Refactor search modes and optimize embedding generation

- Updated the dashboard template to hide the Code Graph Explorer feature.
- Enhanced the `executeCodexLens` function to use `exec` for better cross-platform compatibility and improved command execution.
- Changed the default `maxResults` and `limit` parameters in the smart search tool to 10 for better performance.
- Introduced a new `priority` search mode in the smart search tool, replacing the previous `parallel` mode, which now follows a fallback strategy: hybrid -> exact -> ripgrep.
- Optimized the embedding generation process in the embedding manager by batching operations and using a cached embedder instance to reduce model loading overhead.
- Implemented a thread-safe singleton pattern for the embedder to improve performance across multiple searches.

codex-lens/src/codexlens/search/hybrid_search.py

@@ -257,7 +257,7 @@ class HybridSearchEngine:
                 return []
 
             # Initialize embedder and vector store
-            from codexlens.semantic.embedder import Embedder
+            from codexlens.semantic.embedder import get_embedder
             from codexlens.semantic.vector_store import VectorStore
 
             vector_store = VectorStore(index_path)
@@ -285,7 +285,8 @@ class HybridSearchEngine:
             else:
                 profile = "code"  # Default fallback
 
-            embedder = Embedder(profile=profile)
+            # Use cached embedder (singleton) for performance
+            embedder = get_embedder(profile=profile)
 
             # Generate query embedding
             query_embedding = embedder.embed_single(query)