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>

backend/app/core/config.py

@@ -25,6 +25,7 @@ SESSION_MAX_AGE_DAYS: int = int(os.getenv("SESSION_MAX_AGE_DAYS", "30"))
 TAGGING_MODEL: str | None = os.getenv("TAGGING_MODEL")
 
 # Tavily Web検索
+# GUI設定画面から実行時に変更できるため、実際の参照は search/websearch._api_key() が os.environ から行う。
 TAVILY_API_KEY: str | None = os.getenv("TAVILY_API_KEY")
 # 蓄積した知識をシステムプロンプトに注入する際の最大件数
 KNOWLEDGE_CONTEXT_LIMIT: int = int(os.getenv("KNOWLEDGE_CONTEXT_LIMIT", "15"))