normalize vector data to a standard form during insertion (#27469)

Author: CaoHaiNam

examples/example_normalization_fields.py

@@ -0,0 +1,112 @@
+import time
+
+import numpy as np
+from pymilvus import (
+    connections,
+    utility,
+    FieldSchema, CollectionSchema, DataType,
+    Collection,
+    MilvusClient
+)
+
+fmt = "\n=== {:30} ===\n"
+search_latency_fmt = "search latency = {:.4f}s"
+num_entities, dim = 3000, 8
+
+
+print(fmt.format("start connecting to Milvus"))
+# this is milvus standalone
+connection = connections.connect(
+  alias="default", 
+  host='localhost', # or '0.0.0.0' or 'localhost'
+  port='19530'
+)
+
+client = MilvusClient(connections=connection)
+
+has = utility.has_collection("hello_milvus")
+print(f"Does collection hello_milvus exist in Milvus: {has}")
+if has:
+    utility.drop_collection("hello_milvus")
+
+fields = [
+    FieldSchema(name="pk", dtype=DataType.VARCHAR, is_primary=True, auto_id=False, max_length=100),
+    FieldSchema(name="random", dtype=DataType.DOUBLE),
+    FieldSchema(name="embeddings1", dtype=DataType.FLOAT_VECTOR, dim=dim),
+    FieldSchema(name="embeddings2", dtype=DataType.FLOAT_VECTOR, dim=dim)
+]
+
+schema = CollectionSchema(fields, "hello_milvus is the simplest demo to introduce the APIs")
+
+print(fmt.format("Create collection `hello_milvus`"))
+
+print(fmt.format("Message for handling an invalid format in the normalization_fields value")) # you can try with other value like: dict,...
+try:
+    hello_milvus = Collection("hello_milvus", schema, consistency_level="Strong", normalization_fields='embeddings1')
+except BaseException as e:
+    print(e)
+    
+
+print(fmt.format("Message for handling the invalid vector fields"))
+try:
+    hello_milvus = Collection("hello_milvus", schema, consistency_level="Strong", normalization_fields=['embddings'])
+except BaseException as e:
+    print(e)
+    
+print(fmt.format("Insert data, with conversion to standard form"))
+
+hello_milvus = Collection("hello_milvus", schema, consistency_level="Strong", normalization_fields=['embeddings1'])
+
+print(fmt.format("Start inserting a row"))
+rng = np.random.default_rng(seed=19530)
+
+row = {
+    "pk": "19530",
+    "random": 0.5,
+    "embeddings1": rng.random((1, dim), np.float32)[0],
+    "embeddings2": rng.random((1, dim), np.float32)[0]
+}
+_row = row.copy()
+hello_milvus.insert(row)
+
+index_param = {"index_type": "FLAT", "metric_type": "L2", "params": {}}
+hello_milvus.create_index("embeddings1", index_param)
+hello_milvus.create_index("embeddings2", index_param)
+hello_milvus.load()
+
+original_vector = _row['embeddings1']
+insert_vector = hello_milvus.query(
+    expr="pk == '19530'",
+    output_fields=["embeddings1"],
+)[0]['embeddings1']
+
+print(fmt.format("Mean and standard deviation before normalization."))
+print("Mean: ", np.mean(original_vector))
+print("Std: ", np.std(original_vector))
+
+print(fmt.format("Mean and standard deviation after normalization."))
+print("Mean: ", np.mean(insert_vector))
+print("Std: ", np.std(insert_vector))
+
+
+print(fmt.format("Start inserting entities"))
+
+entities = [
+    [str(i) for i in range(num_entities)],
+    rng.random(num_entities).tolist(),
+    rng.random((num_entities, dim), np.float32),
+    rng.random((num_entities, dim), np.float32),
+]
+
+insert_result = hello_milvus.insert(entities)
+
+insert_vector = hello_milvus.query(
+    expr="pk == '1'",
+    output_fields=["embeddings1"],
+)[0]['embeddings1']
+
+print(fmt.format("Mean and standard deviation after normalization."))
+print("Mean: ", np.mean(insert_vector))
+print("Std: ", np.std(insert_vector))
+
+utility.drop_collection("hello_milvus")

pymilvus/client/utils.py

@@ -10,6 +10,7 @@
 
 from .constants import LOGICAL_BITS, LOGICAL_BITS_MASK
 from .types import DataType
+import numpy as np
 
 MILVUS = "milvus"
 ZILLIZ = "zilliz"
@@ -375,3 +376,23 @@ def is_scipy_sparse(cls, data: Any):
     "csr_array",
     "spmatrix",
 ]
+
+def convert_to_standard_form(vector_data):
+
+    if len(vector_data.shape) == 1:
+        # Calculate the mean and standard deviation of the vector
+        mean = np.mean(vector_data)
+        std_dev = np.std(vector_data)
+        
+        # Standardize the vector
+        standardized_vector = (vector_data - mean) / std_dev if std_dev != 0 else vector_data
+        return standardized_vector
+    
+    else:
+        # Calculate mean and standard deviation for each row
+        row_means = np.mean(vector_data, axis=1, keepdims=True)
+        row_stds = np.std(vector_data, axis=1, keepdims=True)
+        
+        # Standardize each row independently
+        standardized_matrix = np.where(row_stds != 0, (vector_data - row_means) / row_stds, vector_data)
+        return standardized_matrix

pymilvus/exceptions.py

@@ -257,3 +257,7 @@ class ExceptionsMessage:
     DefaultValueInvalid = (
         "Default value cannot be None for a field that is defined as nullable == false."
     )
+    InvalidVectorFields = (
+        "%s is not a valid vector field; expected %s"
+    )
+    InvalidNormalizationParam = "Unexpected normalization_fields parameters. Expected 'all' or a list of fields (e.g., [field1, field2, ...]), but got %s."

pymilvus/orm/collection.py

@@ -156,6 +156,25 @@ def __init__(
         self._schema_dict = self._schema.to_dict()
         self._schema_dict["consistency_level"] = self._consistency_level
 
+        self._normalization_fields = kwargs.get("normalization_fields", None)
+        if self._normalization_fields:
+            self._vector_fields = self._get_vector_fields()
+            if self._normalization_fields == 'all':
+                self._normalization_fields = self._vector_fields
+            elif isinstance(self._normalization_fields, list):
+                for field in self._normalization_fields:
+                    if field not in self._vector_fields:
+                        raise BaseException(ExceptionsMessage.InvalidVectorFields % (field, ', '.join(self._vector_fields)))
+            else:
+                raise BaseException(ExceptionsMessage.InvalidNormalizationParam % (self._normalization_fields))
+        
+    def _get_vector_fields(self):
+        vector_fields = []
+        for field in self._schema_dict.get("fields", []):
+            if field.get("params", {}).get("dim", None):
+                vector_fields.append(field.get("name"))
+        return vector_fields
+
     def __repr__(self) -> str:
         _dict = {
             "name": self.name,
@@ -504,6 +523,9 @@ def insert(
 
         conn = self._get_connection()
         if is_row_based(data):
+            if self._normalization_fields:
+                for norm_fld in self._normalization_fields:
+                    data[norm_fld] = utils.convert_to_standard_form(data[norm_fld])
             return conn.insert_rows(
                 collection_name=self._name,
                 entities=data,
@@ -513,6 +535,9 @@ def insert(
                 **kwargs,
             )
 
+        for idx, fld in enumerate(self._schema_dict['fields']):
+            if fld['name'] in self._normalization_fields:
+                data[idx] = utils.convert_to_standard_form(data[idx])
         check_insert_schema(self.schema, data)
         entities = Prepare.prepare_data(data, self.schema)
         return conn.batch_insert(