weaviate.gql package

GraphQL module used to create get and/or aggregate GraphQL requests from Weaviate.

class weaviate.gql.AdditionalProperties(uuid: bool = False, vector: bool = False, creationTimeUnix: bool = False, lastUpdateTimeUnix: bool = False, distance: bool = False, certainty: bool = False, score: bool = False, explainScore: bool = False)[source]

Bases: object

certainty: bool = False

creationTimeUnix: bool = False

distance: bool = False

explainScore: bool = False

lastUpdateTimeUnix: bool = False

score: bool = False

uuid: bool = False

vector: bool = False

class weaviate.gql.LinkTo(link_on: str, linked_class: str, properties: Sequence[Union[str, ForwardRef('LinkTo')]])[source]

Bases: object

link_on: str

linked_class: str

properties: Sequence[str | LinkTo]

class weaviate.gql.Query(connection: Connection)[source]

Bases: object

Query class used to make get and/or aggregate GraphQL queries.

Initialize a Classification class instance.

Parameters

connectionweaviate.connect.Connection: Connection object to an active and running Weaviate instance.

aggregate(class_name: str) → AggregateBuilder[source]

Instantiate an AggregateBuilder for GraphQL aggregate requests.

Parameters

class_namestr: Class name of the objects to be aggregated.

Returns

AggregateBuilder: An AggregateBuilder to make GraphQL aggregate requests from weaviate.

get(class_name: str, properties: Sequence[str | LinkTo] | str | None = None) → GetBuilder[source]

Instantiate a GetBuilder for GraphQL get requests.

Parameters

class_namestr: Class name of the objects to interact with.
propertieslist of str and ReferenceProperty, str or None: Properties of the objects to get, by default None

Returns

GetBuilder: A GetBuilder to make GraphQL get requests from weaviate.

multi_get(get_builder: List[GetBuilder]) → MultiGetBuilder[source]

Instantiate a MultiGetBuilder for GraphQL multi_get requests. Bundles multiple get requests into one.

Parameters

get_builderlist of GetBuilder: List of GetBuilder objects for a single request each.

Returns

MultiGetBuilder: A MultiGetBuilder to make GraphQL get multiple requests from weaviate.

raw(gql_query: str) → Dict[str, Any][source]

Allows to send simple graph QL string queries. Be cautious of injection risks when generating query strings.

Parameters

gql_querystr: GraphQL query as a string.

Returns

dict: Data response of the query.

Examples

>>> query = """
... {
...     Get {
...         Article(limit: 2) {
...         title
...         hasAuthors {
...             ... on Author {
...                 name
...                 }
...             }
...         }
...     }
... }
... """
>>> client.query.raw(query)
{
"data": {
    "Get": {
    "Article": [
        {
        "hasAuthors": [
            {
            "name": "Jonathan Wilson"
            }
        ],
        "title": "Sergio Agüero has been far more than a great goalscorer for
                    Manchester City"
        },
        {
        "hasAuthors": [
            {
            "name": "Emma Elwick-Bates"
            }
        ],
        "title": "At Swarovski, Giovanna Engelbert Is Crafting Jewels As Exuberantly
                    Joyful As She Is"
        }
    ]
    }
},
"errors": null
}

Raises

TypeError: If ‘gql_query’ is not of type str.
requests.ConnectionError: If the network connection to weaviate fails.
weaviate.UnexpectedStatusCodeException: If weaviate reports a none OK status.

Submodules

weaviate.gql.aggregate module

GraphQL Aggregate command.

class weaviate.gql.aggregate.AggregateBuilder(class_name: str, connection: Connection | ConnectionV4)[source]

Bases: GraphQL

AggregateBuilder class used to aggregate Weaviate objects.

Initialize a AggregateBuilder class instance.

Parameters

class_namestr: Class name of the objects to be aggregated.
connectionweaviate.connect.Connection: Connection object to an active and running Weaviate instance.

build() → str[source]

Build the query and return the string.

Returns

str: The GraphQL query as a string.

with_fields(field: str) → AggregateBuilder[source]

Include a field in the aggregate query.

Parameters

fieldstr: Field to include in the aggregate query. e.g. ‘<property_name> { count }’

Returns

weaviate.gql.aggregate.AggregateBuilder: Updated AggregateBuilder.

with_group_by_filter(properties: List[str]) → AggregateBuilder[source]

Add a group by filter to the query. Might requires the user to set an additional group by clause using with_fields(..).

Parameters

propertieslist of str: list of properties that are included in the group by filter. Generates a filter like: ‘groupBy: [“property1”, “property2”]’ from a list [“property1”, “property2”]

Returns

weaviate.gql.aggregate.AggregateBuilder: Updated AggregateBuilder.

with_limit(limit: int) → AggregateBuilder[source]

Set limit to limit the number of returned results from the aggregation query.

Parameters

limitint: The limit.

Returns

weaviate.gql.aggregate.AggregateBuilder: Updated AggregateBuilder.

with_meta_count() → AggregateBuilder[source]

Set Meta Count to True.

Returns

weaviate.gql.aggregate.AggregateBuilder: Updated AggregateBuilder.

with_near_audio(content: dict, encode: bool = True) → AggregateBuilder[source]

Set nearAudio filter.

Parameters

contentdict: The content of the nearAudio filter to set. See examples below.
encodebool, optional: Whether to encode the content[“audio”] to base64 and convert to string. If True, the content[“audio”] can be an audio path or a file opened in binary read mode. If False, the content[“audio”] MUST be a base64 encoded string (NOT bytes, i.e. NOT binary string that looks like this: b’BASE64ENCODED’ but simple ‘BASE64ENCODED’). By default True.

Examples

Content prototype:

>>> content = {
...     'audio': <str or binary read file>,
...     # certainty ONLY with `cosine` distance specified in the schema
...     'certainty': <float>, # Optional, either 'certainty' OR 'distance'
...     'distance': <float>, # Optional, either 'certainty' OR 'distance'
... }

>>> {
...     'audio': "e5dc4a4c-ef0f-3aed-89a3-a73435c6bbcf",
...     'certainty': 0.7 # or 'distance'
... }

With encoded True:

>>> content = {
...     'audio': "my_audio_path.wav",
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Audio')\
        .with_fields('description')\
...     .with_near_audio(content, encode=True) # <- encode MUST be set to True

OR

>>> my_audio_file = open("my_audio_path.wav", "br")
>>> content = {
...     'audio': my_audio_file,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Audio')\
        .with_fields('description')\
...     .with_near_audio(content, encode=True) # <- encode MUST be set to True
>>> my_audio_file.close()

With encoded False:

>>> from weaviate.util import file_encoder_b64
>>> encoded_audio = file_encoder_b64("my_audio_path.wav")
>>> content = {
...     'audio': encoded_audio,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Audio')\
        .with_fields('description')\
...     .with_near_audio(content, encode=False) # <- encode MUST be set to False

OR

>>> from weaviate.util import file_encoder_b64
>>> with open("my_audio_path.wav", "br") as my_audio_file:
...     encoded_audio = file_encoder_b64(my_audio_file)
>>> content = {
...     'audio': encoded_audio,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Audio')\
        .with_fields('description')\
...     .with_near_audio(content, encode=False) # <- encode MUST be set to False

Encode Audio yourself:

>>> import base64
>>> with open("my_audio_path.wav", "br") as my_audio_file:
...     encoded_audio = base64.b64encode(my_audio_file.read()).decode("utf-8")
>>> content = {
...     'audio': encoded_audio,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Audio')\
        .with_fields('description')\
...     .with_near_audio(content, encode=False) # <- encode MUST be set to False

Returns

weaviate.gql.aggregate.AggregateBuilder: The updated AggregateBuilder.

Raises

AttributeError: If another ‘near’ filter was already set.

with_near_depth(content: dict, encode: bool = True) → AggregateBuilder[source]

Set nearDepth filter.

Parameters

contentdict: The content of the nearDepth filter to set. See examples below.
encodebool, optional: Whether to encode the content[“depth”] to base64 and convert to string. If True, the content[“depth”] can be an depth path or a file opened in binary read mode. If False, the content[“depth”] MUST be a base64 encoded string (NOT bytes, i.e. NOT binary string that looks like this: b’BASE64ENCODED’ but simple ‘BASE64ENCODED’). By default True.

