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ThearchyHelios
e10c4f23fc
来自 2026-09 的 UI/UX 审查（142 条）。本提交是前两波，第三波（记录页接入时间轴、跨页互链、check-ui 更新、全站扫尾）另行提交。 地基 - 设计令牌：全局规则和 .btn/.field/.chip 放进 CSS 层，Tailwind 工具类重新生效，选中态、开启态不再被吞掉；字号阶梯； 辅助文字在所有底色上 >= 4.5:1；状态色专用，节点类型色与之拉开色相；「跟随系统」真的跟随，首帧不闪深色；新字标与 favicon - 基础组件：confirmDialog / promptDialog 取代原生对话框；toast 出错不自动消失、可复制、读屏可播报；离线横幅与断线恢复； 九种剪影的状态徽标；唯一一份状态文案（lib/status）、格式化（lib/format）、术语（lib/terms）、报错人话（lib/errors） - 运行态内核：事件折成航迹（run/trace.ts），可投影到任意时刻；取消 / 失败 / 服务重启后统一收尾，不再永远转圈； run.status 随事件回写；token 事件不再堆进 events - 后端：协作成员逐个完成即发事件，调度者决策有事件；时长分执行 / 墙钟 / 等人；API 时间带时区； 中断的运行也发 stream.end；运行列表可筛选翻页；删除运行 / 工作流有保护；运行默认值接通；可测未保存的配置； 报错说清发生了什么、为什么、怎么办 功能 - 画布：状态四重编码、固定高度遥测槽、按缩放三档、运行胶囊与 HUD、航迹时间轴与回放、执行轨迹边、 等待闸门、跟随执行、正式运行只读、一次性时刻（开场点亮、回扫、阻断级联、出具印章） - 编辑：撤销重做、版本历史、问题面板可定位、三栏可收起、模板与表达式字段区分、变量血缘、快捷键、带搜索的选择器 - 回答流 / 问数据：贴底才跟随滚动、工件下钻、出具横幅列出缺口、答案可复制导出、按会话的忙闲、 答案不再在 2000 字处截断、重启后这一轮会收尾 - 记录页：待审批 / 运行中 / 失败页签、筛选翻页、实时接流、排错路径指到节点、封存凭证常驻 - 管理页：新的「数据」页、写操作失败都有反馈、弹窗里先测连接、统一持久的连接状态 - 外壳：导航改版、⌘K 命令面板、? 快捷键帮助、404、页面标题与 favicon 状态点、后台提醒 验证（沙箱）：tsc 干净，pytest 579 passed，14 个检查脚本中 13 个全绿；check-ui 剩 1 项旧断言（按旧日期格式找运行行），第三波更新。
564 lines
21 KiB
Python
564 lines
21 KiB
Python
from __future__ import annotations
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import logging
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from typing import Any
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from fastapi import (
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APIRouter, BackgroundTasks, Depends, File, HTTPException, Query, UploadFile,
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)
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from pydantic import BaseModel, Field
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.api.errors import explain, raw
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from app.core.config import settings
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from app.db.base import get_session
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from app.db.models import Document, MemoryItem, Skill
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from app.memory import inverted, kb, store
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logger = logging.getLogger(__name__)
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# --------------------------------------------------------------------------
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# 长期记忆
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# --------------------------------------------------------------------------
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memory_router = APIRouter(prefix="/api/memory", tags=["memory"])
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class MemoryIn(BaseModel):
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content: str
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scope: str = "default"
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kind: str = "fact"
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importance: float = Field(default=0.5, ge=0, le=1)
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meta: dict[str, Any] = Field(default_factory=dict)
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class MemoryPatch(BaseModel):
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content: str | None = None
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kind: str | None = None
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importance: float | None = Field(default=None, ge=0, le=1)
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class MemoryOut(BaseModel):
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id: str
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scope: str
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kind: str
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content: str
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importance: float
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use_count: int
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meta: dict[str, Any]
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#: 这条是哪来的：kind=run 时带运行、节点和工作流名；manual 是手动添加的；
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#: playground 是在工具库里直接调 remember 写进来的
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source: dict[str, Any] = Field(default_factory=dict)
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created_at: Any = None
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# 不给 updated_at：每次召回记计数也是一次写，它跟着召回走，当「修改于」
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# 显示就是错的。界面要的「记下于」「上次召回」各有字段
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last_used_at: Any = None
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model_config = {"from_attributes": True}
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def _content(text: str) -> str:
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"""只有空白的内容和空串是一回事——存下去就是一条什么都召回不到的空记忆。"""
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if not text.strip():
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raise HTTPException(422, "记忆内容是空的：写上要记住的那句话再保存")
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return text
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async def _with_source(session: AsyncSession, rows: list[MemoryItem]) -> list[MemoryOut]:
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"""给记忆补上来源。
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长期记忆里存的是口径规则，审查时得知道「这条是哪次运行、哪个节点写进来的」
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才能判断能不能信。meta 里只有 id，节点名和工作流名要回运行记录里查——
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运行被删了就照实标 run_exists=false，不编一个出来。
