1056 lines
48 KiB
Python
1056 lines
48 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.fixed_questions import make_fixed_question_store
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app.state.fixed_question_store = make_fixed_question_store(sf)
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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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# 独立的岗位协同 Session / Node 存储。它只索引业务流程状态，真实消息和
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# 文件仍复用 LangGraph thread/checkpoint 与现有 artifact API。
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from deerflow.persistence.position_roundtable import make_position_roundtable_store
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app.state.position_roundtable_store = make_position_roundtable_store(sf)
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# 岗位会商工作视角（名称 / 顺序 / 特殊意图岗）是全局配置，和组织/RBAC
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# 岗位分开保存。启动只补齐缺失内置项，不覆盖管理员的既有修改。
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from app.gateway.position_role_defaults import POSITION_ROLE_SEED
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from deerflow.persistence.position_roles import make_position_role_store
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app.state.position_role_store = make_position_role_store(sf)
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await app.state.position_role_store.ensure_seed(POSITION_ROLE_SEED)
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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.workflows import make_workflow_store
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app.state.workflow_store = make_workflow_store(sf)
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from deerflow.persistence.workflow_data_sources import make_workflow_data_source_store
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from deerflow.persistence.workflow_events import make_workflow_event_store
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from deerflow.persistence.workflow_planning import make_workflow_planning_store
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from deerflow.persistence.workflow_runs import make_workflow_run_store
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app.state.workflow_run_store = make_workflow_run_store(sf)
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app.state.workflow_event_store = make_workflow_event_store(sf, run_store=app.state.workflow_run_store)
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app.state.workflow_data_source_store = make_workflow_data_source_store(sf)
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# Candidate plans are durable user-editable drafts, deliberately kept
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# separate from both the canvas draft and a formal workflow run.
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app.state.workflow_planning_store = make_workflow_planning_store(sf)
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from app.report_collaboration.execution.live_hub import PublishingReportCollaborationStore, ReportCollaborationLiveHub
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from deerflow.persistence.report_collaboration import make_report_collaboration_store
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app.state.report_collaboration_live_hub = ReportCollaborationLiveHub()
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app.state.report_collaboration_store = PublishingReportCollaborationStore(
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make_report_collaboration_store(sf),
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app.state.report_collaboration_live_hub,
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)
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app.state.report_collaboration_executor = None
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app.state.report_collaboration_dispatcher = None
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rc_cfg = getattr(config, "report_collaboration", None)
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if rc_cfg is not None and rc_cfg.enabled and rc_cfg.worker_enabled:
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from app.report_collaboration.agentscope_runtime.member_kernel import AgentScopeMemberKernel
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from app.report_collaboration.execution.dispatcher import ReportCollaborationDispatcher
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from app.report_collaboration.execution.executor import ReportCollaborationExecutor
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kernel = AgentScopeMemberKernel(app.state.report_collaboration_store, app_config=config, config=rc_cfg)
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executor = ReportCollaborationExecutor(app.state.report_collaboration_store, kernel, config=rc_cfg)
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dispatcher = ReportCollaborationDispatcher(app.state.report_collaboration_store, executor, config=rc_cfg)
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app.state.report_collaboration_executor = executor
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app.state.report_collaboration_dispatcher = dispatcher
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dispatcher.start()
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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.compact_menu_overrides import make_compact_menu_override_store
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app.state.compact_menu_override_store = make_compact_menu_override_store(sf)
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|
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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)
|
||
|
||
from deerflow.persistence.task_buttons import make_task_button_store
|
||
|
||
app.state.task_button_store = make_task_button_store(sf)
|
||
|
||
from deerflow.persistence.report_structures import make_report_structure_store
|
||
|
||
app.state.report_structure_store = make_report_structure_store(sf)
|
||
|
||
from deerflow.integrations.weknora.runtime import register_llmwiki_store
|
||
from deerflow.persistence.llmwiki import make_llmwiki_store
|
||
|
||
app.state.llmwiki_store = make_llmwiki_store(sf)
|
||
register_llmwiki_store(app.state.llmwiki_store)
