greatmengqi
edf345cd72
- Freeze all config models (AppConfig + 15 sub-configs) with frozen=True - Purify from_file() — remove 9 load_*_from_dict() side-effect calls - Replace mtime/reload/push/pop machinery with single ContextVar + init_app_config() - Delete 10 sub-module globals and their getters/setters/loaders - Migrate 50+ consumers from get_*_config() to get_app_config().xxx - Expand DeerFlowContext: app_config + thread_id + agent_name (frozen dataclass) - Wire into Gateway runtime (worker.py) and DeerFlowClient via context= parameter - Remove sandbox_id from runtime.context — flows through ThreadState.sandbox only - Middleware/tools access runtime.context directly via Runtime[DeerFlowContext] generic - resolve_context() retained at server entry points for LangGraph Server fallback
64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
"""Configuration for memory mechanism."""
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from pydantic import BaseModel, ConfigDict, Field
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class MemoryConfig(BaseModel):
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"""Configuration for global memory mechanism."""
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model_config = ConfigDict(frozen=True)
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enabled: bool = Field(
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default=True,
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description="Whether to enable memory mechanism",
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)
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storage_path: str = Field(
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default="",
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description=(
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"Path to store memory data. "
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"If empty, defaults to `{base_dir}/memory.json` (see Paths.memory_file). "
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"Absolute paths are used as-is. "
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"Relative paths are resolved against `Paths.base_dir` "
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"(not the backend working directory). "
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"Note: if you previously set this to `.deer-flow/memory.json`, "
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"the file will now be resolved as `{base_dir}/.deer-flow/memory.json`; "
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"migrate existing data or use an absolute path to preserve the old location."
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),
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)
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storage_class: str = Field(
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default="deerflow.agents.memory.storage.FileMemoryStorage",
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description="The class path for memory storage provider",
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)
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debounce_seconds: int = Field(
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default=30,
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ge=1,
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le=300,
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description="Seconds to wait before processing queued updates (debounce)",
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)
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model_name: str | None = Field(
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default=None,
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description="Model name to use for memory updates (None = use default model)",
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)
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max_facts: int = Field(
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default=100,
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ge=10,
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le=500,
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description="Maximum number of facts to store",
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)
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fact_confidence_threshold: float = Field(
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default=0.7,
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ge=0.0,
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le=1.0,
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description="Minimum confidence threshold for storing facts",
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)
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injection_enabled: bool = Field(
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default=True,
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description="Whether to inject memory into system prompt",
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)
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max_injection_tokens: int = Field(
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default=2000,
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ge=100,
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le=8000,
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description="Maximum tokens to use for memory injection",
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)
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