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"""
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from app.db.models import Run
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run_ids = {str((r.meta or {}).get("run_id") or "") for r in rows} - {"", "playground"}
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runs: dict[str, Any] = {}
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if run_ids:
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for row in await session.execute(
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select(Run.id, Run.workflow_name, Run.graph).where(Run.id.in_(run_ids))
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):
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runs[row.id] = row
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out: list[MemoryOut] = []
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for item in rows:
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meta = item.meta or {}
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run_id = str(meta.get("run_id") or "")
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node_id = meta.get("node_id") or None
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if not run_id:
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source: dict[str, Any] = {"kind": "manual"}
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elif run_id == "playground":
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source = {"kind": "playground"}
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else:
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run = runs.get(run_id)
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nodes = ((run.graph if run else None) or {}).get("nodes") or []
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label = next(
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((n.get("data") or {}).get("label") for n in nodes if n.get("id") == node_id), None
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)
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source = {
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"kind": "run", "run_id": run_id, "node_id": node_id,
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"node_label": label or None,
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"workflow_name": (run.workflow_name or None) if run else None,
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"run_exists": run is not None,
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}
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view = MemoryOut.model_validate(item)
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view.source = source
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out.append(view)
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return out
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@memory_router.get("/scopes")
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async def memory_scopes(session: AsyncSession = Depends(get_session)) -> list[dict[str, Any]]:
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return await store.list_scopes(session)
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@memory_router.get("", response_model=list[MemoryOut])
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async def list_memory(
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scope: str | None = None,
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limit: int = Query(default=200, le=500),
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session: AsyncSession = Depends(get_session),
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) -> list[MemoryOut]:
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return await _with_source(session, await store.list_memories(session, scope=scope, limit=limit))
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@memory_router.post("", response_model=MemoryOut, status_code=201)
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async def add_memory(
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payload: MemoryIn, session: AsyncSession = Depends(get_session)
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) -> MemoryOut:
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item = await store.remember(
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session,
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scope=payload.scope,
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content=_content(payload.content),
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kind=payload.kind,
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importance=payload.importance,
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meta=payload.meta,
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)
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return (await _with_source(session, [item]))[0]
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@memory_router.patch("/{memory_id}", response_model=MemoryOut)
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async def update_memory(
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memory_id: str, payload: MemoryPatch, session: AsyncSession = Depends(get_session)
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) -> MemoryOut:
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"""写错了原地改，不必删掉重写——重写会丢掉来源和召回记录。"""
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item = await store.revise(
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session, memory_id,
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content=None if payload.content is None else _content(payload.content),
