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Data types

Introduction​

When creating a property, you must specify a data type. Weaviate accepts the following types.

Array types

Arrays of a data type are specified by adding [] to the type (e.g. text ➡ text[]). Note that not all data types support arrays.

NameExact typeFormattingArray ([]) available (example)Note
textstringstring✅ ["string one", "string two"]
booleanbooleantrue/false✅ [true, false]
intint64 (see notes)123✅ [123, -456]
numberfloat640.0✅ [0.0, 1.1]
datestringmore info✅
uuidstring"c8f8176c-6f9b-5461-8ab3-f3c7ce8c2f5c"✅ ["c8f8176c-6f9b-5461-8ab3-f3c7ce8c2f5c", "36ddd591-2dee-4e7e-a3cc-eb86d30a4303"]
geoCoordinatesstringmore info❌
phoneNumberstringmore info❌
blobbase64 encoded stringmore info❌
objectobject{"child": "I'm nested!"}✅ [{"child": "I'm nested!"}, {"child": "I'm nested too!"}Available from 1.22
cross referencestringmore info❌
Deprecated types
NameExact typeFormattingArray available (example)Deprecated from
stringstring"string"✅ ["string", "second string"]v1.19

Further details on each data type are provided below.

text​

Use this type for any text data.

string is deprecated

Prior to v1.19, Weaviate supported an additional datatype string, which was differentiated by tokenization behavior to text. As of v1.19, this type is deprecated and will be removed in a future release.

Use text instead of string. text supports the tokenization options that are available through string.

Examples​

Property definition​

from weaviate.classes.config import Property, DataType, Configure, Tokenization

# Create collection
my_collection = client.collections.create(
    name="Movie",
    properties=[
        Property(
            name="title", data_type=DataType.TEXT, tokenization=Tokenization.LOWERCASE
        ),
        Property(
            name="movie_id", data_type=DataType.TEXT, tokenization=Tokenization.FIELD
        ),
        Property(
            name="genres", data_type=DataType.TEXT_ARRAY, tokenization=Tokenization.WORD
        ),
    ],
    # Other properties are omitted for brevity
)
py docs  API docs

Object insertion​

# Create an object
example_object = {
    "title": "Rogue One",
    "movie_id": "ro123456",
    "genres": ["Action", "Adventure", "Sci-Fi"],
}

obj_uuid = my_collection.data.insert(example_object)
py docs  API docs

boolean / int / number​

The boolean, int, and number types are used for storing boolean, integer, and floating-point numbers, respectively.

Examples​

Property definition​

from weaviate.classes.config import Property, DataType

# Create collection
my_collection = client.collections.create(
    name="Product",
    properties=[
        Property(name="name", data_type=DataType.TEXT),
        Property(name="price", data_type=DataType.NUMBER),
        Property(name="stock_quantity", data_type=DataType.INT),
        Property(name="is_on_sale", data_type=DataType.BOOL),
        Property(name="customer_ratings", data_type=DataType.NUMBER_ARRAY),
    ],
    # Other properties are omitted for brevity
)
py docs  API docs

Object insertion​

# Create an object
example_object = {
    "name": "Wireless Headphones",
    "price": 95.50,
    "stock_quantity": 100,
    "is_on_sale": True,
    "customer_ratings": [4.5, 4.8, 4.2],
}

obj_uuid = my_collection.data.insert(example_object)
py docs  API docs

Note: GraphQL and int64​

Although Weaviate supports int64, GraphQL currently only supports int32, and does not support int64. This means that currently integer data fields in Weaviate with integer values larger than int32, will not be returned using GraphQL queries. We are working on solving this issue. As current workaround is to use a string instead.

date​

A date in Weaviate is represented by an RFC 3339 timestamp in the date-time format. The timestamp includes the time and an offset.

For example:

  • "1985-04-12T23:20:50.52Z"
  • "1996-12-19T16:39:57-08:00"
  • "1937-01-01T12:00:27.87+00:20"

To add a list of dates as a single entity, use an array of date-time formatted strings. For example: ["1985-04-12T23:20:50.52Z", "1937-01-01T12:00:27.87+00:20"]

In specific client libraries, you may be able to use the native date object as shown in the following examples.

