696 lines
29 KiB
Python
696 lines
29 KiB
Python
"""Centralized accessors for singleton objects stored on ``app.state``.
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**Getters** (used by routers): raise 503 when a required dependency is
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missing, except ``get_store`` which returns ``None``.
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Initialization is handled directly in ``app.py`` via :class:`AsyncExitStack`.
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"""
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from __future__ import annotations
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import asyncio
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import logging
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from collections.abc import AsyncGenerator, Callable
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from contextlib import AsyncExitStack, asynccontextmanager
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from typing import TYPE_CHECKING, TypeVar, cast
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from fastapi import FastAPI, HTTPException, Request
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from langgraph.types import Checkpointer
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from deerflow.config.app_config import AppConfig
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from deerflow.persistence.feedback import FeedbackRepository
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from deerflow.runtime import RunContext, RunManager, StreamBridge
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from deerflow.runtime.events.store.base import RunEventStore
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from deerflow.runtime.runs.store.base import RunStore
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logger = logging.getLogger(__name__)
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if TYPE_CHECKING:
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from app.gateway.auth.local_provider import LocalAuthProvider
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from app.gateway.auth.repositories.sqlite import SQLiteUserRepository
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from deerflow.persistence.agents import AgentStore
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from deerflow.persistence.notifications import NotificationStore
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from deerflow.persistence.scheduled_tasks import ScheduledTaskStore
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from deerflow.persistence.skills import SkillStore
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from deerflow.persistence.tags import TagStore
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from deerflow.persistence.thread_meta.base import ThreadMetaStore
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T = TypeVar("T")
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def resolve_roundtable_gateway_mode(raw: str | None) -> str:
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"""解析 ``ROUNDTABLE_JOB_GATEWAY``：**默认 ``inprocess``（真模型后台挂起）**。
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只有显式写 ``simulated`` 才用模拟网关（前端联调用：真实进度、不调真模型）；
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缺省 / 空串 / 其它值一律落到 ``inprocess`` —— 部署时无需在 .env 里配置任何值，
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后台挂起开箱即用（Phase 3b 已在 Windows 本地与内网 Docker 验证）。
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"""
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mode = (raw or "").strip().lower()
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return "simulated" if mode == "simulated" else "inprocess"
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def get_config(request: Request) -> AppConfig:
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"""Return the app-scoped ``AppConfig`` stored on ``app.state``."""
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config = getattr(request.app.state, "config", None)
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if config is None:
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raise HTTPException(status_code=503, detail="Configuration not available")
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return config
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@asynccontextmanager
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async def langgraph_runtime(app: FastAPI) -> AsyncGenerator[None, None]:
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"""Bootstrap and tear down all LangGraph runtime singletons.
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Usage in ``app.py``::
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async with langgraph_runtime(app):
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yield
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"""
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from deerflow.persistence.engine import close_engine, get_session_factory, init_engine_from_config
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from deerflow.runtime import make_store, make_stream_bridge
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from deerflow.runtime.checkpointer.async_provider import make_checkpointer
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from deerflow.runtime.events.store import make_run_event_store
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async with AsyncExitStack() as stack:
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config = getattr(app.state, "config", None)
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if config is None:
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raise RuntimeError("langgraph_runtime() requires app.state.config to be initialized")
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app.state.stream_bridge = await stack.enter_async_context(make_stream_bridge(config))
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# Initialize persistence engine BEFORE checkpointer so that
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# auto-create-database logic runs first (postgres backend).
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await init_engine_from_config(config.database)
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app.state.checkpointer = await stack.enter_async_context(make_checkpointer(config))
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app.state.store = await stack.enter_async_context(make_store(config))
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# Initialize repositories — one get_session_factory() call for all.