Examples

Content prototype:

>>> content = {
...     'depth': <str or binary read file>,
...     # certainty ONLY with `cosine` distance specified in the schema
...     'certainty': <float>, # Optional, either 'certainty' OR 'distance'
...     'distance': <float>, # Optional, either 'certainty' OR 'distance'
... }

>>> {
...     'depth': "e5dc4a4c-ef0f-3aed-89a3-a73435c6bbcf",
...     'certainty': 0.7 # or 'distance'
... }

With encoded True:

>>> content = {
...     'depth': "my_depth_path.png",
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Depth')\
        .with_fields('description')\
...     .with_near_depth(content, encode=True) # <- encode MUST be set to True

OR

>>> my_depth_file = open("my_depth_path.png", "br")
>>> content = {
...     'depth': my_depth_file,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Depth')\
        .with_fields('description')\
...     .with_near_depth(content, encode=True) # <- encode MUST be set to True
>>> my_depth_file.close()

With encoded False:

>>> from weaviate.util import file_encoder_b64
>>> encoded_depth = file_encoder_b64("my_depth_path.png")
>>> content = {
...     'depth': encoded_depth,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Depth')\
        .with_fields('description')\
...     .with_near_depth(content, encode=False) # <- encode MUST be set to False

OR

>>> from weaviate.util import file_encoder_b64
>>> with open("my_depth_path.png", "br") as my_depth_file:
...     encoded_depth = file_encoder_b64(my_depth_file)
>>> content = {
...     'depth': encoded_depth,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Depth')\
        .with_fields('description')\
...     .with_near_depth(content, encode=False) # <- encode MUST be set to False

Encode Depth yourself:

>>> import base64
>>> with open("my_depth_path.png", "br") as my_depth_file:
...     encoded_depth = base64.b64encode(my_depth_file.read()).decode("utf-8")
>>> content = {
...     'depth': encoded_depth,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Depth')\
        .with_fields('description')\
...     .with_near_depth(content, encode=False) # <- encode MUST be set to False

Returns

weaviate.gql.aggregate.AggregateBuilder: The updated AggregateBuilder.

Raises

AttributeError: If another ‘near’ filter was already set.

with_near_image(content: dict, encode: bool = True) → AggregateBuilder[source]

Set nearImage filter.

Parameters

contentdict: The content of the nearImage filter to set. See examples below.
encodebool, optional: Whether to encode the content[“image”] to base64 and convert to string. If True, the content[“image”] can be an image path or a file opened in binary read mode. If False, the content[“image”] MUST be a base64 encoded string (NOT bytes, i.e. NOT binary string that looks like this: b’BASE64ENCODED’ but simple ‘BASE64ENCODED’). By default True.

Examples

Content prototype:

>>> content = {
...     'image': <str or binary read file>,
...     # certainty ONLY with `cosine` distance specified in the schema
...     'certainty': <float>, # Optional, either 'certainty' OR 'distance'
...     'distance': <float>, # Optional, either 'certainty' OR 'distance'
... }

>>> {
...     'image': "e5dc4a4c-ef0f-3aed-89a3-a73435c6bbcf",
...     'certainty': 0.7 # or 'distance'
... }

With encoded True:

>>> content = {
...     'image': "my_image_path.png",
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Image')\
        .with_fields('description')\
...     .with_near_image(content, encode=True) # <- encode MUST be set to True

OR

>>> my_image_file = open("my_image_path.png", "br")
>>> content = {
...     'image': my_image_file,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Image')\
        .with_fields('description')\
...     .with_near_image(content, encode=True) # <- encode MUST be set to True
>>> my_image_file.close()

With encoded False:

>>> from weaviate.util import image_encoder_b64, image_decoder_b64
>>> encoded_image = image_encoder_b64("my_image_path.png")
>>> content = {
...     'image': encoded_image,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Image')\
        .with_fields('description')\
...     .with_near_image(content, encode=False) # <- encode MUST be set to False

OR

>>> from weaviate.util import image_encoder_b64, image_decoder_b64
>>> with open("my_image_path.png", "br") as my_image_file:
...     encoded_image = image_encoder_b64(my_image_file)
>>> content = {
...     'image': encoded_image,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Image')\
        .with_fields('description')\
...     .with_near_image(content, encode=False) # <- encode MUST be set to False

Encode Image yourself:

>>> import base64
>>> with open("my_image_path.png", "br") as my_image_file:
...     encoded_image = base64.b64encode(my_image_file.read()).decode("utf-8")
>>> content = {
...     'image': encoded_image,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Image')\
        .with_fields('description')\
...     .with_near_image(content, encode=False) # <- encode MUST be set to False

Returns

weaviate.gql.aggregate.AggregateBuilder: The updated AggregateBuilder.

Raises

AttributeError: If another ‘near’ filter was already set.

with_near_imu(content: dict, encode: bool = True) → AggregateBuilder[source]

Set nearIMU filter.

Parameters

contentdict: The content of the nearIMU filter to set. See examples below.
encodebool, optional: Whether to encode the content[“thermal”] to base64 and convert to string. If True, the content[“thermal”] can be an thermal path or a file opened in binary read mode. If False, the content[“thermal”] MUST be a base64 encoded string (NOT bytes, i.e. NOT binary string that looks like this: b’BASE64ENCODED’ but simple ‘BASE64ENCODED’). By default True.

Examples

Content prototype:

>>> content = {
...     'thermal': <str or binary read file>,
...     # certainty ONLY with `cosine` distance specified in the schema
...     'certainty': <float>, # Optional, either 'certainty' OR 'distance'
...     'distance': <float>, # Optional, either 'certainty' OR 'distance'
... }

>>> {
...     'thermal': "e5dc4a4c-ef0f-3aed-89a3-a73435c6bbcf",
...     'certainty': 0.7 # or 'distance'
... }

With encoded True:

>>> content = {
...     'thermal': "my_thermal_path.png",
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('IMU')\
...     .with_fields('description')\
...     .with_near_thermal(content, encode=True) # <- encode MUST be set to True

OR

>>> my_thermal_file = open("my_thermal_path.png", "br")
>>> content = {
...     'thermal': my_thermal_file,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('IMU')\
...     .with_fields('description')\
...     .with_near_thermal(content, encode=True) # <- encode MUST be set to True
>>> my_thermal_file.close()

With encoded False:

>>> from weaviate.util import file_encoder_b64
>>> encoded_thermal = file_encoder_b64("my_thermal_path.png")
>>> content = {
...     'thermal': encoded_thermal,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('IMU')\
...     .with_fields('description')\
...     .with_near_thermal(content, encode=False) # <- encode MUST be set to False

OR

>>> from weaviate.util import file_encoder_b64
>>> with open("my_thermal_path.png", "br") as my_thermal_file:
...     encoded_thermal = file_encoder_b64(my_thermal_file)
>>> content = {
...     'thermal': encoded_thermal,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('IMU')\
...     .with_fields('description')\
...     .with_near_thermal(content, encode=False) # <- encode MUST be set to False

Encode IMU yourself:

>>> import base64
>>> with open("my_thermal_path.png", "br") as my_thermal_file:
...     encoded_thermal = base64.b64encode(my_thermal_file.read()).decode("utf-8")
>>> content = {
...     'thermal': encoded_thermal,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('IMU')\
...     .with_fields('description')\
...     .with_near_thermal(content, encode=False) # <- encode MUST be set to False

Returns

weaviate.gql.aggregate.AggregateBuilder: The updated AggregateBuilder.

Raises

AttributeError: If another ‘near’ filter was already set.

with_near_object(content: dict) → AggregateBuilder[source]

Set nearObject filter.

Parameters

contentdict: The content of the nearObject filter to set. See examples below.

Examples

Content prototype:

>>> content = {
...     'id': <str>, # OR 'beacon'
...     'beacon': <str>, # OR 'id'
...     # certainty ONLY with `cosine` distance specified in the schema
...     'certainty': <float>, # Optional, either 'certainty' OR 'distance'
...     'distance': <float>, # Optional, either 'certainty' OR 'distance'
... }

>>> {
...     'id': "e5dc4a4c-ef0f-3aed-89a3-a73435c6bbcf",
...     'certainty': 0.7 # or 'distance' instead
... }
>>> # alternatively
>>> {
...     'beacon': "weaviate://localhost/Book/e5dc4a4c-ef0f-3aed-89a3-a73435c6bbcf"
...     'certainty': 0.7 # or 'distance' instead
... }

Returns

weaviate.gql.aggregate.AggregateBuilder: Updated AggregateBuilder.

