is set to 'postgres' but asyncpg is not installed.\nInstall it with:\n uv sync --extra postgres\nOr switch to backend: sqlite in config.yaml for single-node deployment.") from None
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if backend == "sqlite":
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import os
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os.makedirs(sqlite_dir or ".", exist_ok=True)
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_engine = create_async_engine(url, echo=echo)
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elif backend == "postgres":
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_engine = create_async_engine(
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url,
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echo=echo,
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pool_size=pool_size,
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pool_pre_ping=True,
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)
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else:
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raise ValueError(f"Unknown persistence backend: {backend!r}")
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_session_factory = async_sessionmaker(_engine, expire_on_commit=False)
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# Auto-create tables (dev convenience). Production should use Alembic.
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from deerflow.persistence.base import Base
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# Import all models so Base.metadata discovers them.
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# When no models exist yet (scaffolding phase), this is a no-op.
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try:
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import deerflow.persistence.models # noqa: F401
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except ImportError:
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pass
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async with _engine.begin() as conn:
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await conn.run_sync(Base.metadata.create_all)
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logger.info("Persistence engine initialized: backend=%s", backend)
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async def init_engine_from_config(config) -> None:
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"""Convenience: init engine from a DatabaseConfig object."""
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if config.backend == "memory":
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await init_engine("memory")
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return
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await init_engine(
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backend=config.backend,
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url=config.app_sqlalchemy_url,
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echo=config.echo_sql,
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pool_size=config.pool_size,
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sqlite_dir=config.sqlite_dir if config.backend == "sqlite" else "",
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)
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def get_session_factory() -> async_sessionmaker[AsyncSession] | None:
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"""Return the async session factory, or None if backend=memory."""
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return _session_factory
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def get_engine() -> AsyncEngine | None:
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"""Return the async engine, or None if not initialized."""
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return _engine
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async def close_engine() -> None:
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"""Dispose the engine, release all connections."""
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global _engine, _session_factory
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if _engine is not None:
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await _engine.dispose()
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logger.info("Persistence engine closed")
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_engine = None
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_session_factory = None
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[alembic]
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script_location = %(here)s
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# Default URL for offline mode / autogenerate.
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# Runtime uses engine from DeerFlow config.
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sqlalchemy.url = sqlite+aiosqlite:///./data/app.db
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[loggers]
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keys = root,sqlalchemy,alembic
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[handlers]
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keys = console
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[formatters]
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keys = generic
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[logger_root]
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level = WARN
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handlers = console
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[logger_sqlalchemy]
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level = WARN
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handlers =
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qualname = sqlalchemy.engine
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[logger_alembic]
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level = INFO
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handlers =
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qualname = alembic
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[handler_console]
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class = StreamHandler
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args = (sys.stderr,)
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level = NOTSET
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formatter = generic
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[formatter_generic]
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format = %(levelname)-5.5s [%(name)s] %(message)s
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datefmt = %H:%M:%S
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"""Alembic environment for DeerFlow application tables.
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ONLY manages DeerFlow's tables (runs, threads_meta, cron_jobs, users).
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LangGraph's checkpointer tables are managed by LangGraph itself -- they
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have their own schema lifecycle and must not be touched by Alembic.
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"""
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from __future__ import annotations
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import asyncio
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from logging.config import fileConfig
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from alembic import context
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from sqlalchemy.ext.asyncio import create_async_engine
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from deerflow.persistence.base import Base
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# Import all models so metadata is populated.
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try:
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import deerflow.persistence.models # noqa: F401
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except ImportError:
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pass
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config = context.config
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if config.config_file_name is not None:
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fileConfig(config.config_file_name)
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target_metadata = Base.metadata
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def run_migrations_offline() -> None:
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url = config.get_main_option("sqlalchemy.url")
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context.configure(
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url=url,
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target_metadata=target_metadata,
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literal_binds=True,
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render_as_batch=True,
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)
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with context.begin_transaction():
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context.run_migrations()
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def do_run_migrations(connection):
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context.configure(
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connection=connection,
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target_metadata=target_metadata,
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render_as_batch=True, # Required for SQLite ALTER TABLE support
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)
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with context.begin_transaction():
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context.run_migrations()
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async def run_migrations_online() -> None:
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connectable = create_async_engine(config.get_main_option("sqlalchemy.url"))
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async with connectable.connect() as connection:
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await connection.run_sync(do_run_migrations)
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await connectable.dispose()
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if context.is_offline_mode():
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run_migrations_offline()
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else:
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asyncio.run(run_migrations_online())
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