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ThearchyHelios
6a46e4b55c
压测量出来的三个问题，10MB 文本（切 7914 段）：
上传耗时 150s（本地哈希） 换成 Qwen3-Embedding-4B 要 40 分钟以上
而这 150 秒是压在一个 HTTP 请求里同步跑完的
浏览器和反向代理的默认超时都在 60 秒上下，也就是 5MB 以上的文件用户必然
看到超时——而后端其实还在跑，跑完文档也确实入了库。界面显示失败、数据其实
成功，比直接拒绝还糟。
**分批算向量。** 原来 ingest 是 embed_texts(pieces) 一次全发。本地哈希只是慢，
换成远端模型就是必挂：近八千段一次请求塞不下（OpenAI 单次上限 2048）。
reindex 那边早就分批了，ingest 这条一直漏着——而我上一轮刚把"换真模型"做成
设置页里点两下的事。
**上传转后台。** create_document / process_document 拆开，上传立刻返回
status=processing，切块和算向量在后台跑，进度写回文档行，前端轮询显示
"正在切块并算向量… 4288/7914 段"。实测上传返回 0.07 秒。
进度这件事返工过一次：先用另一个 session 单独写，结果整个过程界面上都是空的、
只在结束时闪一下——SQLite 只允许一个写者，主事务开着的时候那次写被堵住了。
改成主事务自己按批提交，顺手把进度写进去。
失败要写回 failed，否则永远停在"处理中"转圈。服务重启时也把残留的
processing 收尾成 failed——和 runner 对 running 的 run 是同一个道理。
**边读边数大小。** 原来是 raw = await file.read() 读完再判断，传个 1GB 的
文件内存在报 413 之前就吃掉了——那个检查拦的是"入库"，不是"占内存"。改成
按块读、超了立刻停。上限从写死的 10MB 变成配置项，默认 50MB。
端到端验过：10MB 上传返回 0.07s，后台进度 768→1472→…→7914 实时可见，
最终 ready / 7914 段。
409 lines
14 KiB
Python
409 lines
14 KiB
Python
from __future__ import annotations
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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.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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# --------------------------------------------------------------------------
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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 = Field(min_length=1)
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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 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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created_at: Any = None
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model_config = {"from_attributes": True}
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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[MemoryItem]:
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return 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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) -> MemoryItem:
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return await store.remember(
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session,
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scope=payload.scope,
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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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@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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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)
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return {"query": q, "scope": scope, "results": hits}
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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, "记忆不存在")
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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("/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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await session.rollback()
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doc = await session.get(Document, doc_id)
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if doc:
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doc.status = "failed"
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doc.error = f"{type(e).__name__}: {e}"[: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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notes: list[str] = []
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hits = await kb.search(session, collection=collection, query=q, limit=limit,
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alpha=alpha, 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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@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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)
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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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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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"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
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"unindexed_chunks": await inverted.missing_count(session, collection),
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}
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class EmbeddingIn(BaseModel):
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kind: str = Field(default="local", pattern="^(local|openai)$")
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model: str = ""
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#: 任何讲 OpenAI /v1/embeddings 的地址：本机 LM Studio / Ollama / vLLM / TEI，
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#: 或者你自己的网关。留空才走 api.openai.com
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base_url: str = ""
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@kb_router.put("/embedding")
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async def set_embedding(
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payload: EmbeddingIn, session: AsyncSession = Depends(get_session)
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) -> dict[str, Any]:
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"""换 embedding 模型。
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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，
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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。
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"""
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from app.memory import embeddings as emb
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from app.db.models import Setting
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try:
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emb.configure(payload.kind, payload.model, payload.base_url)
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except emb.EmbedderUnavailable as e:
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raise HTTPException(400, str(e)) from e
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row = await session.get(Setting, emb.EMBEDDING_SETTING_KEY)
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value = {"kind": payload.kind, "model": payload.model,
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"base_url": payload.base_url}
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if row:
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row.value = value
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else:
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session.add(Setting(key=emb.EMBEDDING_SETTING_KEY, value=value))
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await session.commit()
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return {
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"embedder": emb.embedder_id(),
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"dim": emb.embedder_dim(),
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"stale_chunks": await kb.stale_count(session),
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"stale_memories": await store.stale_count(session),
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}
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@kb_router.get("/embedding/probe")
