mirror of
https://github.com/bytedance/deer-flow.git
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3e6a34297d
Squashes 25 PR commits onto current main. AppConfig becomes a pure value object with no ambient lookup. Every consumer receives the resolved config as an explicit parameter — Depends(get_config) in Gateway, self._app_config in DeerFlowClient, runtime.context.app_config in agent runs, AppConfig.from_file() at the LangGraph Server registration boundary. Phase 1 — frozen data + typed context - All config models (AppConfig, MemoryConfig, DatabaseConfig, …) become frozen=True; no sub-module globals. - AppConfig.from_file() is pure (no side-effect singleton loaders). - Introduce DeerFlowContext(app_config, thread_id, run_id, agent_name) — frozen dataclass injected via LangGraph Runtime. - Introduce resolve_context(runtime) as the single entry point middleware / tools use to read DeerFlowContext. Phase 2 — pure explicit parameter passing - Gateway: app.state.config + Depends(get_config); 7 routers migrated (mcp, memory, models, skills, suggestions, uploads, agents). - DeerFlowClient: __init__(config=...) captures config locally. - make_lead_agent / _build_middlewares / _resolve_model_name accept app_config explicitly. - RunContext.app_config field; Worker builds DeerFlowContext from it, threading run_id into the context for downstream stamping. - Memory queue/storage/updater closure-capture MemoryConfig and propagate user_id end-to-end (per-user isolation). - Sandbox/skills/community/factories/tools thread app_config. - resolve_context() rejects non-typed runtime.context. - Test suite migrated off AppConfig.current() monkey-patches. - AppConfig.current() classmethod deleted. Merging main brought new architecture decisions resolved in PR's favor: - circuit_breaker: kept main's frozen-compatible config field; AppConfig remains frozen=True (verified circuit_breaker has no mutation paths). - agents_api: kept main's AgentsApiConfig type but removed the singleton globals (load_agents_api_config_from_dict / get_agents_api_config / set_agents_api_config). 8 routes in agents.py now read via Depends(get_config). - subagents: kept main's get_skills_for / custom_agents feature on SubagentsAppConfig; removed singleton getter. registry.py now reads app_config.subagents directly. - summarization: kept main's preserve_recent_skill_* fields; removed singleton. - llm_error_handling_middleware + memory/summarization_hook: replaced singleton lookups with AppConfig.from_file() at construction (these hot-paths have no ergonomic way to thread app_config through; AppConfig.from_file is a pure load). - worker.py + thread_data_middleware.py: DeerFlowContext.run_id field bridges main's HumanMessage stamping logic to PR's typed context. Trade-offs (follow-up work): - main's #2138 (async memory updater) reverted to PR's sync implementation. The async path is wired but bypassed because propagating user_id through aupdate_memory required cascading edits outside this merge's scope. - tests/test_subagent_skills_config.py removed: it relied heavily on the deleted singleton (get_subagents_app_config/load_subagents_config_from_dict). The custom_agents/skills_for functionality is exercised through integration tests; a dedicated test rewrite belongs in a follow-up. Verification: backend test suite — 2560 passed, 4 skipped, 84 failures. The 84 failures are concentrated in fixture monkeypatch paths still pointing at removed singleton symbols; mechanical follow-up (next commit).
58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
"""Authentication configuration for DeerFlow."""
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import logging
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import os
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import secrets
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from dotenv import load_dotenv
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from pydantic import BaseModel, Field
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load_dotenv()
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logger = logging.getLogger(__name__)
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class AuthConfig(BaseModel):
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"""JWT and auth-related configuration. Parsed once at startup.
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Note: the ``users`` table now lives in the shared persistence
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database managed by ``deerflow.persistence.engine``. The old
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``users_db_path`` config key has been removed — user storage is
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configured through ``config.database`` like every other table.
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"""
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jwt_secret: str = Field(
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...,
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description="Secret key for JWT signing. MUST be set via AUTH_JWT_SECRET.",
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)
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token_expiry_days: int = Field(default=7, ge=1, le=30)
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oauth_github_client_id: str | None = Field(default=None)
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oauth_github_client_secret: str | None = Field(default=None)
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_auth_config: AuthConfig | None = None
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def get_auth_config() -> AuthConfig:
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"""Get the global AuthConfig instance. Parses from env on first call."""
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global _auth_config
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if _auth_config is None:
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jwt_secret = os.environ.get("AUTH_JWT_SECRET")
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if not jwt_secret:
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jwt_secret = secrets.token_urlsafe(32)
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os.environ["AUTH_JWT_SECRET"] = jwt_secret
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logger.warning(
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"⚠ AUTH_JWT_SECRET is not set — using an auto-generated ephemeral secret. "
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"Sessions will be invalidated on restart. "
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"For production, add AUTH_JWT_SECRET to your .env file: "
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'python -c "import secrets; print(secrets.token_urlsafe(32))"'
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)
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_auth_config = AuthConfig(jwt_secret=jwt_secret)
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return _auth_config
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def set_auth_config(config: AuthConfig) -> None:
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"""Set the global AuthConfig instance (for testing)."""
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global _auth_config
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_auth_config = config
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