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
27 lines
906 B
Python
27 lines
906 B
Python
"""Configuration for stream bridge."""
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from typing import Literal
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from pydantic import BaseModel, ConfigDict, Field
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StreamBridgeType = Literal["memory", "redis"]
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class StreamBridgeConfig(BaseModel):
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"""Configuration for the stream bridge that connects agent workers to SSE endpoints."""
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model_config = ConfigDict(frozen=True)
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type: StreamBridgeType = Field(
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default="memory",
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description="Stream bridge backend type. 'memory' uses in-process asyncio.Queue (single-process only). 'redis' uses Redis Streams (planned for Phase 2, not yet implemented).",
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)
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redis_url: str | None = Field(
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default=None,
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description="Redis URL for the redis stream bridge type. Example: 'redis://localhost:6379/0'.",
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)
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queue_maxsize: int = Field(
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default=256,
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description="Maximum number of events buffered per run in the memory bridge.",
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)
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