Raises

AttributeError: If another ‘near’ filter was already set.

with_near_text(content: dict) → AggregateBuilder[source]

Set nearText filter. This filter can be used with text modules (text2vec). E.g.: text2vec-contextionary, text2vec-transformers. NOTE: The ‘autocorrect’ field is enabled only with the text-spellcheck Weaviate module.

Parameters

contentdict: The content of the nearText filter to set. See examples below.

Examples

Content full prototype:

>>> content = {
...     'concepts': <list of str or str>,
...     # certainty ONLY with `cosine` distance specified in the schema
...     'certainty': <float>, # Optional, either 'certainty' OR 'distance'
...     'distance': <float>, # Optional, either 'certainty' OR 'distance'
...     'moveAwayFrom': { # Optional
...         'concepts': <list of str or str>,
...         'force': <float>
...     },
...     'moveTo': { # Optional
...         'concepts': <list of str or str>,
...         'force': <float>
...     },
...     'autocorrect': <bool>, # Optional
... }

Full content:

>>> content = {
...     'concepts': ["fashion"],
...     'certainty': 0.7, # or 'distance' instead
...     'moveAwayFrom': {
...         'concepts': ["finance"],
...         'force': 0.45
...     },
...     'moveTo': {
...         'concepts': ["haute couture"],
...         'force': 0.85
...     },
...     'autocorrect': True
... }

Partial content:

>>> content = {
...     'concepts': ["fashion"],
...     'certainty': 0.7, # or 'distance' instead
...     'moveTo': {
...         'concepts': ["haute couture"],
...         'force': 0.85
...     }
... }

Minimal content:

>>> content = {
...     'concepts': "fashion"
... }

Returns

weaviate.gql.aggregate.AggregateBuilder: Updated AggregateBuilder.

Raises

AttributeError: If another ‘near’ filter was already set.

with_near_thermal(content: dict, encode: bool = True) → AggregateBuilder[source]

Set nearThermal filter.

Parameters

contentdict: The content of the nearThermal filter to set. See examples below.
encodebool, optional: Whether to encode the content[“thermal”] to base64 and convert to string. If True, the content[“thermal”] can be an thermal path or a file opened in binary read mode. If False, the content[“thermal”] MUST be a base64 encoded string (NOT bytes, i.e. NOT binary string that looks like this: b’BASE64ENCODED’ but simple ‘BASE64ENCODED’). By default True.

Examples

Content prototype:

>>> content = {
...     'thermal': <str or binary read file>,
...     # certainty ONLY with `cosine` distance specified in the schema
...     'certainty': <float>, # Optional, either 'certainty' OR 'distance'
...     'distance': <float>, # Optional, either 'certainty' OR 'distance'
... }

>>> {
...     'thermal': "e5dc4a4c-ef0f-3aed-89a3-a73435c6bbcf",
...     'certainty': 0.7 # or 'distance'
... }

With encoded True:

>>> content = {
...     'thermal': "my_thermal_path.png",
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Thermal', 'description')\
...     .with_near_thermal(content, encode=True) # <- encode MUST be set to True

OR

>>> my_thermal_file = open("my_thermal_path.png", "br")
>>> content = {
...     'thermal': my_thermal_file,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Thermal')\
...     .with_fields('description')\
...     .with_near_thermal(content, encode=True) # <- encode MUST be set to True
>>> my_thermal_file.close()

With encoded False:

>>> from weaviate.util import file_encoder_b64
>>> encoded_thermal = file_encoder_b64("my_thermal_path.png")
>>> content = {
...     'thermal': encoded_thermal,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Thermal')\
...     .with_fields('description')\
...     .with_near_thermal(content, encode=False) # <- encode MUST be set to False

OR

>>> from weaviate.util import file_encoder_b64
>>> with open("my_thermal_path.png", "br") as my_thermal_file:
...     encoded_thermal = file_encoder_b64(my_thermal_file)
>>> content = {
...     'thermal': encoded_thermal,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Thermal')\
...     .with_fields('description')\
...     .with_near_thermal(content, encode=False) # <- encode MUST be set to False

Encode Thermal yourself:

>>> import base64
>>> with open("my_thermal_path.png", "br") as my_thermal_file:
...     encoded_thermal = base64.b64encode(my_thermal_file.read()).decode("utf-8")
>>> content = {
...     'thermal': encoded_thermal,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Thermal')\
...     .with_fields('description')\
...     .with_near_thermal(content, encode=False) # <- encode MUST be set to False

Returns

weaviate.gql.aggregate.AggregateBuilder: The updated AggregateBuilder.

Raises

AttributeError: If another ‘near’ filter was already set.

with_near_vector(content: dict) → AggregateBuilder[source]

Set nearVector filter.

Parameters

contentdict: The content of the nearVector filter to set. See examples below.

Examples

Content full prototype:

>>> content = {
...     'vector' : <list of float>,
...     # certainty ONLY with `cosine` distance specified in the schema
...     'certainty': <float>, # Optional, either 'certainty' OR 'distance'
...     'distance': <float>, # Optional, either 'certainty' OR 'distance'
... }

NOTE: Supported types for ‘vector’ are list, ‘numpy.ndarray`, torch.Tensor: and tf.Tensor.

Full content:

>>> content = {
...     'vector' : [.1, .2, .3, .5],
...     'certainty': 0.75, # or 'distance' instead
... }

Minimal content:

>>> content = {
...     'vector' : [.1, .2, .3, .5]
... }

Or

>>> content = {
...     'vector' : torch.tensor([.1, .2, .3, .5])
... }

Or

>>> content = {
...     'vector' : torch.tensor([[.1, .2, .3, .5]]) # it is going to be squeezed.
... }

Returns

weaviate.gql.aggregate.AggregateBuilder: Updated AggregateBuilder.

Raises

AttributeError: If another ‘near’ filter was already set.

with_near_video(content: dict, encode: bool = True) → AggregateBuilder[source]

Set nearVideo filter.

Parameters

contentdict: The content of the nearVideo filter to set. See examples below.
encodebool, optional: Whether to encode the content[“video”] to base64 and convert to string. If True, the content[“video”] can be an video path or a file opened in binary read mode. If False, the content[“video”] MUST be a base64 encoded string (NOT bytes, i.e. NOT binary string that looks like this: b’BASE64ENCODED’ but simple ‘BASE64ENCODED’). By default True.

Examples

Content prototype:

>>> content = {
...     'video': <str or binary read file>,
...     # certainty ONLY with `cosine` distance specified in the schema
...     'certainty': <float>, # Optional, either 'certainty' OR 'distance'
...     'distance': <float>, # Optional, either 'certainty' OR 'distance'
... }

>>> {
...     'video': "e5dc4a4c-ef0f-3aed-89a3-a73435c6bbcf",
...     'certainty': 0.7 # or 'distance'
... }

With encoded True:

>>> content = {
...     'video': "my_video_path.avi",
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Video')\
        .with_fields('description')\
...     .with_near_video(content, encode=True) # <- encode MUST be set to True

OR

>>> my_video_file = open("my_video_path.avi", "br")
>>> content = {
...     'video': my_video_file,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Video')\
        .with_fields('description')\
...     .with_near_video(content, encode=True) # <- encode MUST be set to True
>>> my_video_file.close()

With encoded False:

>>> from weaviate.util import file_encoder_b64
>>> encoded_video = file_encoder_b64("my_video_path.avi")
>>> content = {
...     'video': encoded_video,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Video')\
        .with_fields('description')\
...     .with_near_video(content, encode=False) # <- encode MUST be set to False

OR

>>> from weaviate.util import file_encoder_b64, video_decoder_b64
>>> with open("my_video_path.avi", "br") as my_video_file:
...     encoded_video = file_encoder_b64(my_video_file)
>>> content = {
...     'video': encoded_video,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Video')\
        .with_fields('description')\
...     .with_near_video(content, encode=False) # <- encode MUST be set to False

Encode Video yourself:

>>> import base64
>>> with open("my_video_path.avi", "br") as my_video_file:
...     encoded_video = base64.b64encode(my_video_file.read()).decode("utf-8")
>>> content = {
...     'video': encoded_video,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.aggregate('Video')\
        .with_fields('description')\
...     .with_near_video(content, encode=False) # <- encode MUST be set to False

Returns

weaviate.gql.aggregate.AggregateBuilder: The updated AggregateBuilder.

