Update README.md

README.md

@@ -38,14 +38,33 @@ For each embedding model, the directory contains two key file:
 
 ## Data Fields
 
-- Centroids: `centroids.npy`
-    - This data is for finding the nearest clusters for IVF
-    - numpy array(`np.ndarray`) with 768-dimensional 32,768 centroids
-
-- Tree Metadata: `tree_info.pkl`
-    - This data is for finding the virtual cluster following tree structure
-    - keys: node_parents, leaf_ids, and leaf_to_centroid_idx
-    - Detailed parsing information is provided in `es2`
+### Centroids: `centroids.npy`
+
+- **Purpose**: Finding the nearest clusters for IVF (Inverted File Index)
+- **Type**: NumPy array (`np.ndarray`)
+- **Shape**: `[32768, 768]`
+- **Description**: 768-dimensional vectors representing 32,768 cluster centroids
+- **Normalization**: L2-normalized (unit norm)
+- **Format**: float32
+
+### Tree Metadata: `tree_info.pkl`
+
+- **Purpose**: Finding virtual clusters following hierarchical tree structure for efficient GAS search
+- **Type**: Python dictionary (pickle)
+- **Keys**:
+  - `node_parents`: Dictionary mapping each node ID to its parent node ID
+    - Format: `{node_id: parent_node_id, ...}`
+    - Contains parent-child relationships for all nodes in the tree
+  
+  - `leaf_ids`: List of leaf node IDs
+    - Format: `[leaf_id_1, leaf_id_2, ..., leaf_id_32768]`
+    - Total 32,768 leaf nodes (corresponding to 32,768 centroids)
+  
+  - `leaf_to_centroid_idx`: Mapping from leaf node IDs to centroid indices in `centroids.npy`
+    - Format: `{leaf_node_id: centroid_index, ...}`
+    - Maps each leaf node to its corresponding row index in `centroids.npy`
+    - Important: Leaf IDs in `leaf_ids` are ordered sequentially, so the i-th leaf corresponds to the i-th centroid
+
 
 ## Dataset Creation