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/db/database.py

@@ -24,6 +24,7 @@ _ADDED_COLUMNS = [
     ("knowledge_entries", "headline", "VARCHAR"),
     ("messages", "archived_at", "DATETIME"),
     ("messages", "memory_id", "INTEGER"),
+    ("local_knowledge_docs", "embedding_model", "VARCHAR"),
 ]