Raises

AttributeError: If another ‘near’ filter was already set.

with_object_limit(limit: int) → AggregateBuilder[source]

Set objectLimit to limit vector search results used within the aggregation query only when with near<MEDIA> filter.

Parameters

limitint: The object limit.

Returns

weaviate.gql.aggregate.AggregateBuilder: Updated AggregateBuilder.

with_tenant(tenant: str) → AggregateBuilder[source]: Sets a tenant for the query.

with_where(content: dict) → AggregateBuilder[source]

Set ‘where’ filter.

Parameters

contentdict: The where filter to include in the aggregate query. See examples below.

Examples

The content prototype is like this:

>>> content = {
...     'operator': '<operator>',
...     'operands': [
...         {
...             'path': [path],
...             'operator': '<operator>'
...             '<valueType>': <value>
...         },
...         {
...             'path': [<matchPath>],
...             'operator': '<operator>',
...             '<valueType>': <value>
...         }
...     ]
... }

This is a complete where filter but it does not have to be like this all the time.

Single operand:

>>> content = {
...     'path': ["wordCount"],    # Path to the property that should be used
...     'operator': 'GreaterThan',  # operator
...     'valueInt': 1000       # value (which is always = to the type of the path property)
... }

Or

>>> content = {
...     'path': ["id"],
...     'operator': 'Equal',
...     'valueString': "e5dc4a4c-ef0f-3aed-89a3-a73435c6bbcf"
... }

Multiple operands:

>>> content = {
...     'operator': 'And',
...     'operands': [
...         {
...             'path': ["wordCount"],
...             'operator': 'GreaterThan',
...             'valueInt': 1000
...         },
...         {
...             'path': ["wordCount"],
...             'operator': 'LessThan',
...             'valueInt': 1500
...         }
...     ]
... }

Returns

weaviate.gql.aggregate.AggregateBuilder: Updated AggregateBuilder.

weaviate.gql.filter module

GraphQL filters for Get and Aggregate commands. GraphQL abstract class for GraphQL commands to inherit from.

class weaviate.gql.filter.Ask(content: dict)[source]

Bases: Filter

Ask class used to filter weaviate objects by asking a question.

Initialize a Ask class instance.

Parameters

contentlist: The content of the ask clause.

Raises

TypeError: If ‘content’ is not of type dict.
ValueError: If ‘content’ has key “certainty”/”distance” but the value is not float.
TypeError: If ‘content’ has key “properties” but the type is not list or str.

class weaviate.gql.filter.Filter(content: dict)[source]

Bases: ABC

A base abstract class for all filters.

Initialize a Filter class instance.

Parameters

contentdict: The content of the Filter clause.

property content: dict

class weaviate.gql.filter.GraphQL(connection: Connection | ConnectionV4)[source]

Bases: ABC

A base abstract class for GraphQL commands, such as Get, Aggregate.

Initialize a GraphQL abstract class instance.

Parameters

connectionweaviate.connect.Connection: Connection object to an active and running weaviate instance.

abstract build() → str[source]

Build method to be overloaded by the child classes. It should return the GraphQL query as a str.

Returns

str: The query.

do() → dict[source]

Builds and runs the query.

Returns

dict: The response of the query.

Raises

requests.ConnectionError: If the network connection to weaviate fails.
weaviate.UnexpectedStatusCodeException: If weaviate reports a none OK status.

class weaviate.gql.filter.MediaType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]

Bases: Enum

AUDIO = 'audio'

DEPTH = 'depth'

IMAGE = 'image'

IMU = 'imu'

THERMAL = 'thermal'

VIDEO = 'video'

class weaviate.gql.filter.NearAudio(content: dict)[source]

Bases: NearMedia

NearAudio class used to filter weaviate objects.

Initialize a NearAudio class instance.

Parameters

contentlist: The content of the nearAudio clause.

Raises

TypeError: If ‘content’ is not of type dict.
TypeError: If ‘content[“audio”]’ is not of type str.
ValueError: If ‘content’ has key “certainty”/”distance” but the value is not float.

class weaviate.gql.filter.NearDepth(content: dict)[source]

Bases: NearMedia

NearDepth class used to filter weaviate objects.

Initialize a NearDepth class instance.

Parameters

contentlist: The content of the nearDepth clause.

Raises

TypeError: If ‘content’ is not of type dict.
TypeError: If ‘content[“depth”]’ is not of type str.
ValueError: If ‘content’ has key “certainty”/”distance” but the value is not float.

class weaviate.gql.filter.NearIMU(content: dict)[source]

Bases: NearMedia

NearIMU class used to filter weaviate objects.

Initialize a NearIMU class instance.

Parameters

contentlist: The content of the nearIMU clause.

Raises

TypeError: If ‘content’ is not of type dict.
TypeError: If ‘content[“imu”]’ is not of type str.
ValueError: If ‘content’ has key “certainty”/”distance” but the value is not float.

class weaviate.gql.filter.NearImage(content: dict)[source]

Bases: NearMedia

NearImage class used to filter weaviate objects.

Initialize a NearImage class instance.

Parameters

contentlist: The content of the nearImage clause.

Raises

TypeError: If ‘content’ is not of type dict.
TypeError: If ‘content[“image”]’ is not of type str.
ValueError: If ‘content’ has key “certainty”/”distance” but the value is not float.

class weaviate.gql.filter.NearMedia(content: dict, media_type: MediaType)[source]

Bases: Filter

Initialize a NearMedia class instance.

Parameters

contentlist: The content of the near<Media> clause.

Raises

TypeError: If ‘content’ is not of type dict.
TypeError: If ‘content[“<media>”]’ is not of type str.
ValueError: If ‘content’ has key “certainty”/”distance” but the value is not float.

class weaviate.gql.filter.NearObject(content: dict, is_server_version_14: bool)[source]

Bases: Filter

NearObject class used to filter weaviate objects.

Initialize a NearVector class instance.

Parameters

contentlist: The content of the nearVector clause.
is_server_version_14bool: Whether the Server version is >= 1.14.0.

Raises

TypeError: If ‘content’ is not of type dict.
ValueError: If ‘content’ has key “certainty”/”distance” but the value is not float.
TypeError: If ‘id’/’beacon’ key does not have a value of type str!

class weaviate.gql.filter.NearText(content: dict)[source]

Bases: Filter

NearText class used to filter weaviate objects. Can be used with text models only (text2vec). E.g.: text2vec-contextionary, text2vec-transformers.

Initialize a NearText class instance.

Parameters

contentdict: The content of the nearText clause.

Raises

TypeError: If ‘content’ is not of type dict.
ValueError: If ‘content’ has key “certainty”/”distance” but the value is not float.

class weaviate.gql.filter.NearThermal(content: dict)[source]

Bases: NearMedia

NearThermal class used to filter weaviate objects.

Initialize a NearThermal class instance.

Parameters

contentlist: The content of the nearThermal clause.

Raises

TypeError: If ‘content’ is not of type dict.
TypeError: If ‘content[“thermal”]’ is not of type str.
ValueError: If ‘content’ has key “certainty”/”distance” but the value is not float.

class weaviate.gql.filter.NearVector(content: dict)[source]

Bases: Filter

NearVector class used to filter weaviate objects.

Initialize a NearVector class instance.

Parameters

contentlist: The content of the nearVector clause.

Raises

TypeError: If ‘content’ is not of type dict.
KeyError: If ‘content’ does not contain “vector”.
TypeError: If ‘content[“vector”]’ is not of type list.
AttributeError: If invalid ‘content’ keys are provided.
ValueError: If ‘content’ has key “certainty”/”distance” but the value is not float.

class weaviate.gql.filter.NearVideo(content: dict)[source]

Bases: NearMedia

NearVideo class used to filter weaviate objects.