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sf = get_session_factory()
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if sf is not None:
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from deerflow.persistence.feedback import FeedbackRepository
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from deerflow.persistence.run import RunRepository
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app.state.run_store = RunRepository(sf)
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app.state.feedback_repo = FeedbackRepository(sf)
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else:
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from deerflow.runtime.runs.store.memory import MemoryRunStore
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app.state.run_store = MemoryRunStore()
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app.state.feedback_repo = None
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from deerflow.persistence.thread_meta import make_thread_store
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app.state.thread_store = make_thread_store(sf, app.state.store)
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from deerflow.persistence.scheduled_tasks import make_scheduled_task_store
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app.state.scheduled_task_store = make_scheduled_task_store(sf)
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from deerflow.persistence.html_pages import make_html_page_store
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app.state.html_page_store = make_html_page_store(sf)
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from deerflow.persistence.llm_metrics import make_llm_metrics_store
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app.state.llm_metrics_store = make_llm_metrics_store(sf)
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from deerflow.persistence.concurrency import make_concurrency_sample_store
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app.state.concurrency_sample_store = make_concurrency_sample_store(sf)
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from deerflow.persistence.tool_metrics import make_tool_metrics_store
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app.state.tool_metrics_store = make_tool_metrics_store(sf)
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from deerflow.persistence.agents import make_agent_store
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app.state.agent_store = make_agent_store(sf)
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from deerflow.persistence.skills import make_skill_store
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app.state.skill_store = make_skill_store(sf)
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from deerflow.persistence.notifications import (
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make_notification_store,
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set_notification_store,
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)
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app.state.notification_store = make_notification_store(sf)
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# Make the sender helper (harness layer) able to write without going
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# through FastAPI request context — used by cascade.py.
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set_notification_store(app.state.notification_store)
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from deerflow.persistence.recommended_questions import make_recommended_question_store
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app.state.recommended_question_store = make_recommended_question_store(sf)
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from deerflow.persistence.tags import make_tag_store
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app.state.tag_store = make_tag_store(sf)
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from deerflow.persistence.positions import make_position_store
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app.state.position_store = make_position_store(sf)
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from deerflow.services.position_sync import PositionSyncService
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app.state.position_sync = PositionSyncService(sf, app.state.position_store, app.state.tag_store)
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from deerflow.persistence.thread_shares import make_thread_share_store
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app.state.thread_share_store = make_thread_share_store(sf)
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from deerflow.persistence.embed_sessions import make_embed_session_store
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app.state.embed_session_store = make_embed_session_store(sf)
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from deerflow.persistence.ai_writing_sessions import (
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make_ai_writing_session_store,
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)
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from deerflow.persistence.ai_writing_sessions import (
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register_default_store as _register_ai_writing_default_store,
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)
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app.state.ai_writing_session_store = make_ai_writing_session_store(sf)
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# 让 LangGraph Server 路径下的 AI 写作节点能跨 app/harness 边界拿到 repo。
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# 老 /start /stream /resume 路由仍走 request.app.state，互不影响。
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_register_ai_writing_default_store(app.state.ai_writing_session_store)
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# AI 写作「后台挂起」执行器：把写作图作为后台任务驱动到定稿（离页/刷新/切号不打断）。
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# 用 app.state.checkpointer（与运行时 worker 同一份 saver）续上线程 checkpoint。
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from app.gateway.ai_writing_job_executor import AIWritingJobExecutor
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app.state.ai_writing_job_executor = AIWritingJobExecutor(app)
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# 进程重启续跑：把仍标记为 background 的会话重新拉起驱动（best-effort，不阻塞启动）。
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if app.state.ai_writing_session_store is not None:
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from app.gateway.ai_writing_job_executor import STATUS_BACKGROUND
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async def _reconcile_ai_writing_background() -> None:
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try:
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sessions = await app.state.ai_writing_session_store.list_by_status(STATUS_BACKGROUND)
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app.state.ai_writing_job_executor.reconcile(sessions=sessions)
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except Exception: # noqa: BLE001
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logger.warning("[ai-writing-bg] startup reconcile failed", exc_info=True)
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asyncio.create_task(_reconcile_ai_writing_background())
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from deerflow.persistence.article_types import make_article_type_store
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app.state.article_type_store = make_article_type_store(sf)
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from deerflow.persistence.research_templates import make_research_template_store
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app.state.research_template_store = make_research_template_store(sf)
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from deerflow.persistence.memory_config import make_user_memory_config_store
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app.state.memory_config_store = make_user_memory_config_store(sf)
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from deerflow.knowledge import make_knowledge_service
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app.state.knowledge_service = make_knowledge_service(sf, config.knowledge)
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# Seed the built-in extraction prompts as editable templates so operators
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# can immediately view/edit them (idempotent; never overwrites edits).
