add progress bar in build

examples/wechat_history_reader_leann.py

@@ -224,7 +224,7 @@ async def query_leann_index(index_path: str, query: str):
         query,
         top_k=20,
         recompute_beighbor_embeddings=True,
-        complexity=128,
+        complexity=32,
         beam_width=1,
         llm_config={
             "type": "openai",
@@ -252,7 +252,7 @@ async def main():
     parser.add_argument(
         "--index-dir",
         type=str,
-        default="./wechat_history_june19_test",
+        default="./wechat_history_magic_test",
         help="Directory to store the LEANN index (default: ./wechat_history_index_leann_test)",
     )
     parser.add_argument(

packages/leann-core/src/leann/api.py

@@ -20,6 +20,7 @@ def compute_embeddings(
     mode: str = "sentence-transformers",
     use_server: bool = True,
     port: Optional[int] = None,
+    is_build=False,
 ) -> np.ndarray:
     """
     Computes embeddings using different backends.
@@ -51,6 +52,7 @@ def compute_embeddings(
             chunks,
             model_name,
             mode=mode,
+            is_build=is_build,
         )
 
 
@@ -209,6 +211,7 @@ class LeannBuilder:
             self.embedding_model,
             self.embedding_mode,
             use_server=False,
+            is_build=True,
         )
         string_ids = [chunk["id"] for chunk in self.chunks]
         current_backend_kwargs = {**self.backend_kwargs, "dimensions": self.dimensions}

packages/leann-core/src/leann/embedding_compute.py

@@ -13,7 +13,7 @@ logger = logging.getLogger(__name__)
 
 
 def compute_embeddings(
-    texts: List[str], model_name: str, mode: str = "sentence-transformers"
+    texts: List[str], model_name: str, mode: str = "sentence-transformers",is_build: bool = False
 ) -> np.ndarray:
     """
     Unified embedding computation entry point
@@ -27,7 +27,7 @@ def compute_embeddings(
         Normalized embeddings array, shape: (len(texts), embedding_dim)
     """
     if mode == "sentence-transformers":
-        return compute_embeddings_sentence_transformers(texts, model_name)
+        return compute_embeddings_sentence_transformers(texts, model_name, is_build=is_build)
     elif mode == "openai":
         return compute_embeddings_openai(texts, model_name)
     elif mode == "mlx":
@@ -42,6 +42,7 @@ def compute_embeddings_sentence_transformers(
     use_fp16: bool = True,
     device: str = "auto",
     batch_size: int = 32,
+    is_build: bool = False,
 ) -> np.ndarray:
     """
     Compute embeddings using SentenceTransformer
@@ -133,7 +134,7 @@ def compute_embeddings_sentence_transformers(
     embeddings = model.encode(
         texts,
         batch_size=batch_size,
-        show_progress_bar=False,  # Don't show progress bar in server environment
+        show_progress_bar=is_build,  # Don't show progress bar in server environment
         convert_to_numpy=True,
         normalize_embeddings=False,  # Keep consistent with original API behavior
         device=device,