Initialize a NearVideo class instance.

Parameters

contentlist: The content of the nearVideo clause.

Raises

TypeError: If ‘content’ is not of type dict.
TypeError: If ‘content[“video”]’ is not of type str.
ValueError: If ‘content’ has key “certainty”/”distance” but the value is not float.

class weaviate.gql.filter.Sort(content: dict | list)[source]

Bases: Filter

Sort filter class used to sort weaviate objects.

Initialize a Where filter class instance.

Parameters

contentlist or dict: The content of the sort filter clause or a single clause.

Raises

TypeError: If ‘content’ is not of type dict.
ValueError: If a mandatory key is missing in the filter content.

add(content: dict | list) → None[source]

Add more sort clauses to the already existing sort clauses.

Parameters

contentlist or dict: The content of the sort filter clause or a single clause to be added to the already existing ones.

Raises

TypeError: If ‘content’ is not of type dict.
ValueError: If a mandatory key is missing in the filter content.

class weaviate.gql.filter.Where(content: dict)[source]

Bases: Filter

Where filter class used to filter weaviate objects.

Initialize a Where filter class instance.

Parameters

contentdict: The content of the where filter clause.

Raises

TypeError: If ‘content’ is not of type dict.
ValueError: If a mandatory key is missing in the filter content.

weaviate.gql.get module

GraphQL Get command.

class weaviate.gql.get.AdditionalProperties(uuid: bool = False, vector: bool = False, creationTimeUnix: bool = False, lastUpdateTimeUnix: bool = False, distance: bool = False, certainty: bool = False, score: bool = False, explainScore: bool = False)[source]

Bases: object

certainty: bool = False

creationTimeUnix: bool = False

distance: bool = False

explainScore: bool = False

lastUpdateTimeUnix: bool = False

score: bool = False

uuid: bool = False

vector: bool = False

class weaviate.gql.get.BM25(query: str, properties: List[str] | None)[source]

Bases: object

properties: List[str] | None

query: str

class weaviate.gql.get.GetBuilder(class_name: str, properties: Sequence[str | LinkTo] | str | None, connection: Connection)[source]

Bases: GraphQL

GetBuilder class used to create GraphQL queries.

Initialize a GetBuilder class instance.

Parameters

class_namestr: Class name of the objects to interact with.
propertiesstr or list of str: Properties of the objects to interact with.
connectionweaviate.connect.Connection: Connection object to an active and running Weaviate instance.

Raises

TypeError: If argument/s is/are of wrong type.

build(wrap_get: bool = True) → str[source]

Build query filter as a string.

Parameters

wrap_get: bool: A boolean to decide wether {Get{…}} is placed around the query. Useful for multi_get.

Returns

str: The GraphQL query as a string.

do() → dict[source]

Builds and runs the query.

Returns

dict: The response of the query.

Raises

requests.ConnectionError: If the network connection to weaviate fails.
weaviate.UnexpectedStatusCodeException: If weaviate reports a none OK status.

property name: str

Add additional properties (i.e. properties from _additional clause). See Examples below. If the the ‘properties’ is of data type str or list of str then the method is idempotent, if it is of type dict or tuple then the exiting property is going to be replaced. To set the setting of one of the additional property use the tuple data type where properties look like this (clause: dict, settings: dict) where the ‘settings’ are the properties inside the ‘(…)’ of the clause. See Examples for more information.

Parameters

propertiesstr, list of str, dict[str, str], dict[str, list of str] or tuple[dict, dict]

The additional properties to include in the query. Can be property name as str, a list of property names, a dictionary (clause without settings) where the value is a str or list of str, or a tuple of 2 elements:

(clause: Dict[str, str or list[str]], settings: Dict[str, Any])

where the ‘clause’ is the property and all its sub-properties and the ‘settings’ is the setting of the property, i.e. everything that is inside the (…) right after the property name. See Examples below.

Examples

>>> # single additional property with this GraphQL query
>>> '''
... {
...     Get {
...         Article {
...             title
...             author
...             _additional {
...                 id
...             }
...         }
...     }
... }
... '''
>>> client.query\
...     .get('Article', ['title', 'author'])\
...     .with_additional('id']) # argument as `str`

>>> # multiple additional property with this GraphQL query
>>> '''
... {
...     Get {
...         Article {
...             title
...             author
...             _additional {
...                 id
...                 certainty
...             }
...         }
...     }
... }
... '''
>>> client.query\
...     .get('Article', ['title', 'author'])\
...     .with_additional(['id', 'certainty']) # argument as `List[str]`

>>> # additional properties as clause with this GraphQL query
>>> '''
... {
...     Get {
...         Article {
...             title
...             author
...             _additional {
...                 classification {
...                     basedOn
...                     classifiedFields
...                     completed
...                     id
...                     scope
...                 }
...             }
...         }
...     }
... }
... '''
>>> client.query\
...     .get('Article', ['title', 'author'])\
...     .with_additional(
...         {
...             'classification' : ['basedOn', 'classifiedFields', 'completed', 'id']
...         }
...     ) # argument as `dict[str, List[str]]`
>>> # or with this GraphQL query
>>> '''
... {
...     Get {
...         Article {
...             title
...             author
...             _additional {
...                 classification {
...                     completed
...                 }
...             }
...         }
...     }
... }
... '''
>>> client.query\
...     .get('Article', ['title', 'author'])\
...     .with_additional(
...         {
...             'classification' : 'completed'
...         }
...     ) # argument as `Dict[str, str]`

Consider the following GraphQL clause:

>>> '''
... {
...     Get {
...         Article {
...             title
...             author
...             _additional {
...                 token (
...                     properties: ["content"]
...                     limit: 10
...                     certainty: 0.8
...                 ) {
...                     certainty
...                     endPosition
...                     entity
...                     property
...                     startPosition
...                     word
...                 }
...             }
...         }
...     }
... }
... '''

Then the python translation of this is the following:

>>> clause = {
...     'token': [ # if only one, can be passes as `str`
...         'certainty',
...         'endPosition',
...         'entity',
...         'property',
...         'startPosition',
...         'word',
...     ]
... }
>>> settings = {
...     'properties': ["content"],  # is required
...     'limit': 10,                # optional, int
...     'certainty': 0.8            # optional, float
... }
>>> client.query\
...     .get('Article', ['title', 'author'])\
...     .with_additional(
...         (clause, settings)
...     ) # argument as `Tuple[Dict[str, List[str]], Dict[str, Any]]`

If the desired clause does not match any example above, then the clause can always be converted to string before passing it to the .with_additional() method.

Returns

weaviate.gql.get.GetBuilder: The updated GetBuilder.

Raises

TypeError: If one of the property is not of a correct data type.

with_after(after_uuid: str | UUID) → GetBuilder[source]

Can be used to extract all elements by giving the last ID from the previous “page”.

Requires limit to be set but cannot be combined with any other filters or search. Part of the Cursor API.

with_alias(alias: str) → GetBuilder[source]

Gives an alias for the query. Needs to be used if ‘multi_get’ requests the same ‘class_name’ twice.

Parameters

alias: str: The alias for the query.

with_ask(content: dict) → GetBuilder[source]

Ask a question for which weaviate will retrieve the answer from your data. This filter can be used only with QnA module: qna-transformers. NOTE: The ‘autocorrect’ field is enabled only with the text-spellcheck Weaviate module.

Parameters

contentdict: The content of the ask filter to set. See examples below.

Examples

Content full prototype:

>>> content = {
...     'question' : <str>,
...     # certainty ONLY with `cosine` distance specified in the schema
...     'certainty': <float>, # Optional, either 'certainty' OR 'distance'
...     'distance': <float>, # Optional, either 'certainty' OR 'distance'
...     'properties': <list of str or str> # Optional
...     'autocorrect': <bool>, # Optional
... }

Full content:

>>> content = {
...     'question' : "What is the NLP?",
...     'certainty': 0.7, # or 'distance'
...     'properties': ['body'] # search the answer in these properties only.
...     'autocorrect': True
... }

Minimal content:

>>> content = {
...     'question' : "What is the NLP?"
... }

Returns

weaviate.gql.get.GetBuilder: The updated GetBuilder.

with_autocut(autocut: int) → GetBuilder[source]: Cuts off irrelevant results based on “jumps” in scores.

with_bm25(query: str, properties: List[str] | None = None) → GetBuilder[source]

Add BM25 query to search the inverted index for keywords.