|
||
|
||
# Skill distillation and the single global assistant knowledge base.
|
||
# Both repositories live in ``deerflow.*``; only this composition layer
|
||
# wires them to Gateway executors and remote adapters.
|
||
from deerflow.persistence.assistant_knowledge import make_assistant_knowledge_store
|
||
from deerflow.persistence.skill_knowledge import make_skill_knowledge_store
|
||
|
||
app.state.assistant_knowledge_store = make_assistant_knowledge_store(sf)
|
||
app.state.skill_knowledge_store = make_skill_knowledge_store(sf)
|
||
if app.state.skill_knowledge_store is not None and app.state.assistant_knowledge_store is not None:
|
||
from app.gateway.skill_knowledge_job_dispatcher import SkillKnowledgeJobDispatcher
|
||
from app.gateway.skill_knowledge_job_executor import SkillKnowledgeJobExecutor
|
||
|
||
app.state.skill_knowledge_executor = SkillKnowledgeJobExecutor(app)
|
||
app.state.skill_knowledge_dispatcher = SkillKnowledgeJobDispatcher(
|
||
app.state.skill_knowledge_store,
|
||
app.state.skill_knowledge_executor,
|
||
)
|
||
app.state.skill_knowledge_dispatcher.start()
|
||
from app.gateway.skill_knowledge_auto_sync import SkillKnowledgeAutoSyncService
|
||
|
||
app.state.skill_knowledge_auto_sync = SkillKnowledgeAutoSyncService(app)
|
||
app.state.skill_knowledge_auto_sync.start()
|
||
else:
|
||
app.state.skill_knowledge_executor = None
|
||
app.state.skill_knowledge_dispatcher = None
|
||
app.state.skill_knowledge_auto_sync = None
|
||
|
||
# DeerFlow-local Wiki mirror/vector index. The store is always wired so
|
||
# migrations and status inspection work while feature flags are off;
|
||
# network/search services only exist when explicitly enabled.
|
||
from deerflow.integrations.weknora.local_index.runtime import register_local_wiki_index_runtime
|
||
from deerflow.persistence.llmwiki_index import make_llmwiki_index_store
|
||
|
||
local_index_config = config.llmwiki.local_wiki_index
|
||
app.state.llmwiki_index_store = make_llmwiki_index_store(
|
||
sf,
|
||
write_batch_size=local_index_config.database_batch_size,
|
||
)
|
||
app.state.llmwiki_embedding = None
|
||
app.state.llmwiki_vector_cache = None
|
||
app.state.llmwiki_vector_search = None
|
||
app.state.llmwiki_sync_service = None
|
||
app.state.llmwiki_retrieval_service = None
|
||
from deerflow.integrations.weknora.runtime import build_weknora_client, get_resolved_llmwiki_runtime
|
||
|
||
resolved_llmwiki = get_resolved_llmwiki_runtime(config)
|
||
weknora_client = build_weknora_client(resolved_llmwiki) if resolved_llmwiki.weknora_enabled else None