Examples​

Property definition​

from weaviate.classes.config import Property, DataType
from datetime import datetime, timezone

# Create collection
my_collection = client.collections.create(
    name="ConcertTour",
    properties=[
        Property(name="artist", data_type=DataType.TEXT),
        Property(name="tour_name", data_type=DataType.TEXT),
        Property(name="tour_start", data_type=DataType.DATE),
        Property(name="tour_dates", data_type=DataType.DATE_ARRAY),
    ],
    # Other properties are omitted for brevity
)
py docs  API docs

Object insertion​

# Create an object
# In Python, you can use the RFC 3339 format or a datetime object (preferably with a timezone)
example_object = {
    "artist": "Taylor Swift",
    "tour_name": "Eras Tour",
    "tour_start": datetime(2023, 3, 17).replace(tzinfo=timezone.utc),
    "tour_dates": [
        # Use `datetime` objects with a timezone
        datetime(2023, 3, 17).replace(tzinfo=timezone.utc),
        datetime(2023, 3, 18).replace(tzinfo=timezone.utc),
        # .. more dates
        # Or use RFC 3339 format
        "2024-12-07T00:00:00Z",
        "2024-12-08T00:00:00Z",
    ],
}

obj_uuid = my_collection.data.insert(example_object)
py docs  API docs

uuid​

Added in v1.19

The dedicated uuid and uuid[] data types efficiently store UUIDs.

  • Each uuid is a 128-bit (16-byte) number.
  • The filterable index uses roaring bitmaps.
Aggregate/sort currently not possible

It is currently not possible to aggregate or sort by uuid or uuid[] types.

Examples​

Property definition​

from weaviate.classes.config import Property, DataType
from weaviate.util import generate_uuid5

# Create collection
my_collection = client.collections.create(
    name="Movie",
    properties=[
        Property(name="title", data_type=DataType.TEXT),
        Property(name="movie_uuid", data_type=DataType.UUID),
        Property(name="related_movie_uuids", data_type=DataType.UUID_ARRAY),
    ],
    # Other properties are omitted for brevity
)
py docs  API docs

Object insertion​

# Create an object
example_object = {
    "title": "The Matrix",
    "movie_uuid": generate_uuid5("The Matrix"),
    "related_movie_uuids": [
        generate_uuid5("The Matrix Reloaded"),
        generate_uuid5("The Matrix Revolutions"),
        generate_uuid5("Matrix Resurrections"),
    ],
}

obj_uuid = my_collection.data.insert(example_object)
py docs  API docs

geoCoordinates​

Geo coordinates can be used to find objects in a radius around a query location. A geo coordinate value stored as a float, and is processed as decimal degree according to the ISO standard.

To supply a geoCoordinates property, specify the latitude and longitude as floating point decimal degrees.

Examples​

Property definition​

from weaviate.classes.config import Property, DataType
from weaviate.classes.data import GeoCoordinate

# Create collection
my_collection = client.collections.create(
    name="City",
    properties=[
        Property(name="name", data_type=DataType.TEXT),
        Property(name="location", data_type=DataType.GEO_COORDINATES),
    ],
    # Other properties are omitted for brevity
)
py docs  API docs

Object insertion​

# Create an object
example_object = {
    "name": "Sydney",
    "location": GeoCoordinate(latitude=-33.8688, longitude=151.2093),
}

obj_uuid = my_collection.data.insert(example_object)
py docs  API docs
Limitations

Currently, geo-coordinate filtering is limited to the nearest 800 results from the source location, which will be further reduced by any other filter conditions and search parameters.

If you plan on a densely populated dataset, consider using another strategy such as geo-hashing into a text datatype, and filtering further, such as with a ContainsAny filter.

phoneNumber​

A phoneNumber input will be normalized and validated, unlike the single fields as number and string. The data field is an object with multiple fields.

{
  "phoneNumber": {
    "input": "020 1234567",                       // Required. Raw input in string format
    "defaultCountry": "nl",                       // Required if only a national number is provided, ISO 3166-1 alpha-2 country code. Only set if explicitly set by the user.
    "internationalFormatted": "+31 20 1234567",   // Read-only string
    "countryCode": 31,                            // Read-only unsigned integer, numerical country code
    "national": 201234567,                        // Read-only unsigned integer, numerical representation of the national number
    "nationalFormatted": "020 1234567",           // Read-only string
    "valid": true                                 // Read-only boolean. Whether the parser recognized the phone number as valid
  }
}

There are two fields that accept input. input must always be set, while defaultCountry must only be set in specific situations. There are two scenarios possible:

  • When you enter an international number (e.g. "+31 20 1234567") to the input field, no defaultCountry needs to be entered. The underlying parser will automatically recognize the number's country.
  • When you enter a national number (e.g. "020 1234567"), you need to specify the country in defaultCountry (in this case, "nl"), so that the parse can correctly convert the number into all formats. The string in defaultCountry should be an ISO 3166-1 alpha-2 country code.

Weaviate will also add further read-only fields such as internationalFormatted, countryCode, national, nationalFormatted and valid when reading back a field of type phoneNumber.

