mirror of
https://github.com/bytedance/deer-flow.git
synced 2026-05-05 08:18:22 +00:00
1ff6b5f7ab
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>
14 lines
547 B
Python
14 lines
547 B
Python
"""DeerFlow application persistence layer (SQLAlchemy 2.0 async ORM).
|
|
|
|
This module manages DeerFlow's own application data -- runs metadata,
|
|
thread ownership, cron jobs, users. It is completely separate from
|
|
LangGraph's checkpointer, which manages graph execution state.
|
|
|
|
Usage:
|
|
from deerflow.persistence import init_engine, close_engine, get_session_factory
|
|
"""
|
|
|
|
from deerflow.persistence.engine import close_engine, get_engine, get_session_factory, init_engine
|
|
|
|
__all__ = ["close_engine", "get_engine", "get_session_factory", "init_engine"]
|