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kind=payload.kind, importance=payload.importance,
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)
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if item is None:
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raise HTTPException(404, "这条记忆不存在，可能已经被删了")
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return (await _with_source(session, [item]))[0]
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@memory_router.get("/search")
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async def search_memory(
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q: str,
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scope: str = "default",
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limit: int = 10,
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peek: bool = Query(default=False, description="只看不记：调试台用，不计入召回次数"),
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session: AsyncSession = Depends(get_session),
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) -> dict[str, Any]:
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hits = await store.recall(session, scope=scope, query=q, limit=limit, track=not peek)
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return {"query": q, "scope": scope, "results": hits, "peek": peek}
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@memory_router.delete("/{memory_id}", status_code=204)
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async def delete_memory(
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memory_id: str, session: AsyncSession = Depends(get_session)
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) -> None:
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if not await store.forget(session, memory_id):
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raise HTTPException(404, "这条记忆不存在，可能已经被删了")
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@memory_router.delete("/scope/{scope}")
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async def clear_scope(scope: str, session: AsyncSession = Depends(get_session)) -> dict[str, int]:
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return {"removed": await store.clear_scope(session, scope)}
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# --------------------------------------------------------------------------
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# 知识库
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# --------------------------------------------------------------------------
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kb_router = APIRouter(prefix="/api/kb", tags=["knowledge"])
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class DocumentOut(BaseModel):
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id: str
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collection: str
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title: str
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source: str
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mime: str
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chunk_count: int
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#: ready | processing | failed。前端据此显示"处理中 3200/7914 段"
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status: str = "ready"
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error: str = ""
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meta: dict[str, Any] = Field(default_factory=dict)
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created_at: Any = None
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model_config = {"from_attributes": True}
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@kb_router.get("/collections")
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async def collections(session: AsyncSession = Depends(get_session)) -> list[dict[str, Any]]:
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return await kb.list_collections(session)
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@kb_router.get("/formats")
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async def formats() -> dict[str, Any]:
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"""上传框收哪些文件。前端的 accept 从这里取，和后端的解析判断是同一份清单。"""
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from app.memory.parsing import supported_formats
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out: dict[str, Any] = supported_formats()
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out["accept"] = ",".join(out["extensions"])
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return out
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@kb_router.get("/documents", response_model=list[DocumentOut])
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async def list_documents(
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collection: str | None = None,
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session: AsyncSession = Depends(get_session),
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) -> list[Document]:
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stmt = select(Document).order_by(Document.created_at.desc()).limit(300)
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if collection:
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stmt = stmt.where(Document.collection == collection)
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return list((await session.execute(stmt)).scalars())
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class IngestIn(BaseModel):
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collection: str = "default"
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title: str = ""
