fix: update default embedding models for better performance

- Change WeChat, Browser, and Email RAG examples to use all-MiniLM-L6-v2
- Previous Qwen/Qwen3-Embedding-0.6B was too slow for these use cases
- all-MiniLM-L6-v2 is a fast 384-dim model, ideal for large-scale personal data

examples/browser_rag.py

@@ -18,6 +18,11 @@ class BrowserRAG(BaseRAGExample):
     """RAG example for Chrome browser history."""
 
     def __init__(self):
+        # Set default values BEFORE calling super().__init__
+        self.embedding_model_default = (
+            "sentence-transformers/all-MiniLM-L6-v2"  # Fast 384-dim model
+        )
+
         super().__init__(
             name="Browser History",
             description="Process and query Chrome browser history with LEANN",

examples/email_rag.py

@@ -17,6 +17,11 @@ class EmailRAG(BaseRAGExample):
     """RAG example for Apple Mail processing."""
 
     def __init__(self):
+        # Set default values BEFORE calling super().__init__
+        self.embedding_model_default = (
+            "sentence-transformers/all-MiniLM-L6-v2"  # Fast 384-dim model
+        )
+
         super().__init__(
             name="Email",
             description="Process and query Apple Mail emails with LEANN",

examples/wechat_rag.py

@@ -20,7 +20,9 @@ class WeChatRAG(BaseRAGExample):
     def __init__(self):
         # Set default values BEFORE calling super().__init__
         self.max_items_default = 50  # Match original default
-        self.embedding_model_default = "Qwen/Qwen3-Embedding-0.6B"  # Match original default
+        self.embedding_model_default = (
+            "sentence-transformers/all-MiniLM-L6-v2"  # Fast 384-dim model
+        )
 
         super().__init__(
             name="WeChat History",