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/semantic/embedder.py

@@ -2,11 +2,57 @@
 
 from __future__ import annotations
 
-from typing import Iterable, List
+import threading
+from typing import Dict, Iterable, List, Optional
 
 from . import SEMANTIC_AVAILABLE
 
 
+# Global embedder cache for singleton pattern
+_embedder_cache: Dict[str, "Embedder"] = {}
+_cache_lock = threading.Lock()
+
+
+def get_embedder(profile: str = "code") -> "Embedder":
+    """Get or create a cached Embedder instance (thread-safe singleton).
+
+    This function provides significant performance improvement by reusing
+    Embedder instances across multiple searches, avoiding repeated model
+    loading overhead (~0.8s per load).
+
+    Args:
+        profile: Model profile ("fast", "code", "multilingual", "balanced")
+
+    Returns:
+        Cached Embedder instance for the given profile
+    """
+    global _embedder_cache
+
+    # Fast path: check cache without lock
+    if profile in _embedder_cache:
+        return _embedder_cache[profile]
+
+    # Slow path: acquire lock for initialization
+    with _cache_lock:
+        # Double-check after acquiring lock
+        if profile in _embedder_cache:
+            return _embedder_cache[profile]
+
+        # Create new embedder and cache it
+        embedder = Embedder(profile=profile)
+        # Pre-load model to ensure it's ready
+        embedder._load_model()
+        _embedder_cache[profile] = embedder
+        return embedder
+
+
+def clear_embedder_cache() -> None:
+    """Clear the embedder cache (useful for testing or memory management)."""
+    global _embedder_cache
+    with _cache_lock:
+        _embedder_cache.clear()
+
+
 class Embedder:
     """Generate embeddings for code chunks using fastembed (ONNX-based).