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if app.state.knowledge_service is not None:
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try:
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await app.state.knowledge_service.seed_default_templates()
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except Exception:
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logger.exception("Failed to seed knowledge extraction templates")
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# Background auto-sedimentation queue (phase 3) — drains finished
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# conversations off the chat path. Worker only does work when
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# ``knowledge.auto_ingest_enabled`` is set; the queue is always wired so
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# the middleware can enqueue without a hard dependency.
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from deerflow.knowledge.auto_ingest import AutoIngestQueue, set_auto_ingest_queue
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if app.state.knowledge_service is not None:
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_auto_ingest_queue = AutoIngestQueue(
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lambda: getattr(app.state, "knowledge_service", None),
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min_chars=config.knowledge.auto_ingest_min_chars,
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)
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_auto_ingest_queue.start()
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set_auto_ingest_queue(_auto_ingest_queue)
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app.state.knowledge_auto_ingest_queue = _auto_ingest_queue
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from deerflow.persistence.user_prompts import make_user_prompt_store
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app.state.user_prompt_store = make_user_prompt_store(sf)
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from deerflow.persistence.user_preferences import make_user_preferences_store
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app.state.user_preferences_store = make_user_preferences_store(sf)
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from deerflow.persistence.roundtable_drafts import make_roundtable_draft_store
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app.state.roundtable_draft_store = make_roundtable_draft_store(sf)
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# Task-scoped roundtable drafts (taskId 深链聊天记录，独立存、不按 user 分权)。
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from deerflow.persistence.roundtable_task_drafts import make_roundtable_task_draft_store
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app.state.roundtable_task_draft_store = make_roundtable_task_draft_store(sf)
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from deerflow.persistence.roundtable_draft_shares import make_roundtable_draft_share_store
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app.state.roundtable_draft_share_store = make_roundtable_draft_share_store(sf)
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from deerflow.persistence.roundtable_chains import make_roundtable_chain_store
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app.state.roundtable_chain_store = make_roundtable_chain_store(sf)
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from deerflow.persistence.business_mapping import make_business_mapping_store
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app.state.business_mapping_store = make_business_mapping_store(sf)
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from deerflow.persistence.light_apps import make_light_app_store
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app.state.light_app_store = make_light_app_store(sf)
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from deerflow.persistence.menu_overrides import make_menu_override_store
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app.state.menu_override_store = make_menu_override_store(sf)
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from deerflow.persistence.sensitive_words import make_sensitive_word_store
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app.state.sensitive_word_store = make_sensitive_word_store(sf)
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from deerflow.persistence.dashboard_sessions import make_dashboard_session_store
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app.state.dashboard_session_store = make_dashboard_session_store(sf)
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from deerflow.persistence.task_buttons import make_task_button_store
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app.state.task_button_store = make_task_button_store(sf)
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from deerflow.persistence.taskcop_tasks import make_taskcop_task_store
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app.state.taskcop_task_store = make_taskcop_task_store(sf)
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from deerflow.persistence.task_reports import make_task_report_store
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app.state.task_report_store = make_task_report_store(sf)
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from deerflow.persistence.parallel_agent_tasks import make_parallel_agent_task_store
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app.state.parallel_agent_task_store = make_parallel_agent_task_store(sf)
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from deerflow.persistence.roundtable_jobs import (
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make_roundtable_job_store,
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)
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from deerflow.persistence.roundtable_jobs import (
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register_default_store as _register_roundtable_job_default_store,
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)
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app.state.roundtable_job_store = make_roundtable_job_store(sf)
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# 让 LangGraph Server 路径下的圆桌编排图节点能跨 app/harness 边界拿到 repo
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# （Phase 2 后台编排图写进度用）。
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_register_roundtable_job_default_store(app.state.roundtable_job_store)
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# 圆桌会商「诊断日志」store：会商全流程各处报错 / 关键事件落库，供管理员诊断页筛选。
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# 同样登记进程级默认仓储——harness 侧编排引擎 / 进程内网关跨边界写诊断用。
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from deerflow.persistence.roundtable_diagnostics import (
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make_roundtable_diagnostic_store,
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)
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from deerflow.persistence.roundtable_diagnostics import (
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register_default_store as _register_roundtable_diagnostic_default_store,
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)
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app.state.roundtable_diagnostic_store = make_roundtable_diagnostic_store(sf)
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_register_roundtable_diagnostic_default_store(app.state.roundtable_diagnostic_store)
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# 圆桌后台作业执行器：把编排引擎作为后台任务跑。
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# 网关由环境变量 ``ROUNDTABLE_JOB_GATEWAY`` 选择（见 resolve_roundtable_gateway_mode）：
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# - 缺省 / "inprocess"（Phase 3b）：进程内驱动真模型（leader/seat/report）——**默认**，
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# 部署时无需任何配置即可用后台挂起；
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# - 显式 "simulated"（Phase 3a）：模拟网关，产生真实进度但不调真模型，供前端联调。
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if app.state.roundtable_job_store is not None:
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import os
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from app.gateway.roundtable_job_executor import RoundtableJobExecutor
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gateway_mode = resolve_roundtable_gateway_mode(os.getenv("ROUNDTABLE_JOB_GATEWAY"))
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if gateway_mode == "simulated":
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from deerflow.agents.roundtable_orchestrator import SimulatedRoundtableGateway
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# delay=3s/步：让模拟后台任务有可观察的进行中时长（切页/重连仍在跑）。
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gateway_factory = lambda p: SimulatedRoundtableGateway( # noqa: E731
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p.agents, mode=p.mode, delay=3.0
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)
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logger.info("[roundtable-jobs] gateway = simulated")
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else:
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from app.gateway.roundtable_inprocess_gateway import InProcessRoundtableGateway
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_app = app # 闭包捕获 app，供网关访问 app.state（checkpointer/thread_store）
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gateway_factory = lambda p: InProcessRoundtableGateway( # noqa: E731
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_app,
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user_id=p.user_id,
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agents=p.agents,
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model=p.model,
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# 席位执行模式沿用 Step2 选择，后台席位 run 据此覆盖 policy 的 thinking/reasoning + 开子代理。
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seat_thinking_enabled=p.seat_thinking_enabled,
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seat_reasoning_effort=p.seat_reasoning_effort,
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seat_subagent_enabled=p.seat_subagent_enabled,
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seat_skill_directives=p.seat_skill_directives,
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# 每席位推理深度覆盖（业务链条里各席位单独配）：run_seat 命中即覆盖作业级 seatMode。
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seat_modes=p.seat_modes,
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# 诊断日志上下文：网关每轮 leader/seat/report/summary/dashboard 报错均带这些归属。
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job_id=p.job_id,
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draft_id=p.draft_id,
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task_id=p.task_id,
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)
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logger.info("[roundtable-jobs] gateway = inprocess (real models, default)")
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app.state.roundtable_job_executor = RoundtableJobExecutor(
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app.state.roundtable_job_store,
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gateway_factory=gateway_factory,
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draft_store=getattr(app.state, "roundtable_draft_store", None),
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# task 作业把最终报告写回**独立的** task-draft 存储（不分权）。
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task_draft_store=getattr(app.state, "roundtable_task_draft_store", None),
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)
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else:
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app.state.roundtable_job_executor = None
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# Run event store (has its own factory with config-driven backend selection)
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run_events_config = getattr(config, "run_events", None)
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app.state.run_event_store = make_run_event_store(run_events_config)
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# RunManager with store backing for persistence
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app.state.run_manager = RunManager(store=app.state.run_store)
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try:
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yield
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finally:
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from deerflow.persistence.notifications import set_notification_store
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set_notification_store(None)
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queue = getattr(app.state, "knowledge_auto_ingest_queue", None)
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if queue is not None:
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from deerflow.knowledge.auto_ingest import set_auto_ingest_queue
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await queue.stop()
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set_auto_ingest_queue(None)
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await close_engine()
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# ---------------------------------------------------------------------------
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# Getters – called by routers per-request
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# ---------------------------------------------------------------------------
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def _require(attr: str, label: str) -> Callable[[Request], T]:
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"""Create a FastAPI dependency that returns ``app.state.<attr>`` or 503."""
