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
60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
"""Per-invocation context for DeerFlow agent execution.
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Injected via LangGraph Runtime. Middleware and tools access this
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via Runtime[DeerFlowContext] parameters, through resolve_context().
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Any
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@dataclass(frozen=True)
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class DeerFlowContext:
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"""Typed, immutable, per-invocation context injected via LangGraph Runtime.
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Fields are all known at run start and never change during execution.
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Mutable runtime state (e.g. sandbox_id) flows through ThreadState, not here.
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"""
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app_config: Any # AppConfig — typed as Any to avoid circular import at module level
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thread_id: str
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agent_name: str | None = None
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def resolve_context(runtime: Any) -> DeerFlowContext:
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"""Extract or construct DeerFlowContext from runtime.
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Gateway/Client paths: runtime.context is already DeerFlowContext → return directly.
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LangGraph Server / legacy dict path: construct from dict context or configurable fallback.
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"""
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ctx = getattr(runtime, "context", None)
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if isinstance(ctx, DeerFlowContext):
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return ctx
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from deerflow.config.app_config import AppConfig
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# Try dict context first (legacy path, tests), then configurable
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if isinstance(ctx, dict):
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return DeerFlowContext(
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app_config=AppConfig.current(),
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thread_id=ctx.get("thread_id", ""),
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agent_name=ctx.get("agent_name"),
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)
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# No context at all — fall back to LangGraph configurable
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try:
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from langgraph.config import get_config
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cfg = get_config().get("configurable", {})
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except RuntimeError:
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# Outside runnable context (e.g. unit tests)
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cfg = {}
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return DeerFlowContext(
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app_config=AppConfig.current(),
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thread_id=cfg.get("thread_id", ""),
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agent_name=cfg.get("agent_name"),
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)
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