Parameters

query: str: The query to search for.
properties: Optional[List[str]]: Which properties should be searched. If ‘None’ or empty all properties will be searched. By default, None

with_consistency_level(consistency_level: ConsistencyLevel) → GetBuilder[source]: Set the consistency level for the request.

with_generate(single_prompt: str | None = None, grouped_task: str | None = None, grouped_properties: List[str] | None = None) → GetBuilder[source]

Generate responses using the OpenAI generative search.

At least one of the two parameters must be set.

Parameters

grouped_task: Optional[str]: The task to generate a grouped response.
grouped_properties: Optional[List[str]]:: The properties whose contents are added to the prompts. If None or empty, all text properties contents are added.
single_prompt: Optional[str]: The prompt to generate a single response.

with_group_by(properties: List[str], groups: int, objects_per_group: int) → GetBuilder[source]

Retrieve groups of objects from Weaviate.

Note that the return values must be set using .with_additional() to see the output.

Parameters

properties: List[str]: Properties to group by
groups: int: Maximum number of groups
objects_per_group: int: Maximum number of objects per group

with_hybrid(query: str, alpha: float | None = None, vector: List[float] | None = None, properties: List[str] | None = None, fusion_type: HybridFusion | None = None) → GetBuilder[source]

Get objects using bm25 and vector, then combine the results using a reciprocal ranking algorithm.

Parameters

query: str: The query to search for.
alpha: Optional[float]: Factor determining how BM25 and vector search are weighted. If ‘None’ the weaviate default of 0.75 is used. By default, None alpha = 0 -> bm25, alpha=1 -> vector search
vector: Optional[List[float]]: Vector that is searched for. If ‘None’, weaviate will use the configured text-to-vector module to create a vector from the “query” field. By default, None
properties: Optional[List[str]]:: Which properties should be searched by BM25. Does not have any effect for vector search. If None or empty all properties are searched.
fusion_type: Optional[HybridFusionType]:: Which fusion type should be used to merge keyword and vector search.

with_limit(limit: int) → GetBuilder[source]

The limit of objects returned.

Parameters

limitint: The max number of objects returned.

Returns

weaviate.gql.get.GetBuilder: The updated GetBuilder.

Raises

ValueError: If ‘limit’ is non-positive.

with_near_audio(content: dict, encode: bool = True) → GetBuilder[source]

Set nearAudio filter.

Parameters

contentdict: The content of the nearAudio filter to set. See examples below.
encodebool, optional: Whether to encode the content[“audio”] to base64 and convert to string. If True, the content[“audio”] can be an audio path or a file opened in binary read mode. If False, the content[“audio”] MUST be a base64 encoded string (NOT bytes, i.e. NOT binary string that looks like this: b’BASE64ENCODED’ but simple ‘BASE64ENCODED’). By default True.

Examples

Content prototype:

>>> content = {
...     'audio': <str or binary read file>,
...     # certainty ONLY with `cosine` distance specified in the schema
...     'certainty': <float>, # Optional, either 'certainty' OR 'distance'
...     'distance': <float>, # Optional, either 'certainty' OR 'distance'
... }

>>> {
...     'audio': "e5dc4a4c-ef0f-3aed-89a3-a73435c6bbcf",
...     'certainty': 0.7 # or 'distance'
... }

With encoded True:

>>> content = {
...     'audio': "my_audio_path.wav",
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Audio', 'description')\
...     .with_near_audio(content, encode=True) # <- encode MUST be set to True

OR

>>> my_audio_file = open("my_audio_path.wav", "br")
>>> content = {
...     'audio': my_audio_file,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Audio', 'description')\
...     .with_near_audio(content, encode=True) # <- encode MUST be set to True
>>> my_audio_file.close()

With encoded False:

>>> from weaviate.util import file_encoder_b64
>>> encoded_audio = file_encoder_b64("my_audio_path.wav")
>>> content = {
...     'audio': encoded_audio,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Audio', 'description')\
...     .with_near_audio(content, encode=False) # <- encode MUST be set to False

OR

>>> from weaviate.util import file_encoder_b64
>>> with open("my_audio_path.wav", "br") as my_audio_file:
...     encoded_audio = file_encoder_b64(my_audio_file)
>>> content = {
...     'audio': encoded_audio,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Audio', 'description')\
...     .with_near_audio(content, encode=False) # <- encode MUST be set to False

Encode Audio yourself:

>>> import base64
>>> with open("my_audio_path.wav", "br") as my_audio_file:
...     encoded_audio = base64.b64encode(my_audio_file.read()).decode("utf-8")
>>> content = {
...     'audio': encoded_audio,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Audio', 'description')\
...     .with_near_audio(content, encode=False) # <- encode MUST be set to False

Returns

weaviate.gql.get.GetBuilder: The updated GetBuilder.

Raises

AttributeError: If another ‘near’ filter was already set.

with_near_depth(content: dict, encode: bool = True) → GetBuilder[source]

Set nearDepth filter.

Parameters

contentdict: The content of the nearDepth filter to set. See examples below.
encodebool, optional: Whether to encode the content[“depth”] to base64 and convert to string. If True, the content[“depth”] can be an depth path or a file opened in binary read mode. If False, the content[“depth”] MUST be a base64 encoded string (NOT bytes, i.e. NOT binary string that looks like this: b’BASE64ENCODED’ but simple ‘BASE64ENCODED’). By default True.

Examples

Content prototype:

>>> content = {
...     'depth': <str or binary read file>,
...     # certainty ONLY with `cosine` distance specified in the schema
...     'certainty': <float>, # Optional, either 'certainty' OR 'distance'
...     'distance': <float>, # Optional, either 'certainty' OR 'distance'
... }

>>> {
...     'depth': "e5dc4a4c-ef0f-3aed-89a3-a73435c6bbcf",
...     'certainty': 0.7 # or 'distance'
... }

With encoded True:

>>> content = {
...     'depth': "my_depth_path.png",
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Depth', 'description')\
...     .with_near_depth(content, encode=True) # <- encode MUST be set to True

OR

>>> my_depth_file = open("my_depth_path.png", "br")
>>> content = {
...     'depth': my_depth_file,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Depth', 'description')\
...     .with_near_depth(content, encode=True) # <- encode MUST be set to True
>>> my_depth_file.close()

With encoded False:

>>> from weaviate.util import file_encoder_b64
>>> encoded_depth = file_encoder_b64("my_depth_path.png")
>>> content = {
...     'depth': encoded_depth,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Depth', 'description')\
...     .with_near_depth(content, encode=False) # <- encode MUST be set to False

OR

>>> from weaviate.util import file_encoder_b64
>>> with open("my_depth_path.png", "br") as my_depth_file:
...     encoded_depth = file_encoder_b64(my_depth_file)
>>> content = {
...     'depth': encoded_depth,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Depth', 'description')\
...     .with_near_depth(content, encode=False) # <- encode MUST be set to False

Encode Depth yourself:

>>> import base64
>>> with open("my_depth_path.png", "br") as my_depth_file:
...     encoded_depth = base64.b64encode(my_depth_file.read()).decode("utf-8")
>>> content = {
...     'depth': encoded_depth,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Depth', 'description')\
...     .with_near_depth(content, encode=False) # <- encode MUST be set to False

Returns

weaviate.gql.get.GetBuilder: The updated GetBuilder.

Raises

AttributeError: If another ‘near’ filter was already set.

with_near_image(content: dict, encode: bool = True) → GetBuilder[source]

Set nearImage filter.

Parameters

contentdict: The content of the nearImage filter to set. See examples below.
encodebool, optional: Whether to encode the content[“image”] to base64 and convert to string. If True, the content[“image”] can be an image path or a file opened in binary read mode. If False, the content[“image”] MUST be a base64 encoded string (NOT bytes, i.e. NOT binary string that looks like this: b’BASE64ENCODED’ but simple ‘BASE64ENCODED’). By default True.