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def dep(request: Request) -> T:
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val = getattr(request.app.state, attr, None)
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if val is None:
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raise HTTPException(status_code=503, detail=f"{label} not available")
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return cast(T, val)
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dep.__name__ = dep.__qualname__ = f"get_{attr}"
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return dep
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get_stream_bridge: Callable[[Request], StreamBridge] = _require("stream_bridge", "Stream bridge")
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get_run_manager: Callable[[Request], RunManager] = _require("run_manager", "Run manager")
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get_checkpointer: Callable[[Request], Checkpointer] = _require("checkpointer", "Checkpointer")
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get_run_event_store: Callable[[Request], RunEventStore] = _require("run_event_store", "Run event store")
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get_feedback_repo: Callable[[Request], FeedbackRepository] = _require("feedback_repo", "Feedback")
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get_run_store: Callable[[Request], RunStore] = _require("run_store", "Run store")
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def get_store(request: Request):
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"""Return the global store (may be ``None`` if not configured)."""
|
||
return getattr(request.app.state, "store", None)
|
||
|
||
|
||
def get_thread_store(request: Request) -> ThreadMetaStore:
|
||
"""Return the thread metadata store (SQL or memory-backed)."""
|
||
val = getattr(request.app.state, "thread_store", None)
|
||
if val is None:
|
||
raise HTTPException(status_code=503, detail="Thread metadata store not available")
|
||
return val
|
||
|
||
|
||
def get_scheduled_task_store(request: Request) -> ScheduledTaskStore:
|
||
"""Return the scheduled-task store."""
|
||
val = getattr(request.app.state, "scheduled_task_store", None)
|
||
if val is None:
|
||
raise HTTPException(status_code=503, detail="Scheduled task store not available")
|
||
return val
|
||
|
||
|
||
def get_llm_metrics_store(request: Request):
|
||
"""Return the LLM metrics store, or None when not configured."""
|
||
return getattr(request.app.state, "llm_metrics_store", None)
|
||
|
||
|
||
def get_concurrency_sample_store(request: Request):
|
||
"""Return the concurrency-sample store, or None on the memory backend."""
|
||
return getattr(request.app.state, "concurrency_sample_store", None)
|
||
|
||
|
||
def get_tool_metrics_store(request: Request):
|
||
"""Return the tool-call metrics store, or None when not configured."""
|
||
return getattr(request.app.state, "tool_metrics_store", None)
|
||
|
||
|
||
def get_agent_store(request: Request) -> AgentStore:
|
||
"""Return the custom-agent store."""
|
||
val = getattr(request.app.state, "agent_store", None)
|
||
if val is None:
|
||
raise HTTPException(status_code=503, detail="Agent store not available")
|
||
return val
|
||
|
||
|
||
def get_skill_store(request: Request) -> SkillStore:
|
||
"""Return the custom-skill store."""
|
||
val = getattr(request.app.state, "skill_store", None)
|
||
if val is None:
|
||
raise HTTPException(status_code=503, detail="Skill store not available")
|
||
return val
|
||
|
||
|
||
def get_notification_store(request: Request) -> NotificationStore:
|
||
"""Return the in-app notification store."""
|
||
val = getattr(request.app.state, "notification_store", None)
|
||
if val is None:
|
||
raise HTTPException(status_code=503, detail="Notification store not available")
|
||
return val
|
||
|
||
|
||
def get_tag_store(request: Request) -> TagStore:
|
||
"""Return the tag store."""
|
||
val = getattr(request.app.state, "tag_store", None)
|
||
if val is None:
|
||
raise HTTPException(status_code=503, detail="Tag store not available")
|
||
return val
|
||
|
||
|
||
def get_position_store(request: Request):
|
||
"""Return the 岗位 (position) store."""