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content: str = Field(min_length=1)
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source: str = ""
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@kb_router.post("/documents", response_model=DocumentOut, status_code=201)
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async def ingest(payload: IngestIn, session: AsyncSession = Depends(get_session)) -> Document:
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return await kb.ingest_document(
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session,
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collection=payload.collection,
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title=payload.title,
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content=payload.content,
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source=payload.source,
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)
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@kb_router.post("/upload", response_model=DocumentOut, status_code=201)
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async def upload(
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background: BackgroundTasks,
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file: UploadFile = File(...),
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collection: str = "default",
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session: AsyncSession = Depends(get_session),
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) -> Document:
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# 边读边数，超了立刻停——原来是 raw = await file.read() 读完再判断，
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# 传个 1GB 的文件，内存在报 413 之前就已经吃掉了。那个检查拦的是"入库"，
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# 不是"占内存"
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limit = settings.max_upload_mb * 1024 * 1024
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chunks: list[bytes] = []
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total = 0
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while piece := await file.read(1024 * 1024):
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total += len(piece)
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if total > limit:
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raise HTTPException(413, f"文件超过 {settings.max_upload_mb}MB")
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chunks.append(piece)
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raw = b"".join(chunks)
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from app.memory.parsing import UnsupportedDocument, extract
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try:
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content = extract(raw, file.filename or "", file.content_type or "")
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except UnsupportedDocument as e:
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# 原样交回：里面写的是"装哪个包"或"这是扫描件"，都能照着做
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raise HTTPException(415, str(e)) from None
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doc = await kb.create_document(
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session,
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collection=collection,
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title=file.filename or "上传文档",
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content=content,
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source=file.filename or "",
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mime=file.content_type or "text/plain",
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status="processing",
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)
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# 立刻返回，切块和算向量在后台跑。实测一个 10MB 文档用本地哈希要两分半，
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# 换成远端 embedding 是几十分钟——压在请求里必然超时，而后端其实还在跑，
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# 界面显示失败、数据其实成功，比直接拒绝还糟
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background.add_task(_process_in_background, doc.id)
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return doc
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async def _process_in_background(doc_id: str) -> None:
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"""后台切块。进度写回文档行，失败也写回——不然它会永远停在"处理中"。"""
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from app.db.base import SessionLocal
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async with SessionLocal() as session:
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doc = await session.get(Document, doc_id)
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if not doc:
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return
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try:
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# 进度由 process_document 自己按批提交——它就在主事务里，
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# 不会和另一个 session 抢 SQLite 的写锁
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await kb.process_document(session, doc)
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except Exception as e: # noqa: BLE001
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logger.warning("文档 %s 处理失败：%s", doc_id, raw(e))
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await session.rollback()
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doc = await session.get(Document, doc_id)
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if doc:
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reason, hint = explain(e)
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doc.status = "failed"
