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Version: User Guides (Cloud)

String Field

In Zilliz Cloud clusters, VARCHAR is the data type used for storing string data. When you define a VARCHAR field, two parameters are mandatory:

  • Set the datatype to DataType.VARCHAR.

  • Specify the max_length, which defines the maximum number of characters the VARCHAR field can store. The valid range for max_length is from 1 to 65,535.

📘Notes

Zilliz Cloud supports null values and default values for VARCHAR fields. To enable these features, set nullable to True and default_value to a string value. For details, refer to Nullable & Default.

Add VARCHAR field​

To store string data in Zilliz Cloud clusters, define a VARCHAR field in your collection schema. Below is an example of defining a collection schema with two VARCHAR fields:

  • varchar_field1: stores up to 100 characters, allows null values, and has a default value of "Unknown".

  • varchar_field2: stores up to 200 characters, allows null values, but does not have a default value.

📘Notes

If you set enable_dynamic_fields=True when defining the schema, Zilliz Cloud allows you to insert scalar fields that were not defined in advance. However, this may increase the complexity of queries and management, potentially impacting performance. For more information, refer to Dynamic Field.

# Import necessary libraries
from pymilvus import MilvusClient, DataType

# Define server address
SERVER_ADDR = "YOUR_CLUSTER_ENDPOINT"

# Create a MilvusClient instance
client = MilvusClient(uri=SERVER_ADDR)

# Define the collection schema
schema = client.create_schema(
    auto_id=False,
    enable_dynamic_fields=True,
)

# Add `varchar_field1` that supports null values with default value "Unknown"
schema.add_field(field_name="varchar_field1", datatype=DataType.VARCHAR, max_length=100, nullable=True, default_value="Unknown")
# Add `varchar_field2` that supports null values without default value
schema.add_field(field_name="varchar_field2", datatype=DataType.VARCHAR, max_length=200, nullable=True)
schema.add_field(field_name="pk", datatype=DataType.INT64, is_primary=True)
schema.add_field(field_name="embedding", datatype=DataType.FLOAT_VECTOR, dim=3)

Set index params​

Indexing helps improve search and query performance. In Zilliz Cloud clusters, indexing is mandatory for vector fields but optional for scalar fields.

The following example creates indexes on the vector field embedding and the scalar field varchar_field1, both using the AUTOINDEX index type. With this type, Milvus automatically selects the most suitable index based on the data type.

# Set index params

index_params = client.prepare_index_params()

# Index `varchar_field1` with AUTOINDEX
index_params.add_index(
    field_name="varchar_field1",
    index_type="AUTOINDEX",
    index_name="varchar_index"
)

# Index `embedding` with AUTOINDEX and specify metric_type
index_params.add_index(
    field_name="embedding",
    index_type="AUTOINDEX",  # Use automatic indexing to simplify complex index settings
    metric_type="COSINE"  # Specify similarity metric type, options include L2, COSINE, or IP
)

Create collection​

Once the schema and index are defined, create a collection that includes string fields.

# Create Collection
client.create_collection(
    collection_name="my_varchar_collection",
    schema=schema,
    index_params=index_params
)

Insert data​

After creating the collection, insert entities that match the schema.

# Sample data
data = [
    {"varchar_field1": "Product A", "varchar_field2": "High quality product", "pk": 1, "embedding": [0.1, 0.2, 0.3]},
    {"varchar_field1": "Product B", "pk": 2, "embedding": [0.4, 0.5, 0.6]}, # varchar_field2 field is missing, which should be NULL
    {"varchar_field1": None, "varchar_field2": None, "pk": 3, "embedding": [0.2, 0.3, 0.1]},  # `varchar_field1` should default to `Unknown`, `varchar_field2` is NULL
    {"varchar_field1": "Product C", "varchar_field2": None, "pk": 4, "embedding": [0.5, 0.7, 0.2]},  # `varchar_field2` is NULL
    {"varchar_field1": None, "varchar_field2": "Exclusive deal", "pk": 5, "embedding": [0.6, 0.4, 0.8]},  # `varchar_field1` should default to `Unknown`
    {"varchar_field1": "Unknown", "varchar_field2": None, "pk": 6, "embedding": [0.8, 0.5, 0.3]},  # `varchar_field2` is NULL
    {"varchar_field1": "", "varchar_field2": "Best seller", "pk": 7, "embedding": [0.8, 0.5, 0.3]}, # Empty string is not treated as NULL
]

# Insert data
client.insert(
    collection_name="my_varchar_collection",
    data=data
)

Query with filter expressions​

After inserting entities, use the query method to retrieve entities that match the specified filter expressions.

To retrieve entities where the varchar_field1 matches the string "Product A":

# Filter `varchar_field1` with value "Product A"
filter = 'varchar_field1 == "Product A"'

res = client.query(
    collection_name="my_varchar_collection",
    filter=filter,
    output_fields=["varchar_field1", "varchar_field2"]
)

print(res)

# Example output:
# data: [
#     "{'varchar_field1': 'Product A', 'varchar_field2': 'High quality product', 'pk': 1}"
# ]

To retrieve entities where the varchar_field2 is null:

# Filter entities where `varchar_field2` is null
filter = 'varchar_field2 is null'

res = client.query(
    collection_name="my_varchar_collection",
    filter=filter,
    output_fields=["varchar_field1", "varchar_field2"]
)

print(res)

# Example output:
# data: [
#     "{'varchar_field1': 'Product B', 'varchar_field2': None, 'pk': 2}",
#     "{'varchar_field1': 'Unknown', 'varchar_field2': None, 'pk': 3}",
#     "{'varchar_field1': 'Product C', 'varchar_field2': None, 'pk': 4}",
#     "{'varchar_field1': 'Unknown', 'varchar_field2': None, 'pk': 6}"
# ]

To retrieve entities where varchar_field1 has the value "Unknown", use the following expression below. As the default value of varchar_field1 is "Unknown", the expected result should include entities with varchar_field1 explicitly set to "Unknown" or with varchar_field1 set to null.

# Filter entities with `varchar_field1` with value `Unknown`
filter = 'varchar_field1 == "Unknown"'

res = client.query(
    collection_name="my_varchar_collection",
    filter=filter,
    output_fields=["varchar_field1", "varchar_field2"]
)

print(res)

# Example output:
# data: [
#     "{'varchar_field1': 'Unknown', 'varchar_field2': None, 'pk': 3}",
#     "{'varchar_field1': 'Unknown', 'varchar_field2': 'Exclusive deal', 'pk': 5}",
#     "{'varchar_field1': 'Unknown', 'varchar_field2': None, 'pk': 6}"
# ]

Vector search with filter expressions​

In addition to basic scalar field filtering, you can combine vector similarity searches with scalar field filters. For example, the following code shows how to add a scalar field filter to a vector search:

# Search with string filtering

# Filter `varchar_field2` with value "Best seller"
filter = 'varchar_field2 == "Best seller"'

res = client.search(
    collection_name="my_varchar_collection",
    data=[[0.3, -0.6, 0.1]],
    limit=5,
    search_params={"params": {"nprobe": 10}},
    output_fields=["varchar_field1", "varchar_field2"],
    filter=filter
)

print(res)

# Example output:
# data: [
#     "[{'id': 7, 'distance': -0.04468163847923279, 'entity': {'varchar_field1': '', 'varchar_field2': 'Best seller'}}]"
# ]