mirror of
https://github.com/bytedance/deer-flow.git
synced 2026-04-25 19:28:23 +00:00
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
92 lines
3.0 KiB
Python
92 lines
3.0 KiB
Python
from unittest.mock import MagicMock, patch
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from deerflow.agents.memory.queue import ConversationContext, MemoryUpdateQueue
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from deerflow.config.memory_config import MemoryConfig
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def _memory_config(**overrides: object) -> MemoryConfig:
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config = MemoryConfig()
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for key, value in overrides.items():
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setattr(config, key, value)
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return config
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def test_queue_add_preserves_existing_correction_flag_for_same_thread() -> None:
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queue = MemoryUpdateQueue()
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with (
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patch("deerflow.agents.memory.queue.get_memory_config", return_value=_memory_config(enabled=True)),
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patch.object(queue, "_reset_timer"),
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):
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queue.add(thread_id="thread-1", messages=["first"], correction_detected=True)
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queue.add(thread_id="thread-1", messages=["second"], correction_detected=False)
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assert len(queue._queue) == 1
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assert queue._queue[0].messages == ["second"]
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assert queue._queue[0].correction_detected is True
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def test_process_queue_forwards_correction_flag_to_updater() -> None:
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queue = MemoryUpdateQueue()
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queue._queue = [
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ConversationContext(
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thread_id="thread-1",
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messages=["conversation"],
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agent_name="lead_agent",
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correction_detected=True,
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)
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]
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mock_updater = MagicMock()
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mock_updater.update_memory.return_value = True
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with patch("deerflow.agents.memory.updater.MemoryUpdater", return_value=mock_updater):
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queue._process_queue()
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mock_updater.update_memory.assert_called_once_with(
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messages=["conversation"],
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thread_id="thread-1",
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agent_name="lead_agent",
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correction_detected=True,
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reinforcement_detected=False,
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)
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def test_queue_add_preserves_existing_reinforcement_flag_for_same_thread() -> None:
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queue = MemoryUpdateQueue()
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with (
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patch("deerflow.agents.memory.queue.get_memory_config", return_value=_memory_config(enabled=True)),
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patch.object(queue, "_reset_timer"),
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):
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queue.add(thread_id="thread-1", messages=["first"], reinforcement_detected=True)
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queue.add(thread_id="thread-1", messages=["second"], reinforcement_detected=False)
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assert len(queue._queue) == 1
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assert queue._queue[0].messages == ["second"]
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assert queue._queue[0].reinforcement_detected is True
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def test_process_queue_forwards_reinforcement_flag_to_updater() -> None:
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queue = MemoryUpdateQueue()
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queue._queue = [
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ConversationContext(
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thread_id="thread-1",
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messages=["conversation"],
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agent_name="lead_agent",
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reinforcement_detected=True,
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)
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]
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mock_updater = MagicMock()
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mock_updater.update_memory.return_value = True
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with patch("deerflow.agents.memory.updater.MemoryUpdater", return_value=mock_updater):
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queue._process_queue()
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mock_updater.update_memory.assert_called_once_with(
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messages=["conversation"],
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thread_id="thread-1",
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agent_name="lead_agent",
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correction_detected=False,
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reinforcement_detected=True,
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)
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