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doc.error = (f"切块或算向量时出错：{reason}" + (f"。{hint}" if hint else ""))[:500]
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await session.commit()
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@kb_router.get("/search")
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async def search_kb(
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q: str,
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collection: str | None = None,
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limit: int = 5,
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alpha: float | None = Query(default=None, ge=0, le=1,
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description="1=纯向量, 0=纯关键词；不传则按 embedder 能力取默认"),
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session: AsyncSession = Depends(get_session),
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) -> dict[str, Any]:
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from app.memory.embeddings import default_alpha
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notes: list[str] = []
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# 没传就是运行时的默认值。把实际用的那个交回去：调试台的用处是预演 agent
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# 会拿到什么，默认值对不上，人会照着错的命中去调知识库
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used = default_alpha() if alpha is None else alpha
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hits = await kb.search(session, collection=collection, query=q, limit=limit,
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alpha=used, on_degrade=notes.append)
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# 退回关键词这件事要跟着结果一起交出去，而不是只写进服务端日志——
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# 调用方（设置页的"试一下"、外部脚本）看到的是结果变差，得知道为什么
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return {"query": q, "collection": collection, "results": hits, "degraded": notes,
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"alpha": used}
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@kb_router.get("/embedding")
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async def embedding_status(
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collection: str | None = None, session: AsyncSession = Depends(get_session)
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) -> dict[str, Any]:
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"""当前用的是哪个 embedder，以及有多少条向量已经对不上了。"""
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from app.memory.embeddings import (
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EMBEDDING_SETTING_KEY, default_alpha, embedder_dim, embedder_id, has_semantics,
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unavailable_reason,
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)
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from app.db.models import Setting
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row = await session.get(Setting, EMBEDDING_SETTING_KEY)
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saved = (row.value if row else {}) or {}
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# 配的是语义模型，实际在用本地哈希：保存的配置没生效（多半是启动时本机的模型
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# 服务还没起）。这时"对不上"的存量向量正是那个模型建好的，界面不能再劝人重建——
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# 那等于拿哈希把它们覆盖掉，连上之后还得再重建一遍
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fallback = saved.get("kind") == "openai" and not has_semantics()
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return {
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"embedder": embedder_id(),
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"dim": embedder_dim(),
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"configured": bool(saved.get("kind")),
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"kind": saved.get("kind", "local"),
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"model": saved.get("model", ""),
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"base_url": saved.get("base_url", ""),
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"stale_chunks": await kb.stale_count(session, collection),
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# 记忆和知识库共用一个 embedder，换模型时一起失效——报一个漏一个
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# 的话，用户点完重建还是想不起事，而且不知道为什么
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"stale_memories": await store.stale_count(session),
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"has_semantics": has_semantics(),
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"fallback": fallback,
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"fallback_reason": (unavailable_reason() or "") if fallback else "",
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"default_alpha": default_alpha(),
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# 没建倒排的片段会走全表扫——结果对，但慢
|
||
"unindexed_chunks": await inverted.missing_count(session, collection),
|
||
}
|
||
|
||
|
||
class EmbeddingIn(BaseModel):
|
||
kind: str = Field(default="local", pattern="^(local|openai)$")
|
||
model: str = ""
|
||
#: 任何讲 OpenAI /v1/embeddings 的地址：本机 LM Studio / Ollama / vLLM / TEI，
|
||
#: 或者你自己的网关。留空才走 api.openai.com
|
||
base_url: str = ""
|
||
|
||
|
||
@kb_router.put("/embedding")
|
||
async def set_embedding(
|
||
payload: EmbeddingIn, session: AsyncSession = Depends(get_session)
|
||
) -> dict[str, Any]:
|
||
"""换 embedding 模型。
|
||
|
||
建不起来就 400 原样交回原因——选了远端却静默退回本地哈希向量，
|
||
用户会以为自己在用语义检索，而搜不出同义表达时完全无从怀疑到这里。
|
||
"""
|
||
from app.memory import embeddings as emb
|
||
from app.db.models import Setting
|
||
|
||
try:
|
||
emb.configure(payload.kind, payload.model, payload.base_url)
