feat: add managed Granite embedding models

GoodBuddy previously required an external OpenAI-compatible endpoint for vector retrieval. It can now install, verify, import, remove, and run the built-in Granite multilingual embedding model in an isolated local inference process, while retaining custom embedding connections and migrating existing settings.

The model connection UI now manages built-in and custom vector connections consistently, reports the actual retrieval path, preserves credentials across failed saves, and renders the vector tab synchronously. Related settings copy, switch styling, metadata typography, and renderer bundle guardrails are aligned with the resulting workflow.

Release note: 新增可管理的内置 Granite 多语言向量模型，支持本地下载、离线导入、连接切换与诊断；同时优化向量设置及相关界面的文案、开关和状态显示。

AGENTS.md

@@ -89,6 +89,11 @@ Keep Electron security boundaries intact:
   blocking page state, or an error with an immediate local recovery action.
 - Do not show the same event both inline and as an application notification.
   Preserve user input and actionable error context when an operation fails.
+- Treat renderer bundle budgets as regression guardrails, not UX targets. Do
+  not lazy-load one lightweight peer tab solely to satisfy a byte ceiling.
+  When a genuinely heavy view needs deferred loading, preserve peer
+  interaction semantics, render a non-empty layout-stable local fallback,
+  and test its first interaction rather than only its eventual appearance.
 
 ## Commit Messages and Release Notes