diff --git a/ccw/src/tools/codex-lens.ts b/ccw/src/tools/codex-lens.ts
index 1c7ba261..3088fb0c 100644
--- a/ccw/src/tools/codex-lens.ts
+++ b/ccw/src/tools/codex-lens.ts
@@ -410,9 +410,8 @@ function parseProgressLine(line: string): ProgressInfo | null {
   if (line.includes('Generating embeddings') || line.includes('Creating embeddings')) {
     return { stage: 'embeddings', message: 'Generating embeddings...', percent: 70 };
   }
-  if (line.includes('Finalizing') || line.includes('Complete')) {
-    return { stage: 'complete', message: 'Finalizing...', percent: 95 };
-  }
+  // Note: "Finalizing index" and "Building ANN" are handled separately below
+  // Only match generic "Complete" here (not "Finalizing" which has specific handlers)
 
   // Parse indexed count: "Indexed X files" - FTS complete, but embeddings may follow
   const indexedMatch = line.match(/Indexed (\d+) files/i);
@@ -460,6 +459,11 @@ function parseProgressLine(line: string): ProgressInfo | null {
     };
   }
 
+  // Parse generic completion (but not "Embeddings complete" which is handled above)
+  if (line.includes('Complete') && !line.toLowerCase().includes('embeddings complete')) {
+    return { stage: 'complete', message: 'Complete', percent: 98 };
+  }
+
   return null;
 }
 
diff --git a/codex-lens/src/codexlens/cli/embedding_manager.py b/codex-lens/src/codexlens/cli/embedding_manager.py
index ba4ec45a..03263325 100644
--- a/codex-lens/src/codexlens/cli/embedding_manager.py
+++ b/codex-lens/src/codexlens/cli/embedding_manager.py
@@ -18,8 +18,9 @@ except ImportError:
 
 logger = logging.getLogger(__name__)
 
-# Periodic embedder recreation interval to prevent memory accumulation
-EMBEDDER_RECREATION_INTERVAL = 10  # Recreate embedder every N batches
+# Embedding batch size - larger values improve throughput on modern hardware
+# Default 64 balances memory usage and GPU/CPU utilization
+EMBEDDING_BATCH_SIZE = 64  # Increased from 8 for better performance
 
 
 def _get_path_column(conn: sqlite3.Connection) -> str:
@@ -196,13 +197,12 @@ def generate_embeddings(
 
     # Initialize components
     try:
-        # Initialize embedder (will be periodically recreated to prevent memory leaks)
+        # Initialize embedder (singleton, reused throughout the function)
         embedder = get_embedder(profile=model_profile)
         chunker = Chunker(config=ChunkConfig(max_chunk_size=chunk_size))
 
         if progress_callback:
             progress_callback(f"Using model: {embedder.model_name} ({embedder.embedding_dim} dimensions)")
-            progress_callback(f"Memory optimization: Embedder will be recreated every {EMBEDDER_RECREATION_INTERVAL} batches")
 
     except Exception as e:
         return {
@@ -210,15 +210,14 @@ def generate_embeddings(
             "error": f"Failed to initialize components: {str(e)}",
         }
 
-    # --- MEMORY-OPTIMIZED STREAMING PROCESSING ---
-    # Process files in small batches to control memory usage
-    # This keeps peak memory under 2GB regardless of project size
+    # --- STREAMING PROCESSING ---
+    # Process files in batches to control memory usage
     start_time = time.time()
     failed_files = []
     total_chunks_created = 0
     total_files_processed = 0
     FILE_BATCH_SIZE = 100  # Process 100 files at a time
-    EMBEDDING_BATCH_SIZE = 8  # jina-embeddings-v2-base-code needs small batches
+    # EMBEDDING_BATCH_SIZE is defined at module level (default: 64)
 
     try:
         with VectorStore(index_path) as vector_store:
@@ -251,14 +250,6 @@ def generate_embeddings(
                         batch_chunks_with_paths = []
                         files_in_batch_with_chunks = set()
 
-                        # Periodic embedder recreation to prevent memory accumulation
-                        if batch_number % EMBEDDER_RECREATION_INTERVAL == 0:
-                            if progress_callback:
-                                progress_callback(f"  [Memory optimization] Recreating embedder at batch {batch_number}")
-                            clear_embedder_cache()
-                            embedder = get_embedder(profile=model_profile)
-                            gc.collect()
-
                         # Step 1: Chunking for the current file batch
                         for file_row in file_batch:
                             file_path = file_row[path_column]
@@ -317,9 +308,8 @@ def generate_embeddings(
                             logger.error(f"Failed to store batch {batch_number}: {str(e)}")
                             failed_files.extend([(file_row[path_column], str(e)) for file_row in file_batch])
 
-                        # Explicit memory cleanup after each batch
+                        # Release batch references (let Python GC handle cleanup naturally)
                         del batch_chunks_with_paths, batch_embeddings
-                        gc.collect()
 
                 # Notify before ANN index finalization (happens when bulk_insert context exits)
                 if progress_callback: