Schema

This topic introduces schema in Milvus. Schema is used to define the properties of a collection and the fields within.

Field schema

A field schema is the logical definition of a field. It is the first thing you need to define before defining a collection schema and creating a collection.

Milvus 2.0 supports only one primary key field in a collection.

Field schema properties

Properties	Description	Note
name	Name of the field in the collection to create	Data type: String. Mandatory
dtype	Data type of the field	Mandatory
description	Description of the field	Data type: String. Optional
is_primary	Whether to set the field as the primary key field or not	Data type: Boolean (true or false). Mandatory for the primary key field
dim	Dimension of the vector	Data type: Integer ∈[1, 32768]. Mandatory for the vector field

Create a field schema

from pymilvus import FieldSchema
id_field = FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, description="primary id")
age_field = FieldSchema(name="age", dtype=DataType.INT64, description="age")
embedding_field = FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=128, description="vector")

Supported data type

DataType defines the kind of data a field contains. Different fields support different data types.

• Primary key field supports:
  • INT64: numpy.int64
• Scalar field supports:
  • BOOL: Boolean (true or false)
  • INT8: numpy.int8
  • INT16: numpy.int16
  • INT32: numpy.int32
  • INT64: numpy.int64
  • FLOAT: numpy.float32
  • DOUBLE: numpy.double
• Vector field supports:
  • BINARY_VECTOR: Binary vector
  • FLOAT_VECTOR: Float vector

Collection schema

A collection schema is the logical definition of a collection. Usually you need to define the field schema before defining a collection schema and creating a collection.

Collection schema properties

Properties	Description	Note
field	Fields in the collection to create	Mandatory
description	Description of the collection	Data type: String. Optional
auto_id	Whether to enable Automatic ID (primary key) allocation or not	Data type: Boolean (true or false). Optional

Create a collection schema

from pymilvus import FieldSchema, CollectionSchema
id_field = FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, description="primary id")
age_field = FieldSchema(name="age", dtype=DataType.INT64, description="age")
embedding_field = FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=128, description="vector")
schema = CollectionSchema(fields=[id_field, age_field, embedding_field], auto_id=False, description="desc of a collection")

Create a collection with the schema specified:

from pymilvus import Collection
collection_name1 = "tutorial_1"
collection1 = Collection(name=collection_name1, schema=schema, using='default', shards_num=2)

You can also create a collection with Collection.construct_from_dataframe, which automatically generates a collection schema from DataFrame and creates a collection.

import pandas as pd
df = pd.DataFrame({
        "id": [i for i in range(nb)],
        "age": [random.randint(20, 40) for i in range(nb)],
        "embedding": [[random.random() for _ in range(dim)] for _ in range(nb)]
    })
collection, ins_res = Collection.construct_from_dataframe(
                                'my_collection',
                                df,
                                primary_field='id',
                                auto_id=False
                                )