|
||
embedding_config = local_index_config.embedding
|
||
# The administrator may select or upload the offline encoder from the
|
||
# UI. This runtime-owned value deliberately overrides config.yaml and
|
||
# survives application restarts without modifying release assets.
|
||
if embedding_config.provider == "local":
|
||
from deerflow.config.system_settings import load_system_settings
|
||
|
||
runtime_model_path = load_system_settings().local_wiki_encoder.model_path.strip()
|
||
if runtime_model_path:
|
||
embedding_config = embedding_config.model_copy(
|
||
update={"local_model_path": runtime_model_path}
|
||
)
|
||
embedding_configured = bool(
|
||
embedding_config.enabled
|
||
and (embedding_config.base_url or embedding_config.provider == "local")
|
||
and embedding_config.model.strip()
|
||
)
|
||
if embedding_configured:
|
||
from deerflow.integrations.weknora.local_index.embedding import StrictWikiEmbeddingClient
|
||
|
||
app.state.llmwiki_embedding = StrictWikiEmbeddingClient(embedding_config)
|
||
if local_index_config.enabled and app.state.llmwiki_embedding is not None:
|
||
from deerflow.integrations.weknora.local_index.search import WikiVectorSearchService
|
||
from deerflow.integrations.weknora.local_index.sync import WikiSyncService
|
||
from deerflow.integrations.weknora.local_index.vector_cache import WikiVectorCache
|
||
|
||
app.state.llmwiki_vector_cache = WikiVectorCache(
|
||
app.state.llmwiki_index_store,
|
||
max_knowledge_bases=local_index_config.cache_max_knowledge_bases,
|
||
revision_check_seconds=local_index_config.cache_revision_check_seconds,
|
||
)
|
||
app.state.llmwiki_vector_search = WikiVectorSearchService(
|
||
local_index_config,
|
||
app.state.llmwiki_embedding,
|
||
app.state.llmwiki_vector_cache,
|
||
)
|
||
if weknora_client is not None:
|
||
app.state.llmwiki_sync_service = WikiSyncService(
|
||
local_index_config,
|
||
app.state.llmwiki_index_store,
|
||
app.state.llmwiki_embedding,
|
||
app.state.llmwiki_vector_cache,
|
||
weknora_client,
|
||
)
|
||
from app.gateway.knowledge_transfer import sync_to_global
|
||
|
||
async def on_wiki_vectorized(mapping):
|
||
return await sync_to_global(app, mapping)
|
||
|
||
app.state.llmwiki_sync_service.on_completed = on_wiki_vectorized
|
||
if weknora_client is not None or app.state.llmwiki_vector_search is not None:
|
||
from deerflow.integrations.weknora.local_index.retrieval import WikiRetrievalService
|
||
|
||
app.state.llmwiki_retrieval_service = WikiRetrievalService(
|
||
app.state.llmwiki_index_store,
|
||
vector_search=(
|
||
app.state.llmwiki_vector_search
|
||
if local_index_config.internal_search_enabled
|
||
else None
|
||
),
|
||
client=weknora_client,
|
||
)
|
||
register_local_wiki_index_runtime(
|
||
store=app.state.llmwiki_index_store,
|
||
search=app.state.llmwiki_vector_search,
|
||
sync=app.state.llmwiki_sync_service,
|
||
retrieval=app.state.llmwiki_retrieval_service,
|
||
)
|
||
|
||
from deerflow.persistence.taskcop_tasks import make_taskcop_task_store
|
||
|
||
app.state.taskcop_task_store = make_taskcop_task_store(sf)
|
||
|
||
from deerflow.persistence.task_reports import make_task_report_store
|
||
|
||
app.state.task_report_store = make_task_report_store(sf)
|
||
|
||
from deerflow.persistence.roundtable_artifact_submissions import make_roundtable_artifact_submission_store