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async def probe_embedding(base_url: str) -> dict[str, Any]:
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"""问问这个地址上有哪些模型。
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自己填模型名太容易写错（LM Studio 里叫
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text-embedding-qwen3-embedding-4b，不是 Qwen3-Embedding-4B），
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而填错的表现是切换时报一句 404，人还以为是服务没起。
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"""
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import httpx
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url = base_url.rstrip("/") + "/models"
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try:
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async with httpx.AsyncClient(timeout=8) as client:
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data = (await client.get(url)).json()
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except Exception as e: # noqa: BLE001
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raise HTTPException(400, f"连不上 {url}：{type(e).__name__}: {e}") from e
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models = [m.get("id", "") for m in (data.get("data") or []) if m.get("id")]
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return {"base_url": base_url, "models": models}
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@kb_router.post("/reindex")
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async def reindex(
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collection: str | None = None, session: AsyncSession = Depends(get_session)
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) -> dict[str, Any]:
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"""用当前 embedder 重算向量。换了模型之后唯一的恢复手段。
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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。
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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——
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真踩过，记忆那边连个提示都没有。
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"""
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out = await kb.reindex(session, collection)
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# 记忆不按 collection 分，重建就是全量
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out["memories_reindexed"] = await store.reindex(session)
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return out
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@kb_router.delete("/documents/{doc_id}", status_code=204)
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async def delete_document(doc_id: str, session: AsyncSession = Depends(get_session)) -> None:
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if not await kb.delete_document(session, doc_id):
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raise HTTPException(404, "文档不存在")
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# --------------------------------------------------------------------------
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# Skill（方法论）
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# --------------------------------------------------------------------------
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skills_router = APIRouter(prefix="/api/skills", tags=["knowledge"])
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class SkillIn(BaseModel):
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name: str = Field(min_length=1, max_length=100)
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description: str = ""
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instructions: str = ""
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examples: list[dict[str, Any]] = Field(default_factory=list)
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suggested_tools: list[str] = Field(default_factory=list)
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tags: list[str] = Field(default_factory=list)
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enabled: bool = True
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class SkillOut(SkillIn):
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id: str
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created_at: Any = None
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model_config = {"from_attributes": True}
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@skills_router.get("", response_model=list[SkillOut])
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async def list_skills(session: AsyncSession = Depends(get_session)) -> list[Skill]:
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return list((await session.execute(select(Skill).order_by(Skill.created_at.desc()))).scalars())
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@skills_router.post("", response_model=SkillOut, status_code=201)
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async def create_skill(payload: SkillIn, session: AsyncSession = Depends(get_session)) -> Skill:
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if (await session.execute(select(Skill).where(Skill.name == payload.name))).scalar_one_or_none():
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raise HTTPException(409, "同名 Skill 已存在")
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row = Skill(**payload.model_dump())
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session.add(row)
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await session.commit()
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await session.refresh(row)
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return row
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@skills_router.patch("/{skill_id}", response_model=SkillOut)
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async def update_skill(
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skill_id: str, payload: SkillIn, session: AsyncSession = Depends(get_session)
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) -> Skill:
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row = await session.get(Skill, skill_id)
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if not row:
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raise HTTPException(404, "Skill 不存在")
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for key, value in payload.model_dump().items():
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setattr(row, key, value)
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await session.commit()
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await session.refresh(row)
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return row
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@skills_router.delete("/{skill_id}", status_code=204)
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async def delete_skill(skill_id: str, session: AsyncSession = Depends(get_session)) -> None:
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row = await session.get(Skill, skill_id)
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if not row:
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raise HTTPException(404, "Skill 不存在")
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await session.delete(row)
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await session.commit()
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