update description

Author: euphoria0-0

README.md

@@ -28,7 +28,7 @@ pip install pyenvector==1.3.0a1
 3. Run Benchmark
 
 ```bash
-# Run Benchmark
+# Run Benchmark (VectorDBBench built-in dataset)
 ./scripts/run_benchmark.sh --index-type FLAT --config-file envector_openai_config.yml
 ```
 
@@ -154,7 +154,7 @@ For the ANN benchmark, we provide two datasets via HuggingFace:
 - `FASHION512D200K`
 - `FOOD512D75K`
 
-Also, we provide centroids and tree metadata for the corresponding embedding model used in the ANN benchmark:
+Also, we provide centroids for the corresponding embedding model used in the ANN benchmark:
 - GAS Centroids: [cryptolab-playground/gas-centroids](https://huggingface.co/datasets/cryptolab-playground/gas-centroids)
 
 To prepare dataset, run the following command as example:
@@ -254,6 +254,35 @@ follows conventions of VectorDBBench,
 see details in [VectorDBBench Options](https://github.com/zilliztech/VectorDBBench?tab=readme-ov-file#custom-dataset-for-performance-case).
 For example, if you have a custom directory for dataset, set `DATASET_LOCAL_DIR`.
 
+### enVector VectorDBBench Config File Options
+
+You can file the customized config files in `vectordb_bench/config-files` to use CLI options in more convinient way.
+
+```yaml
+# FLAT
+envectorflat:
+  index_name: test_index
+  uri: localhost:50050
+  eval_mode: mm
+  case_type: Performance1536D500K
+  db_label: Performance1536D500K-FLAT
+  k: 10
+  drop_old: true
+  load: true
+
+# IVF-FLAT with trained k-means centroids
+envectorivfflat:
+  ...
+  nlist: 256
+  nprobe: 6
+  train_centroids: true
+  centroids_path: centroids/performance1536d500k/centroids_256.npy
+
+# GAS: enVector-customized ANN
+envectorivfgas:
+  ...
+```
+
 
 ## ❓ Troubleshooting
 

scripts/prepare_dataset.py

@@ -44,7 +44,7 @@ def get_args():
         "--centroids-dir",
         type=str,
         default="./centroids",
-        help="Directory to save the centroids and tree info.",
+        help="Directory to save the centroids.",
     )
     return parser.parse_args()
 
@@ -92,7 +92,7 @@ def prepare_neighbors(
 
 
 def download_centroids(embedding_model: str, dataset_dir: str) -> None:
-    """Download pre-computed centroids and tree info for GAS VCT index."""
+    """Download pre-computed centroids and for IVF_GAS index."""
 
     if embedding_model != "embeddinggemma-300m":
         raise ValueError(f"Centroids for {embedding_model} currently not available.")
@@ -103,8 +103,7 @@ def download_centroids(embedding_model: str, dataset_dir: str) -> None:
     # download
     os.makedirs(os.path.join(dataset_dir, embedding_model), exist_ok=True)
     wget.download(f"{dataset_link}/centroids.npy", out=os.path.join(dataset_dir, embedding_model, "centroids.npy"))
-    wget.download(f"{dataset_link}/tree_info.pkl", out=os.path.join(dataset_dir, embedding_model, "tree_info.pkl"))
-    print(f"\nDownloaded centroids and tree info to {os.path.join(dataset_dir, embedding_model)}")
+    print(f"\nDownloaded centroids to {os.path.join(dataset_dir, embedding_model)}")
 
 
 if __name__ == "__main__":