|
||
|
||
# Task-scoped artifact submission status: shared by task; actor is audit-only.
|
||
app.state.roundtable_artifact_submission_store = make_roundtable_artifact_submission_store(sf)
|
||
|
||
from deerflow.persistence.parallel_agent_tasks import make_parallel_agent_task_store
|
||
|
||
app.state.parallel_agent_task_store = make_parallel_agent_task_store(sf)
|
||
|
||
from deerflow.persistence.roundtable_jobs import (
|
||
make_roundtable_job_store,
|
||
)
|
||
from deerflow.persistence.roundtable_jobs import (
|
||
register_default_store as _register_roundtable_job_default_store,
|
||
)
|
||
|
||
app.state.roundtable_job_store = make_roundtable_job_store(sf)
|
||
# 让 LangGraph Server 路径下的圆桌编排图节点能跨 app/harness 边界拿到 repo
|
||
# （Phase 2 后台编排图写进度用）。
|
||
_register_roundtable_job_default_store(app.state.roundtable_job_store)
|
||
|
||
# 意图回合单飞 store（Phase 4）：``/api/intent/stream`` 据此对 (thread_id,
|
||
# client_turn_id) 跨 worker 单飞 + 幂等回放，杜绝并发双发意图。sf 为 None（无 DB）
|
||
# 时为 None，路由侧判空跳过单飞、走原流程。
|
||
from deerflow.persistence.intent_turns import make_intent_turn_store
|
||
|
||
app.state.intent_turn_store = make_intent_turn_store(sf)
|
||
|
||
# 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。
|
||
# 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。
|
||
from deerflow.persistence.roundtable_diagnostics import (
|
||
make_roundtable_diagnostic_store,
|
||
)
|
||
from deerflow.persistence.roundtable_diagnostics import (
|
||
register_default_store as _register_roundtable_diagnostic_default_store,
|
||
)
|
||
|
||
app.state.roundtable_diagnostic_store = make_roundtable_diagnostic_store(sf)
|
||
_register_roundtable_diagnostic_default_store(app.state.roundtable_diagnostic_store)
|
||
|
||
# 圆桌后台作业执行器：把编排引擎作为后台任务跑。
|
||
# 网关由环境变量 ``ROUNDTABLE_JOB_GATEWAY`` 选择（见 resolve_roundtable_gateway_mode）：
|
||
# - 缺省 / "inprocess"（Phase 3b）：进程内驱动真模型（leader/seat/report）——**默认**，
|
||
# 部署时无需任何配置即可用后台挂起；
|
||
# - 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。
|
||
if app.state.roundtable_job_store is not None:
|
||
import os
|
||
|
||
from app.gateway.roundtable_job_executor import RoundtableJobExecutor
|
||
|
||
gateway_mode = resolve_roundtable_gateway_mode(os.getenv("ROUNDTABLE_JOB_GATEWAY"))
|
||
if gateway_mode == "simulated":
|
||
from deerflow.agents.roundtable_orchestrator import SimulatedRoundtableGateway
|
||
|
||
# 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）。
|
||
gateway_factory = lambda p: SimulatedRoundtableGateway( # noqa: E731
|
||
p.agents, mode=p.mode, delay=3.0
|
||
)
|
||
logger.info("[roundtable-jobs] gateway = simulated")
|
||
else:
|
||
from app.gateway.roundtable_inprocess_gateway import InProcessRoundtableGateway
|
||
|
||
_app = app # 闭包捕获 app，供网关访问 app.state（checkpointer/thread_store）
|
||
gateway_factory = lambda p: InProcessRoundtableGateway( # noqa: E731
|
||
_app,
|
||
user_id=p.user_id,
|
||
agents=p.agents,
|
||
model=p.model,
|
||
# 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。
|
||
seat_thinking_enabled=p.seat_thinking_enabled,
|
||
seat_reasoning_effort=p.seat_reasoning_effort,
|
||
seat_subagent_enabled=p.seat_subagent_enabled,
|
||
seat_skill_directives=p.seat_skill_directives,
|
||
# 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。
|
||
seat_modes=p.seat_modes,
|
||
# 诊断日志上下文：网关每轮 leader/seat/report/summary/dashboard 报错均带这些归属。
|
||
job_id=p.job_id,
|
||
draft_id=p.draft_id,
|
||
task_id=p.task_id,
|
||
)
|
||
logger.info("[roundtable-jobs] gateway = inprocess (real models, default)")
|
||
|
||
app.state.roundtable_job_executor = RoundtableJobExecutor(
|
||
app.state.roundtable_job_store,
|
||
gateway_factory=gateway_factory,
|
||
draft_store=getattr(app.state, "roundtable_draft_store", None),