|
||
val = getattr(request.app.state, "position_store", None)
|
||
if val is None:
|
||
raise HTTPException(status_code=503, detail="Position store not available")
|
||
return val
|
||
|
||
|
||
def get_position_sync(request: Request):
|
||
"""Return the position → subscription sync service."""
|
||
val = getattr(request.app.state, "position_sync", None)
|
||
if val is None:
|
||
raise HTTPException(status_code=503, detail="Position sync service not available")
|
||
return val
|
||
|
||
|
||
def get_thread_share_store(request: Request):
|
||
"""Return the conversation share-link store."""
|
||
val = getattr(request.app.state, "thread_share_store", None)
|
||
if val is None:
|
||
raise HTTPException(status_code=503, detail="Thread share store not available")
|
||
return val
|
||
|
||
|
||
def get_roundtable_draft_share_store(request: Request):
|
||
"""Return the roundtable-draft share-link store."""
|
||
val = getattr(request.app.state, "roundtable_draft_share_store", None)
|
||
if val is None:
|
||
raise HTTPException(status_code=503, detail="Roundtable draft share store not available")
|
||
return val
|
||
|
||
|
||
def get_embed_session_store(request: Request):
|
||
"""Return the iframe embed session → thread mapping store."""
|
||
val = getattr(request.app.state, "embed_session_store", None)
|
||
if val is None:
|
||
raise HTTPException(status_code=503, detail="Embed session store not available")
|
||
return val
|
||
|
||
|
||
def get_ai_writing_session_store(request: Request):
|
||
"""Return the AI writing session store, or None when not configured."""
|
||
return getattr(request.app.state, "ai_writing_session_store", None)
|
||
|
||
|
||
def get_ai_writing_job_executor(request: Request):
|
||
"""Return the AI writing background-suspend executor, or None when unavailable."""
|
||
return getattr(request.app.state, "ai_writing_job_executor", None)
|
||
|
||
|
||
def get_article_type_store(request: Request):
|
||
"""Return the article type store, or None when not configured."""
|
||
return getattr(request.app.state, "article_type_store", None)
|
||
|
||
|
||
def get_research_template_store(request: Request):
|
||
"""Return the research template store, or None when not configured."""
|
||
return getattr(request.app.state, "research_template_store", None)
|
||
|
||
|
||
def get_memory_config_store(request: Request):
|
||
"""Return the per-user memory config store."""
|
||
val = getattr(request.app.state, "memory_config_store", None)
|
||
if val is None:
|
||
raise HTTPException(status_code=503, detail="Memory config store not available")
|
||
return val
|
||
|
||
|
||
def get_knowledge_service(request: Request):
|
||
"""Return the knowledge base service (503 when disabled / memory backend)."""
|
||
val = getattr(request.app.state, "knowledge_service", None)
|
||
if val is None:
|
||
raise HTTPException(status_code=503, detail="Knowledge base not available")
|
||
return val
|
||
|
||
|
||
def get_run_context(request: Request) -> RunContext:
|
||
"""Build a :class:`RunContext` from ``app.state`` singletons.
|
||
|
||
Returns a *base* context with infrastructure dependencies.
|
||
"""
|
||
config = get_config(request)
|
||
return RunContext(
|
||
checkpointer=get_checkpointer(request),
|
||
store=get_store(request),
|
||
event_store=get_run_event_store(request),
|
||
run_events_config=getattr(config, "run_events", None),
|
||
thread_store=get_thread_store(request),
|
||
app_config=config,
|
||
)
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Auth helpers (used by authz.py and auth middleware)
|
||
# ---------------------------------------------------------------------------
|
||
|
||
# Cached singletons to avoid repeated instantiation per request
|
||
_cached_local_provider: LocalAuthProvider | None = None
|
||
_cached_repo: SQLiteUserRepository | None = None
|
||
|
||
|
||
def get_request_access_token(request: Request) -> str | None:
|
||
"""Extract access token from cookie or Authorization header.