Examples​

Property definition​

from weaviate.classes.config import Property, DataType
from weaviate.classes.data import PhoneNumber

# Create collection
my_collection = client.collections.create(
    name="Person",
    properties=[
        Property(name="name", data_type=DataType.TEXT),
        Property(name="phone", data_type=DataType.PHONE_NUMBER),
    ],
    # Other properties are omitted for brevity
)
py docs  API docs

Object insertion​

# Create an object
example_object = {
    "name": "Ray Stantz",
    "phone": PhoneNumber(number="212 555 2368", default_country="us"),
}

obj_uuid = my_collection.data.insert(example_object)
py docs  API docs

blob​

The datatype blob accepts any binary data. The data should be base64 encoded, and passed as a string. Characteristics:

  • Weaviate doesn't make assumptions about the type of data that is encoded. A module (e.g. img2vec) can investigate file headers as it wishes, but Weaviate itself does not do this.
  • When storing, the data is base64 decoded (so Weaviate stores it more efficiently).
  • When serving, the data is base64 encoded (so it is safe to serve as json).
  • There is no max file size limit.
  • This blob field is always skipped in the inverted index, regardless of setting. This mean you can not search by this blob field in a Weaviate GraphQL where filter, and there is no valueBlob field accordingly. Depending on the module, this field can be used in module-specific filters (e.g. nearImage{} in the img2vec-neural filter).

To obtain the base64-encoded value of an image, you can run the following command - or use the helper methods in the Weaviate clients - to do so:

cat my_image.png | base64

Examples​

Property definition​

from weaviate.classes.config import Property, DataType

# Create collection
my_collection = client.collections.create(
    name="Poster",
    properties=[
        Property(name="title", data_type=DataType.TEXT),
        Property(name="image", data_type=DataType.BLOB),
    ],
    # Other properties are omitted for brevity
)
py docs  API docs

Object insertion​

# Create an object
example_object = {
    "title": "The Matrix",
    "image": blob_string
}

obj_uuid = my_collection.data.insert(example_object)
py docs  API docs

object​

Added in v1.22

The object type allows you to store nested data as a JSON object that can be nested to any depth.

For example, a Person collection could have an address property as an object. It could in turn include nested properties such as street and city:

Limitations

Currently, object and object[] datatype properties are not indexed and not vectorized.

Future plans include the ability to index nested properties, for example to allow for filtering on nested properties and vectorization options.

Examples​

Property definition​

from weaviate.classes.config import Property, DataType

# Create collection
my_collection = client.collections.create(
    name="Person",
    properties=[
        Property(name="name", data_type=DataType.TEXT),
        Property(
            name="home_address",
            data_type=DataType.OBJECT,
            nested_properties=[
                Property(
                    name="street",
                    data_type=DataType.OBJECT,
                    nested_properties=[
                        Property(name="number", data_type=DataType.INT),
                        Property(name="name", data_type=DataType.TEXT),
                    ],
                ),
                Property(name="city", data_type=DataType.TEXT),
            ],
        ),
        Property(
            name="office_addresses",
            data_type=DataType.OBJECT_ARRAY,
            nested_properties=[
                Property(name="office_name", data_type=DataType.TEXT),
                Property(
                    name="street",
                    data_type=DataType.OBJECT,
                    nested_properties=[
                        Property(name="name", data_type=DataType.TEXT),
                        Property(name="number", data_type=DataType.INT),
                    ],
                ),
            ],
        ),
    ],
    # Other properties are omitted for brevity
)
py docs  API docs

Object insertion​

# Create an object
example_object = {
    "name": "John Smith",
    "home_address": {
        "street": {
            "number": 123,
            "name": "Main Street",
        },
        "city": "London",
    },
    "office_addresses": [
        {
            "office_name": "London HQ",
            "street": {"number": 456, "name": "Oxford Street"},
        },
        {
            "office_name": "Manchester Branch",
            "street": {"number": 789, "name": "Piccadilly Gardens"},
        },
    ],
}

obj_uuid = my_collection.data.insert(example_object)
py docs  API docs

cross-reference​

Cross-references and query performance

Queries involving cross-references can be slower than queries that do not involve cross-references, especially at scale such as for multiple objects or complex queries.

At the first instance, we strongly encourage you to consider whether you can avoid using cross-references in your data schema. As a scalable AI-native database, Weaviate is well-placed to perform complex queries with vector, keyword and hybrid searches involving filters. You may benefit from rethinking your data schema to avoid cross-references where possible.

For example, instead of creating separate "Author" and "Book" collections with cross-references, consider embedding author information directly in Book objects and using searches and filters to find books by author characteristics.

The cross-reference type allows a link to be created from one object to another. This is useful for creating relationships between collections, such as linking a Person collection to a Company collection.

The cross-reference type objects are arrays by default. This allows you to link to any number of instances of a given collection (including zero).

For more information on cross-references, see the cross-references. To see how to work with cross-references, see how to manage data: cross-references.

More information​

Related pages

Notes​

Formatting in payloads​

In raw payloads (e.g. JSON payloads for REST), data types are specified as an array (e.g. ["text"], or ["text[]"]), as it is required for some cross-reference specifications.

Questions and feedback​

If you have any questions or feedback, let us know in the user forum.