Files
playchat/backend/app/api/settings.py
T
colcaroliandClaude Opus 5 97568ba90e Re-embed the knowledge base when the embedding model changes
Vectors from two different embedding models are not comparable — Titan v2 has
1024 dimensions where nomic-embed-text has 768 — and _cosine_similarity zips
them, so a mismatch produced quietly meaningless scores rather than an error.
Nothing forced a re-embed either: sync_local_kb only looked at the content
hash, so switching models left every cached vector in place.

local_knowledge_docs now records the model each vector came from, and a row is
re-embedded when its content hash OR its model differs. Saving a new
LOCAL_KB_EMBEDDING_MODEL from the settings panel starts a background pass over
every file; the knowledge base panel also has a button for it. While that pass
runs, the per-request sync steps aside so no file is embedded twice.

The panel now shows what the embedding model is doing: model name, vector
size, how many files are embedded with the current model, how many are still
on an older one, a progress bar while a pass runs, and the last error. A
"接続テスト" button embeds one short string so a broken or unreachable model
can be told apart from one that is merely slow to warm up.

Also read TAVILY_API_KEY from os.environ at call time. It was imported as a
config constant, which is evaluated before load_into_env() runs, so a key
saved from the settings panel was stored but never used — not even after a
restart.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-23 22:56:12 +09:00

70 lines
2.5 KiB
Python

import os
from fastapi import APIRouter, Depends, HTTPException
from pydantic import BaseModel
from sqlalchemy.orm import Session as DBSession
from app.core import settings_store
from app.core.auth import require_settings_password
from app.core.memory_kb import local_knowledge
from app.db.database import get_db
router = APIRouter(prefix="/api/settings", tags=["settings"], dependencies=[Depends(require_settings_password)])
class SettingEntry(BaseModel):
key: str
is_set: bool
# 秘匿キーはマスク済みの値、それ以外は現在値そのもの。未設定なら None
display_value: str | None
# 入力欄をパスワード扱いにするか(APIキー等は True)
secret: bool
overridden: bool
class SaveSettingsRequest(BaseModel):
values: dict[str, str]
@router.get("", response_model=list[SettingEntry])
def list_settings(db: DBSession = Depends(get_db)):
return settings_store.list_masked(db)
@router.put("", response_model=list[SettingEntry])
async def save_settings(req: SaveSettingsRequest, db: DBSession = Depends(get_db)):
before_embedding_model = os.environ.get("LOCAL_KB_EMBEDDING_MODEL")
try:
for key, value in req.values.items():
if not value:
continue # 空欄は「変更なし」として無視する
settings_store.save(db, key, value)
except ValueError as e:
raise HTTPException(status_code=400, detail=str(e)) from e
_reembed_if_model_changed(before_embedding_model)
return settings_store.list_masked(db)
@router.delete("/{key}", response_model=list[SettingEntry])
async def clear_setting(key: str, db: DBSession = Depends(get_db)):
before_embedding_model = os.environ.get("LOCAL_KB_EMBEDDING_MODEL")
try:
settings_store.clear(db, key)
except ValueError as e:
raise HTTPException(status_code=400, detail=str(e)) from e
_reembed_if_model_changed(before_embedding_model)
return settings_store.list_masked(db)
def _reembed_if_model_changed(before: str | None) -> None:
"""埋め込みモデルが変わったら、ローカルKBの再埋め込みをバックグラウンドで走らせる。
モデルが違うと次元数も意味空間も変わり、古いベクトルは比較しても無意味なので、
設定を変えた時点で全件やり直す(完了を待たずに応答は返す)。
"""
if os.environ.get("LOCAL_KB_EMBEDDING_MODEL") == before:
return
local_knowledge.start_reembed_all()