remove numpy., scypy

requirements.txt

@@ -1,7 +1,5 @@
 fastapi[standard]==0.116.2
 uvicorn==0.35.0
-numpy==2.0.2
-scipy==1.16.3
 torch==2.8.0
 sentence-transformers==5.1.1
 loguru==0.7.3

vectordb/utils.py

@@ -1,77 +0,0 @@
-import numpy as np
-from scipy.sparse import csr_matrix
-from typing import Dict, List
-
-
-def convert_sparse_to_csr(sparse_dict: Dict[str, List]) -> csr_matrix:
-    """
-    Convert sparse embedding to scipy CSR matrix
-    
-    API format: {"indices": [10, 25, 42], "values": [0.85, 0.62, 0.91]}
-    Milvus format: scipy.sparse.csr_matrix with shape (1, max_dimension)
-    
-    Args:
-        sparse_dict: Dictionary with 'indices' and 'values'
-    
-    Returns:
-        scipy CSR matrix
-    """
-    indices = sparse_dict["indices"]
-    values = sparse_dict["values"]
-    
-    max_dim = max(indices) + 1 if indices else 1
-    
-    # Create CSR matrix
-    # Shape: (1, max_dim) karena ini single vector
-    row_indices = [0] * len(indices)  # Semua di row 0
-    col_indices = indices
-    
-    sparse_matrix = csr_matrix(
-        (values, (row_indices, col_indices)),
-        shape=(1, max_dim)
-    )
-    
-    return sparse_matrix
-
-
-def batch_convert_sparse_to_csr(sparse_list: List[Dict[str, List]]) -> csr_matrix:
-    """
-    Convert batch of sparse embeddings to single CSR matrix
-    
-    Args:
-        sparse_list: List of sparse dicts
-    
-    Returns:
-        scipy CSR matrix with shape (batch_size, max_dim)
-    """
-    if not sparse_list:
-        return csr_matrix((0, 0))
-    
-    max_dim = 0
-    for sparse_dict in sparse_list:
-        if sparse_dict["indices"]:
-            max_dim = max(max_dim, max(sparse_dict["indices"]) + 1)
-    
-    if max_dim == 0:
-        max_dim = 30000  # Default vocab size for SPLADE
-    
-    # Build row indices, column indices, and values
-    row_indices = []
-    col_indices = []
-    values = []
-    
-    for row_idx, sparse_dict in enumerate(sparse_list):
-        indices = sparse_dict["indices"]
-        vals = sparse_dict["values"]
-        
-        row_indices.extend([row_idx] * len(indices))
-        col_indices.extend(indices)
-        values.extend(vals)
-    
-    # Create CSR matrix
-    sparse_matrix = csr_matrix(
-        (values, (row_indices, col_indices)),
-        shape=(len(sparse_list), max_dim)
-    )
-    
-    return sparse_matrix