|
||
|
||
Priority:
|
||
1. ``access_token`` cookie (browser session mode)
|
||
2. ``Authorization: Bearer <token>`` header (API client mode)
|
||
"""
|
||
cookie_token = request.cookies.get("access_token")
|
||
if cookie_token:
|
||
return cookie_token
|
||
|
||
authorization = request.headers.get("authorization", "").strip()
|
||
if not authorization:
|
||
return None
|
||
|
||
scheme, _, token = authorization.partition(" ")
|
||
if scheme.lower() != "bearer":
|
||
return None
|
||
|
||
token = token.strip()
|
||
return token or None
|
||
|
||
|
||
def get_local_provider() -> LocalAuthProvider:
|
||
"""Get or create the cached LocalAuthProvider singleton.
|
||
|
||
Must be called after ``init_engine_from_config()`` — the shared
|
||
session factory is required to construct the user repository.
|
||
"""
|
||
global _cached_local_provider, _cached_repo
|
||
if _cached_repo is None:
|
||
from app.gateway.auth.repositories.sqlite import SQLiteUserRepository
|
||
from deerflow.persistence.engine import get_session_factory
|
||
|
||
sf = get_session_factory()
|
||
if sf is None:
|
||
raise RuntimeError("get_local_provider() called before init_engine_from_config(); cannot access users table")
|
||
_cached_repo = SQLiteUserRepository(sf)
|
||
if _cached_local_provider is None:
|
||
from app.gateway.auth.local_provider import LocalAuthProvider
|
||
|
||
_cached_local_provider = LocalAuthProvider(repository=_cached_repo)
|
||
return _cached_local_provider
|
||
|
||
|
||
async def get_current_user_from_request(request: Request):
|
||
"""Get the current authenticated user from request token.
|
||
|
||
Raises HTTPException 401 if not authenticated.
|
||
"""
|
||
from app.gateway.auth import decode_token
|
||
from app.gateway.auth.errors import AuthErrorCode, AuthErrorResponse, TokenError, token_error_to_code
|
||
|
||
access_token = get_request_access_token(request)
|
||
if not access_token:
|
||
raise HTTPException(
|
||
status_code=401,
|
||
detail=AuthErrorResponse(code=AuthErrorCode.NOT_AUTHENTICATED, message="Not authenticated").model_dump(),
|
||
)
|
||
|
||
payload = decode_token(access_token)
|
||
if isinstance(payload, TokenError):
|
||
raise HTTPException(
|
||
status_code=401,
|
||
detail=AuthErrorResponse(code=token_error_to_code(payload), message=f"Token error: {payload.value}").model_dump(),
|
||
)
|
||
|
||
provider = get_local_provider()
|
||
user = await provider.get_user(payload.sub)
|
||
if user is None:
|
||
raise HTTPException(
|
||
status_code=401,
|
||
detail=AuthErrorResponse(code=AuthErrorCode.USER_NOT_FOUND, message="User not found").model_dump(),
|
||
)
|
||
|
||
# Token version mismatch → password was changed, token is stale
|
||
if user.token_version != payload.ver:
|
||
raise HTTPException(
|
||
status_code=401,
|
||
detail=AuthErrorResponse(code=AuthErrorCode.TOKEN_INVALID, message="Token revoked (password changed)").model_dump(),
|
||
)
|
||
|
||
return user
|
||
|
||
|
||
async def get_optional_user_from_request(request: Request):
|
||
"""Get optional authenticated user from request.
|
||
|
||
Returns None if not authenticated.
|
||
"""
|
||
try:
|
||
return await get_current_user_from_request(request)
|
||
except HTTPException:
|
||
return None
|
||
|
||
|
||
async def get_current_user(request: Request) -> str | None:
|
||
"""Extract user_id from request cookie, or None if not authenticated.
|
||
|
||
Thin adapter that returns the string id for callers that only need
|
||
identification (e.g., ``feedback.py``). Full-user callers should use
|
||
``get_current_user_from_request`` or ``get_optional_user_from_request``.
|
||
"""
|
||
user = await get_optional_user_from_request(request)
|
||
return str(user.id) if user else None
|