|
||
except emb.EmbedderUnavailable as e:
|
||
raise HTTPException(400, str(e)) from e
|
||
|
||
row = await session.get(Setting, emb.EMBEDDING_SETTING_KEY)
|
||
value = {"kind": payload.kind, "model": payload.model,
|
||
"base_url": payload.base_url}
|
||
if row:
|
||
row.value = value
|
||
else:
|
||
session.add(Setting(key=emb.EMBEDDING_SETTING_KEY, value=value))
|
||
await session.commit()
|
||
return {
|
||
"embedder": emb.embedder_id(),
|
||
"dim": emb.embedder_dim(),
|
||
"stale_chunks": await kb.stale_count(session),
|
||
"stale_memories": await store.stale_count(session),
|
||
}
|
||
|
||
|
||
@kb_router.get("/embedding/probe")
|
||
async def probe_embedding(base_url: str) -> dict[str, Any]:
|
||
"""问问这个地址上有哪些模型。
|
||
|
||
自己填模型名太容易写错（LM Studio 里叫
|
||
text-embedding-qwen3-embedding-4b，不是 Qwen3-Embedding-4B），
|
||
而填错的表现是切换时报一句 404，人还以为是服务没起。
|
||
"""
|
||
import httpx
|
||
|
||
url = base_url.rstrip("/") + "/models"
|
||
try:
|
||
async with httpx.AsyncClient(timeout=8) as client:
|
||
data = (await client.get(url)).json()
|
||
except Exception as e: # noqa: BLE001
|
||
reason, hint = explain(e)
|
||
raise HTTPException(400, f"问不到 {url} 上有哪些模型：{reason}" + (f"。{hint}" if hint else "")) from e
|
||
models = [m.get("id", "") for m in (data.get("data") or []) if m.get("id")]
|
||
return {"base_url": base_url, "models": models}
|
||
|
||
|
||
@kb_router.post("/reindex")
|
||
async def reindex(
|
||
collection: str | None = None, session: AsyncSession = Depends(get_session)
|
||
) -> dict[str, Any]:
|
||
"""用当前 embedder 重算向量。换了模型之后唯一的恢复手段。
|
||
|
||
知识库和长期记忆一起重建：它们共用一个 embedder，换模型时一起失效。
|
||
分成两个按钮的结果是点了一个、以为好了，另一个还在悄悄退回关键词——
|
||
真踩过，记忆那边连个提示都没有。
|
||
"""
|
||
out = await kb.reindex(session, collection)
|
||
# 记忆不按 collection 分，重建就是全量
|
||
out["memories_reindexed"] = await store.reindex(session)
|
||
return out
|
||
|
||
|
||
#: 单段返回多少字。切块本来就在千字量级，详情页是给人扫一眼"切得对不对"的，
|
||
#: 不是给人通读原文的——整篇几千段全量吐出去，一份大文档能有几兆
|
||
_CHUNK_PREVIEW = 1200
|
||
|
||
|
||
@kb_router.get("/documents/{doc_id}")
|
||
async def get_document(
|
||
doc_id: str, session: AsyncSession = Depends(get_session)
|
||
) -> dict[str, Any]:
|
||
"""一份文档被切成了什么样。
|
||
|
||
切块这一层做了不少事——按行边界留重叠、跨块把表头续上（memory/kb.py 的
|
||
chunk_text），而在此之前结果在界面上一处都看不见。检索不准的时候第一个
|
||
该看的就是它：表头有没有续上、重叠对不对、哪一段被截断了。
|
||
"""
|
||
from app.db.models import Chunk
|
||
|
||
doc = await session.get(Document, doc_id)
|
||
if not doc:
|
||
raise HTTPException(404, "这份文档不存在，可能已经被删了")
|
||
|
||
rows = list((await session.execute(
|
||
select(Chunk).where(Chunk.document_id == doc_id).order_by(Chunk.ordinal)
|
||
)).scalars())
|
||
|
||
return {
|
||
"document": DocumentOut.model_validate(doc).model_dump(mode="json"),
|
||
"chunks": [
|
||
{
|
||
"id": c.id,
|
||
"ordinal": c.ordinal,
|
||
"content": c.content[:_CHUNK_PREVIEW],
|
||
"truncated": len(c.content) > _CHUNK_PREVIEW,
|
||
"chars": len(c.content),
|
||
"token_len": c.token_len,
|
||
# 哪条向量是谁建的。一份文档里混着两个模型建的向量是真会发生的
|
||
# （换模型之后只重建了一部分），而那会让检索悄悄退回关键词
|
||
"embed_model": c.embed_model or "",
|
||
"has_vector": c.embedding is not None,
|
||
}
|
||
for c in rows
|
||
],
|
||
}
|
||
|
||
|
||
@kb_router.delete("/documents/{doc_id}", status_code=204)
|
||
async def delete_document(doc_id: str, session: AsyncSession = Depends(get_session)) -> None:
|
||
if not await kb.delete_document(session, doc_id):
|
||
raise HTTPException(404, "这份文档不存在，可能已经被删了")
|
||
|
||
|
||
# --------------------------------------------------------------------------
|
||
# Skill（方法论）
|
||
# --------------------------------------------------------------------------
|
||
|
||
skills_router = APIRouter(prefix="/api/skills", tags=["knowledge"])
|
||
|
||
|
||
class SkillIn(BaseModel):
|
||
name: str = Field(min_length=1, max_length=100)
|
||
description: str = ""
|
||
instructions: str = ""
|
||
examples: list[dict[str, Any]] = Field(default_factory=list)
|
||
suggested_tools: list[str] = Field(default_factory=list)
|
||
tags: list[str] = Field(default_factory=list)
|
||
enabled: bool = True
|
||
|
||
|
||
class SkillOut(SkillIn):
|
||
id: str
|
||
created_at: Any = None
|
||
|
||
model_config = {"from_attributes": True}
|
||
|
||
|
||
@skills_router.get("", response_model=list[SkillOut])
|
||
async def list_skills(session: AsyncSession = Depends(get_session)) -> list[Skill]:
|
||
return list((await session.execute(select(Skill).order_by(Skill.created_at.desc()))).scalars())
|
||
|
||
|
||
@skills_router.post("", response_model=SkillOut, status_code=201)
|
||
async def create_skill(payload: SkillIn, session: AsyncSession = Depends(get_session)) -> Skill:
|
||
if (await session.execute(select(Skill).where(Skill.name == payload.name))).scalar_one_or_none():
|
||
raise HTTPException(409, "同名 Skill 已存在")
|
||
row = Skill(**payload.model_dump())
|
||
session.add(row)
|
||
await session.commit()
|
||
await session.refresh(row)
|
||
return row
|
||
|
||
|
||
@skills_router.patch("/{skill_id}", response_model=SkillOut)
|
||
async def update_skill(
|
||
skill_id: str, payload: SkillIn, session: AsyncSession = Depends(get_session)
|
||
) -> Skill:
|
||
row = await session.get(Skill, skill_id)
|
||
if not row:
|
||
raise HTTPException(404, "这个 Skill 不存在，可能已经被删了")
|
||
for key, value in payload.model_dump().items():
|
||
setattr(row, key, value)
|
||
await session.commit()
|
||
await session.refresh(row)
|
||
return row
|
||
|
||
|
||
@skills_router.delete("/{skill_id}", status_code=204)
|
||
async def delete_skill(skill_id: str, session: AsyncSession = Depends(get_session)) -> None:
|
||
row = await session.get(Skill, skill_id)
|
||
if not row:
|
||
raise HTTPException(404, "这个 Skill 不存在，可能已经被删了")
|
||
await session.delete(row)
|
||
await session.commit()
|