Examples

Content prototype:

>>> content = {
...     'image': <str or binary read file>,
...     # certainty ONLY with `cosine` distance specified in the schema
...     'certainty': <float>, # Optional, either 'certainty' OR 'distance'
...     'distance': <float>, # Optional, either 'certainty' OR 'distance'
... }

>>> {
...     'image': "e5dc4a4c-ef0f-3aed-89a3-a73435c6bbcf",
...     'certainty': 0.7 # or 'distance'
... }

With encoded True:

>>> content = {
...     'image': "my_image_path.png",
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Image', 'description')\
...     .with_near_image(content, encode=True) # <- encode MUST be set to True

OR

>>> my_image_file = open("my_image_path.png", "br")
>>> content = {
...     'image': my_image_file,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Image', 'description')\
...     .with_near_image(content, encode=True) # <- encode MUST be set to True
>>> my_image_file.close()

With encoded False:

>>> from weaviate.util import image_encoder_b64, image_decoder_b64
>>> encoded_image = image_encoder_b64("my_image_path.png")
>>> content = {
...     'image': encoded_image,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Image', 'description')\
...     .with_near_image(content, encode=False) # <- encode MUST be set to False

OR

>>> from weaviate.util import image_encoder_b64, image_decoder_b64
>>> with open("my_image_path.png", "br") as my_image_file:
...     encoded_image = image_encoder_b64(my_image_file)
>>> content = {
...     'image': encoded_image,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Image', 'description')\
...     .with_near_image(content, encode=False) # <- encode MUST be set to False

Encode Image yourself:

>>> import base64
>>> with open("my_image_path.png", "br") as my_image_file:
...     encoded_image = base64.b64encode(my_image_file.read()).decode("utf-8")
>>> content = {
...     'image': encoded_image,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Image', 'description')\
...     .with_near_image(content, encode=False) # <- encode MUST be set to False

Returns

weaviate.gql.get.GetBuilder: The updated GetBuilder.

Raises

AttributeError: If another ‘near’ filter was already set.

with_near_imu(content: dict, encode: bool = True) → GetBuilder[source]

Set nearIMU filter.

Parameters

contentdict: The content of the nearIMU filter to set. See examples below.
encodebool, optional: Whether to encode the content[“thermal”] to base64 and convert to string. If True, the content[“thermal”] can be an thermal path or a file opened in binary read mode. If False, the content[“thermal”] MUST be a base64 encoded string (NOT bytes, i.e. NOT binary string that looks like this: b’BASE64ENCODED’ but simple ‘BASE64ENCODED’). By default True.

Examples

Content prototype:

>>> content = {
...     'thermal': <str or binary read file>,
...     # certainty ONLY with `cosine` distance specified in the schema
...     'certainty': <float>, # Optional, either 'certainty' OR 'distance'
...     'distance': <float>, # Optional, either 'certainty' OR 'distance'
... }

>>> {
...     'thermal': "e5dc4a4c-ef0f-3aed-89a3-a73435c6bbcf",
...     'certainty': 0.7 # or 'distance'
... }

With encoded True:

>>> content = {
...     'thermal': "my_thermal_path.png",
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('IMU', 'description')\
...     .with_near_thermal(content, encode=True) # <- encode MUST be set to True

OR

>>> my_thermal_file = open("my_thermal_path.png", "br")
>>> content = {
...     'thermal': my_thermal_file,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('IMU', 'description')\
...     .with_near_thermal(content, encode=True) # <- encode MUST be set to True
>>> my_thermal_file.close()

With encoded False:

>>> from weaviate.util import file_encoder_b64
>>> encoded_thermal = file_encoder_b64("my_thermal_path.png")
>>> content = {
...     'thermal': encoded_thermal,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('IMU', 'description')\
...     .with_near_thermal(content, encode=False) # <- encode MUST be set to False

OR

>>> from weaviate.util import file_encoder_b64
>>> with open("my_thermal_path.png", "br") as my_thermal_file:
...     encoded_thermal = file_encoder_b64(my_thermal_file)
>>> content = {
...     'thermal': encoded_thermal,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('IMU', 'description')\
...     .with_near_thermal(content, encode=False) # <- encode MUST be set to False

Encode IMU yourself:

>>> import base64
>>> with open("my_thermal_path.png", "br") as my_thermal_file:
...     encoded_thermal = base64.b64encode(my_thermal_file.read()).decode("utf-8")
>>> content = {
...     'thermal': encoded_thermal,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('IMU', 'description')\
...     .with_near_thermal(content, encode=False) # <- encode MUST be set to False

Returns

weaviate.gql.get.GetBuilder: The updated GetBuilder.

Raises

AttributeError: If another ‘near’ filter was already set.

with_near_object(content: dict) → GetBuilder[source]

Set nearObject filter.

Parameters

contentdict: The content of the nearObject filter to set. See examples below.

Examples

Content prototype:

>>> {
...     'id': "e5dc4a4c-ef0f-3aed-89a3-a73435c6bbcf",
...     # certainty ONLY with `cosine` distance specified in the schema
...     'certainty': <float>, # Optional, either 'certainty' OR 'distance'
...     'distance': <float>, # Optional, either 'certainty' OR 'distance'
... }
>>> # alternatively
>>> {
...     'beacon': "weaviate://localhost/ClassName/e5dc4a4c-ef0f-3aed-89a3-a73435c6bbcf"
...     # certainty ONLY with `cosine` distance specified in the schema
...     'certainty': <float>, # Optional, either 'certainty' OR 'distance'
...     'distance': <float>, # Optional, either 'certainty' OR 'distance'
... }

Returns

weaviate.gql.get.GetBuilder: The updated GetBuilder.

Raises

AttributeError: If another ‘near’ filter was already set.

with_near_text(content: dict) → GetBuilder[source]

Set nearText filter. This filter can be used with text modules (text2vec). E.g.: text2vec-contextionary, text2vec-transformers. NOTE: The ‘autocorrect’ field is enabled only with the text-spellcheck Weaviate module.

Parameters

contentdict: The content of the nearText filter to set. See examples below.

Examples

Content full prototype:

>>> content = {
...     'concepts': <list of str or str>,
...     # certainty ONLY with `cosine` distance specified in the schema
...     'certainty': <float>, # Optional, either 'certainty' OR 'distance'
...     'distance': <float>, # Optional, either 'certainty' OR 'distance'
...     'moveAwayFrom': { # Optional
...         'concepts': <list of str or str>,
...         'force': <float>
...     },
...     'moveTo': { # Optional
...         'concepts': <list of str or str>,
...         'force': <float>
...     },
...     'autocorrect': <bool>, # Optional
... }

Full content:

>>> content = {
...     'concepts': ["fashion"],
...     'certainty': 0.7, # or 'distance'
...     'moveAwayFrom': {
...         'concepts': ["finance"],
...         'force': 0.45
...     },
...     'moveTo': {
...         'concepts': ["haute couture"],
...         'force': 0.85
...     },
...     'autocorrect': True
... }

Partial content:

>>> content = {
...     'concepts': ["fashion"],
...     'certainty': 0.7, # or 'distance'
...     'moveTo': {
...         'concepts': ["haute couture"],
...         'force': 0.85
...     }
... }

Minimal content:

>>> content = {
...     'concepts': "fashion"
... }

Returns

weaviate.gql.get.GetBuilder: The updated GetBuilder.

Raises

AttributeError: If another ‘near’ filter was already set.

with_near_thermal(content: dict, encode: bool = True) → GetBuilder[source]

Set nearThermal filter.

Parameters

contentdict: The content of the nearThermal filter to set. See examples below.
encodebool, optional: Whether to encode the content[“thermal”] to base64 and convert to string. If True, the content[“thermal”] can be an thermal path or a file opened in binary read mode. If False, the content[“thermal”] MUST be a base64 encoded string (NOT bytes, i.e. NOT binary string that looks like this: b’BASE64ENCODED’ but simple ‘BASE64ENCODED’). By default True.