|
||
# task 作业把最终报告写回**独立的** task-draft 存储（不分权）。
|
||
task_draft_store=getattr(app.state, "roundtable_task_draft_store", None),
|
||
)
|
||
|
||
# Phase 3 dispatcher：跨 worker 领取 queued/租约过期作业 + outbox 补偿器。
|
||
# 每个 worker 进程都跑一个；谁先 claim_job 成功谁执行（DB 条件 UPDATE 互斥），
|
||
# 崩溃的 worker 租约过期后由其它 worker 接管。路由 start/resume 后 nudge 立即唤醒。
|
||
from app.gateway.roundtable_job_dispatcher import RoundtableJobDispatcher
|
||
|
||
app.state.roundtable_job_dispatcher = RoundtableJobDispatcher(
|
||
app.state.roundtable_job_store,
|
||
app.state.roundtable_job_executor,
|
||
draft_store=getattr(app.state, "roundtable_draft_store", None),
|
||
task_draft_store=getattr(app.state, "roundtable_task_draft_store", None),
|
||
)
|
||
app.state.roundtable_job_dispatcher.start()
|
||
else:
|
||
app.state.roundtable_job_executor = None
|
||
app.state.roundtable_job_dispatcher = None
|
||
|
||
# ── Deep Research (深度研究) stores + executor + dispatcher ──────────
|
||
# Five tables: sessions / jobs / events / sources / messages.
|
||
# When DB is available, the executor + dispatcher run background research
|
||
# jobs (lease-based claim, crash-recoverable) mirroring the roundtable pattern.
|
||
from deerflow.persistence.deep_research_events import (
|
||
make_deep_research_event_store,
|
||
)
|
||
from deerflow.persistence.deep_research_jobs import make_deep_research_job_store
|
||
from deerflow.persistence.deep_research_messages import (
|
||
make_deep_research_message_store,
|
||
)
|
||
from deerflow.persistence.deep_research_sessions import (
|
||
make_deep_research_session_store,
|
||
)
|
||
from deerflow.persistence.deep_research_sources import (
|
||
make_deep_research_source_store,
|
||
)
|
||
from deerflow.persistence.document_rewrite_versions import (
|
||
make_document_rewrite_version_store,
|
||
)
|
||
|
||
app.state.deep_research_session_store = make_deep_research_session_store(sf)
|
||
app.state.deep_research_job_store = make_deep_research_job_store(sf)
|
||
app.state.deep_research_event_store = make_deep_research_event_store(sf)
|
||
app.state.deep_research_source_store = make_deep_research_source_store(sf)
|
||
app.state.deep_research_message_store = make_deep_research_message_store(sf)
|
||
# Whole-document AI rewrites retain a durable original snapshot so a
|
||
# completed replacement can be safely undone after a refresh.
|
||
app.state.document_rewrite_version_store = make_document_rewrite_version_store(sf)
|
||
# Low-latency in-process SSE fan-out. The database event log remains
|
||
# authoritative for reconnect replay and cross-worker recovery.
|
||
from app.gateway.deep_research_live_hub import DeepResearchLiveHub
|
||
|
||
app.state.deep_research_live_hub = DeepResearchLiveHub()
|
||
|
||
if app.state.deep_research_job_store is not None:
|
||
from app.gateway.deep_research_job_dispatcher import (
|
||
DeepResearchJobDispatcher,
|
||
)
|
||
from app.gateway.deep_research_job_executor import (
|
||
DeepResearchJobExecutor,
|
||
)
|
||
|
||
app.state.deep_research_executor = DeepResearchJobExecutor(
|
||
app.state.deep_research_job_store,
|
||
app.state.deep_research_session_store,
|
||
app.state.deep_research_event_store,
|
||
app.state.deep_research_source_store,
|
||
live_publisher=app.state.deep_research_live_hub.publish,
|
||
live_subscriber_probe=app.state.deep_research_live_hub.has_subscribers,
|
||
checkpointer=app.state.checkpointer,
|
||
document_rewrite_version_store=app.state.document_rewrite_version_store,
|
||
app_config=config,
|
||
)
|
||
app.state.deep_research_dispatcher = DeepResearchJobDispatcher(
|
||
app.state.deep_research_job_store,
|
||
app.state.deep_research_executor,
|
||
)
|
||
app.state.deep_research_dispatcher.start()
|
||
else:
