mirror of
https://github.com/bytedance/deer-flow.git
synced 2026-05-24 17:06:00 +00:00
greatmengqi
27b66d6753
Introduce an always-on auth layer with auto-created admin on first boot, multi-tenant isolation for threads/stores, and a full setup/login flow. Backend - JWT access tokens with `ver` field for stale-token rejection; bump on password/email change - Password hashing, HttpOnly+Secure cookies (Secure derived from request scheme at runtime) - CSRF middleware covering both REST and LangGraph routes - IP-based login rate limiting (5 attempts / 5-min lockout) with bounded dict growth and X-Forwarded-For bypass fix - Multi-worker-safe admin auto-creation (single DB write, WAL once) - needs_setup + token_version on User model; SQLite schema migration - Thread/store isolation by owner; orphan thread migration on first admin registration - thread_id validated as UUID to prevent log injection - CLI tool to reset admin password - Decorator-based authz module extracted from auth core Frontend - Login and setup pages with SSR guard for needs_setup flow - Account settings page (change password / email) - AuthProvider + route guards; skips redirect when no users registered - i18n (en-US / zh-CN) for auth surfaces - Typed auth API client; parseAuthError unwraps FastAPI detail envelope Infra & tooling - Unified `serve.sh` with gateway mode + auto dep install - Public PyPI uv.toml pin for CI compatibility - Regenerated uv.lock with public index Tests - HTTP vs HTTPS cookie security tests - Auth middleware, rate limiter, CSRF, setup flow coverage
55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
from pathlib import Path
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from pydantic import BaseModel, Field
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def _default_repo_root() -> Path:
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"""Resolve the repo root without relying on the current working directory."""
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return Path(__file__).resolve().parents[5]
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class SkillsConfig(BaseModel):
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"""Configuration for skills system"""
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path: str | None = Field(
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default=None,
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description="Path to skills directory. If not specified, defaults to ../skills relative to backend directory",
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)
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container_path: str = Field(
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default="/mnt/skills",
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description="Path where skills are mounted in the sandbox container",
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)
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def get_skills_path(self) -> Path:
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"""
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Get the resolved skills directory path.
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Returns:
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Path to the skills directory
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"""
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if self.path:
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# Use configured path (can be absolute or relative)
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path = Path(self.path)
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if not path.is_absolute():
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# If relative, resolve from the repo root for deterministic behavior.
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path = _default_repo_root() / path
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return path.resolve()
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else:
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# Default: ../skills relative to backend directory
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from deerflow.skills.loader import get_skills_root_path
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return get_skills_root_path()
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def get_skill_container_path(self, skill_name: str, category: str = "public") -> str:
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"""
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Get the full container path for a specific skill.
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Args:
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skill_name: Name of the skill (directory name)
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category: Category of the skill (public or custom)
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Returns:
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Full path to the skill in the container
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"""
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return f"{self.container_path}/{category}/{skill_name}"
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