diff --git a/packages/leann-core/src/leann/embedding_compute.py b/packages/leann-core/src/leann/embedding_compute.py
index 654c7264..6ff5d8bc 100644
--- a/packages/leann-core/src/leann/embedding_compute.py
+++ b/packages/leann-core/src/leann/embedding_compute.py
@@ -481,9 +481,10 @@ def compute_embeddings(
             base_url=provider_options.get("base_url"),
             api_key=provider_options.get("api_key"),
             provider_options=provider_options,
+            is_build=is_build,
         )
     elif mode == "mlx":
-        return compute_embeddings_mlx(texts, model_name)
+        return compute_embeddings_mlx(texts, model_name, is_build=is_build)
     elif mode == "ollama":
         return compute_embeddings_ollama(
             texts,
@@ -848,8 +849,8 @@ def compute_embeddings_openai(
     base_url: Optional[str] = None,
     api_key: Optional[str] = None,
     provider_options: Optional[dict[str, Any]] = None,
+    is_build: bool = False,
 ) -> np.ndarray:
-    # TODO: @yichuan-w add progress bar only in build mode
     """Compute embeddings using OpenAI API"""
     try:
         import openai
@@ -936,9 +937,12 @@ def compute_embeddings_openai(
 
         total_batches = (len(texts) + max_batch_size - 1) // max_batch_size
         batch_range = range(0, len(texts), max_batch_size)
-        batch_iterator = tqdm(
-            batch_range, desc="Computing embeddings", unit="batch", total=total_batches
-        )
+        if is_build:
+            batch_iterator = tqdm(
+                batch_range, desc="Computing embeddings", unit="batch", total=total_batches
+            )
+        else:
+            batch_iterator = batch_range
     except ImportError:
         # Fallback when tqdm is not available
         batch_iterator = range(0, len(texts), max_batch_size)
@@ -967,8 +971,9 @@ def compute_embeddings_openai(
     return embeddings
 
 
-def compute_embeddings_mlx(chunks: list[str], model_name: str, batch_size: int = 16) -> np.ndarray:
-    # TODO: @yichuan-w add progress bar only in build mode
+def compute_embeddings_mlx(
+    chunks: list[str], model_name: str, batch_size: int = 16, is_build: bool = False
+) -> np.ndarray:
     """Computes embeddings using an MLX model."""
     try:
         import mlx.core as mx
@@ -999,9 +1004,11 @@ def compute_embeddings_mlx(chunks: list[str], model_name: str, batch_size: int =
     try:
         from tqdm import tqdm
 
-        batch_iterator = tqdm(
-            range(0, len(chunks), batch_size), desc="Computing embeddings", unit="batch"
-        )
+        batch_range = range(0, len(chunks), batch_size)
+        if is_build:
+            batch_iterator = tqdm(batch_range, desc="Computing embeddings", unit="batch")
+        else:
+            batch_iterator = batch_range
     except ImportError:
         batch_iterator = range(0, len(chunks), batch_size)