|
||
app.state.deep_research_executor = None
|
||
app.state.deep_research_dispatcher = None
|
||
|
||
# ── Workflow Studio runtime: live hub + executor + lease dispatcher ──
|
||
# Every worker runs a dispatcher; the atomic claim decides ownership and
|
||
# an expired lease is how a crashed worker's run gets picked back up.
|
||
workflow_cfg = getattr(config, "workflows", None)
|
||
if workflow_cfg is not None and workflow_cfg.enabled:
|
||
from app.gateway.workflow_dispatcher import WorkflowRunDispatcher
|
||
from app.gateway.workflow_executor import WorkflowRunExecutor
|
||
from app.gateway.workflow_live_hub import WorkflowLiveHub
|
||
|
||
app.state.workflow_live_hub = WorkflowLiveHub()
|
||
app.state.workflow_executor = WorkflowRunExecutor(
|
||
app,
|
||
run_store=app.state.workflow_run_store,
|
||
event_store=app.state.workflow_event_store,
|
||
workflow_store=app.state.workflow_store,
|
||
data_source_store=app.state.workflow_data_source_store,
|
||
live_publisher=app.state.workflow_live_hub.publish,
|
||
config=workflow_cfg,
|
||
)
|
||
app.state.workflow_dispatcher = WorkflowRunDispatcher(
|
||
app.state.workflow_run_store,
|
||
app.state.workflow_executor,
|
||
event_store=app.state.workflow_event_store,
|
||
live_publisher=app.state.workflow_live_hub.publish,
|
||
config=workflow_cfg,
|
||
)
|
||
app.state.workflow_dispatcher.start()
|
||
from app.gateway.workflow_retention import WorkflowRetentionCleaner
|
||
|
||
app.state.workflow_retention = WorkflowRetentionCleaner(
|
||
app.state.workflow_run_store,
|
||
app.state.workflow_event_store,
|
||
config=workflow_cfg,
|
||
)
|
||
app.state.workflow_retention.start()
|
||
else:
|
||
app.state.workflow_live_hub = None
|
||
app.state.workflow_executor = None
|
||
app.state.workflow_dispatcher = None
|
||
app.state.workflow_retention = None
|
||
|
||
# Run event store (has its own factory with config-driven backend selection)
|
||
run_events_config = getattr(config, "run_events", None)
|
||
app.state.run_event_store = make_run_event_store(run_events_config)
|
||
|
||
# RunManager with store backing for persistence
|
||
app.state.run_manager = RunManager(store=app.state.run_store)
|
||
from deerflow.runtime import make_redis_concurrency_gate
|
||
|
||
app.state.concurrency_gate = make_redis_concurrency_gate(config)
|
||
|
||
try:
|
||
yield
|
||
finally:
|
||
from app.gateway.assistant_file_ingest import stop_file_jobs
|
||
|
||
await stop_file_jobs()
|
||
from deerflow.integrations.weknora.local_index.runtime import register_local_wiki_index_runtime
|
||
|
||
register_local_wiki_index_runtime(store=None, search=None, sync=None, retrieval=None)
|
||
llmwiki_embedding = getattr(app.state, "llmwiki_embedding", None)
|
||
if llmwiki_embedding is not None:
|
||
await llmwiki_embedding.close()
|
||
from deerflow.persistence.notifications import set_notification_store
|
||
|
||
set_notification_store(None)
|
||
dispatcher = getattr(app.state, "roundtable_job_dispatcher", None)
|
||
if dispatcher is not None:
|
||
await dispatcher.stop()
|
||
dr_dispatcher = getattr(app.state, "deep_research_dispatcher", None)
|
||
if dr_dispatcher is not None:
|
||
await dr_dispatcher.stop()
|
||
wf_dispatcher = getattr(app.state, "workflow_dispatcher", None)
|
||
if wf_dispatcher is not None:
|
||
await wf_dispatcher.stop()
|
||
rc_dispatcher = getattr(app.state, "report_collaboration_dispatcher", None)
|
||
if rc_dispatcher is not None:
|
||
await rc_dispatcher.stop()
|
||
wf_retention = getattr(app.state, "workflow_retention", None)
|
||
if wf_retention is not None:
|
||
await wf_retention.stop()
|
||
from deerflow.workflows.runtime.sql_runner import dispose_engines
|
||
|
||
await dispose_engines()
|
||
skill_knowledge_auto_sync = getattr(app.state, "skill_knowledge_auto_sync", None)
|
||
if skill_knowledge_auto_sync is not None:
|
||
await skill_knowledge_auto_sync.close()
|
||
skill_knowledge_dispatcher = getattr(app.state, "skill_knowledge_dispatcher", None)
|
||
if skill_knowledge_dispatcher is not None:
|
||
await skill_knowledge_dispatcher.close()
|
||
queue = getattr(app.state, "knowledge_auto_ingest_queue", None)
|
||
if queue is not None:
|
||
from deerflow.knowledge.auto_ingest import set_auto_ingest_queue
|
||
|
||
await queue.stop()
|
||
set_auto_ingest_queue(None)
|
||
await close_engine()
|
||
gate = getattr(app.state, "concurrency_gate", None)
|
||
if gate is not None:
|
||
await gate.aclose()
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Getters – called by routers per-request
|
||
# ---------------------------------------------------------------------------
|
||
|
||
|
||
def _require(attr: str, label: str) -> Callable[[Request], T]:
|
||
"""Create a FastAPI dependency that returns ``app.state.<attr>`` or 503."""
|
||
|
||
def dep(request: Request) -> T:
|
||
val = getattr(request.app.state, attr, None)
|
||
if val is None:
|
||
raise HTTPException(status_code=503, detail=f"{label} not available")
|
||
return cast(T, val)
|
||
|
||
dep.__name__ = dep.__qualname__ = f"get_{attr}"
|
||
return dep
|
||
|
||
|
||
get_stream_bridge: Callable[[Request], StreamBridge] = _require("stream_bridge", "Stream bridge")
|
||
get_run_manager: Callable[[Request], RunManager] = _require("run_manager", "Run manager")
|
||
get_checkpointer: Callable[[Request], Checkpointer] = _require("checkpointer", "Checkpointer")
|
||
get_run_event_store: Callable[[Request], RunEventStore] = _require("run_event_store", "Run event store")
|
||
get_feedback_repo: Callable[[Request], FeedbackRepository] = _require("feedback_repo", "Feedback")
|
||
get_run_store: Callable[[Request], RunStore] = _require("run_store", "Run store")
|
||
|
||
|
||
def get_concurrency_gate(request: Request):
|
||
"""Return the optional cross-worker concurrency gate."""
|
||
return getattr(request.app.state, "concurrency_gate", None)
|
||
|
||
|
||
def get_store(request: Request):
|
||
"""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 the caller identity, preferring the explicit Bearer header.
|
||
|
||
An iframe or another tab can rewrite the origin-wide cookie, while the
|
||
frontend deliberately sources its Bearer token from this tab's auth store.
|
||
"""
|
||
from app.gateway.auth.token_source import get_explicit_or_session_token
|
||
|
||
request_token = get_explicit_or_session_token(request)
|
||
if request_token:
|
||
return request_token
|
||
|
||
from app.gateway.proxy_path import app_relative_path
|
||
|
||
path = app_relative_path(request).rstrip("/")
|
||
if path.startswith("/api/llmwiki/knowledge-bases/") and path.endswith("/weknora/frame"):
|
||
query_token = (request.query_params.get("access_token") or "").strip()
|
||
if query_token:
|
||
return query_token
|
||
return 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
|