Examples

Content prototype:

>>> content = {
...     'thermal': <str or binary read file>,
...     # certainty ONLY with `cosine` distance specified in the schema
...     'certainty': <float>, # Optional, either 'certainty' OR 'distance'
...     'distance': <float>, # Optional, either 'certainty' OR 'distance'
... }

>>> {
...     'thermal': "e5dc4a4c-ef0f-3aed-89a3-a73435c6bbcf",
...     'certainty': 0.7 # or 'distance'
... }

With encoded True:

>>> content = {
...     'thermal': "my_thermal_path.png",
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Thermal', 'description')\
...     .with_near_thermal(content, encode=True) # <- encode MUST be set to True

OR

>>> my_thermal_file = open("my_thermal_path.png", "br")
>>> content = {
...     'thermal': my_thermal_file,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Thermal', 'description')\
...     .with_near_thermal(content, encode=True) # <- encode MUST be set to True
>>> my_thermal_file.close()

With encoded False:

>>> from weaviate.util import file_encoder_b64
>>> encoded_thermal = file_encoder_b64("my_thermal_path.png")
>>> content = {
...     'thermal': encoded_thermal,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Thermal', 'description')\
...     .with_near_thermal(content, encode=False) # <- encode MUST be set to False

OR

>>> from weaviate.util import file_encoder_b64
>>> with open("my_thermal_path.png", "br") as my_thermal_file:
...     encoded_thermal = file_encoder_b64(my_thermal_file)
>>> content = {
...     'thermal': encoded_thermal,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Thermal', 'description')\
...     .with_near_thermal(content, encode=False) # <- encode MUST be set to False

Encode Thermal yourself:

>>> import base64
>>> with open("my_thermal_path.png", "br") as my_thermal_file:
...     encoded_thermal = base64.b64encode(my_thermal_file.read()).decode("utf-8")
>>> content = {
...     'thermal': encoded_thermal,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Thermal', 'description')\
...     .with_near_thermal(content, encode=False) # <- encode MUST be set to False

Returns

weaviate.gql.get.GetBuilder: The updated GetBuilder.

Raises

AttributeError: If another ‘near’ filter was already set.

with_near_vector(content: dict) → GetBuilder[source]

Set nearVector filter.

Parameters

contentdict: The content of the nearVector filter to set. See examples below.

Examples

Content full prototype:

>>> content = {
...     'vector' : <list of float>,
...     # certainty ONLY with `cosine` distance specified in the schema
...     'certainty': <float>, # Optional, either 'certainty' OR 'distance'
...     'distance': <float>, # Optional, either 'certainty' OR 'distance'
... }

NOTE: Supported types for ‘vector’ are list, ‘numpy.ndarray`, torch.Tensor: and tf.Tensor.

Full content:

>>> content = {
...     'vector' : [.1, .2, .3, .5],
...     'certainty': 0.75, # or 'distance'
... }

Minimal content:

>>> content = {
...     'vector' : [.1, .2, .3, .5]
... }

Or

>>> content = {
...     'vector' : torch.tensor([.1, .2, .3, .5])
... }

Or

>>> content = {
...     'vector' : torch.tensor([[.1, .2, .3, .5]]) # it is going to be squeezed.
... }

Returns

weaviate.gql.get.GetBuilder: The updated GetBuilder.

Raises

AttributeError: If another ‘near’ filter was already set.

with_near_video(content: dict, encode: bool = True) → GetBuilder[source]

Set nearVideo filter.

Parameters

contentdict: The content of the nearVideo filter to set. See examples below.
encodebool, optional: Whether to encode the content[“video”] to base64 and convert to string. If True, the content[“video”] can be an video path or a file opened in binary read mode. If False, the content[“video”] MUST be a base64 encoded string (NOT bytes, i.e. NOT binary string that looks like this: b’BASE64ENCODED’ but simple ‘BASE64ENCODED’). By default True.

Examples

Content prototype:

>>> content = {
...     'video': <str or binary read file>,
...     # certainty ONLY with `cosine` distance specified in the schema
...     'certainty': <float>, # Optional, either 'certainty' OR 'distance'
...     'distance': <float>, # Optional, either 'certainty' OR 'distance'
... }

>>> {
...     'video': "e5dc4a4c-ef0f-3aed-89a3-a73435c6bbcf",
...     'certainty': 0.7 # or 'distance'
... }

With encoded True:

>>> content = {
...     'video': "my_video_path.avi",
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Video', 'description')\
...     .with_near_video(content, encode=True) # <- encode MUST be set to True

OR

>>> my_video_file = open("my_video_path.avi", "br")
>>> content = {
...     'video': my_video_file,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Video', 'description')\
...     .with_near_video(content, encode=True) # <- encode MUST be set to True
>>> my_video_file.close()

With encoded False:

>>> from weaviate.util import file_encoder_b64
>>> encoded_video = file_encoder_b64("my_video_path.avi")
>>> content = {
...     'video': encoded_video,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Video', 'description')\
...     .with_near_video(content, encode=False) # <- encode MUST be set to False

OR

>>> from weaviate.util import file_encoder_b64, video_decoder_b64
>>> with open("my_video_path.avi", "br") as my_video_file:
...     encoded_video = file_encoder_b64(my_video_file)
>>> content = {
...     'video': encoded_video,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Video', 'description')\
...     .with_near_video(content, encode=False) # <- encode MUST be set to False

Encode Video yourself:

>>> import base64
>>> with open("my_video_path.avi", "br") as my_video_file:
...     encoded_video = base64.b64encode(my_video_file.read()).decode("utf-8")
>>> content = {
...     'video': encoded_video,
...     'certainty': 0.7 # or 'distance' instead
... }
>>> query = client.query.get('Video', 'description')\
...     .with_near_video(content, encode=False) # <- encode MUST be set to False

Returns

weaviate.gql.get.GetBuilder: The updated GetBuilder.

Raises

AttributeError: If another ‘near’ filter was already set.

with_offset(offset: int) → GetBuilder[source]

The offset of objects returned, i.e. the starting index of the returned objects should be used in conjunction with the with_limit method.

Parameters

offsetint: The offset used for the returned objects.

Returns

weaviate.gql.get.GetBuilder: The updated GetBuilder.

Raises

ValueError: If ‘offset’ is non-positive.

with_sort(content: list | dict) → GetBuilder[source]

Sort objects based on specific field/s. Multiple sort fields can be used, the objects are going to be sorted according to order of the sort configs passed. This method can be called multiple times and it does not overwrite the last entry but appends it to the previous ones, see examples below.

Parameters

contentUnion[list, dict]: The content of the Sort filter. Can be a single Sort configuration or a list of configurations.

Examples

The content should have this form:

>>> content = {
...     'path': ['name']       # Path to the property that should be used
...     'order': 'asc'         # Sort order, possible values: asc, desc
... }
>>> client.query.get('Author', ['name', 'address'])\
...     .with_sort(content)

Or a list of sort configurations:

>>> content = [
...     {
...         'path': ['name']        # Path to the property that should be used
...         'order': 'asc'          # Sort order, possible values: asc, desc
...     },
...         'path': ['address']     # Path to the property that should be used
...         'order': 'desc'         # Sort order, possible values: asc, desc
...     }
... ]

If we have a list we can add it in 2 ways. Pass the list:

>>> client.query.get('Author', ['name', 'address'])\
...     .with_sort(content)

Or one configuration at a time:

>>> client.query.get('Author', ['name', 'address'])\
...     .with_sort(content[0])
...     .with_sort(content[1])

It is possible to call this method multiple times with lists only too.

Returns

weaviate.gql.get.GetBuilder: The updated GetBuilder.

with_tenant(tenant: str) → GetBuilder[source]: Sets a tenant for the query.

with_where(content: dict) → GetBuilder[source]

Set where filter.

Parameters

contentdict: The content of the where filter to set. See examples below.

Examples

The content prototype is like this:

>>> content = {
...     'operator': '<operator>',
...     'operands': [
...         {
...             'path': [path],
...             'operator': '<operator>'
...             '<valueType>': <value>
...         },
...         {
...             'path': [<matchPath>],
...             'operator': '<operator>',
...             '<valueType>': <value>
...         }
...     ]
... }

This is a complete where filter but it does not have to be like this all the time.

Single operand:

>>> content = {
...     'path': ["wordCount"],    # Path to the property that should be used
...     'operator': 'GreaterThan',  # operator
...     'valueInt': 1000       # value (which is always = to the type of the path property)
... }

Or

>>> content = {
...     'path': ["id"],
...     'operator': 'Equal',
...     'valueString': "e5dc4a4c-ef0f-3aed-89a3-a73435c6bbcf"
... }

Multiple operands:

>>> content = {
...     'operator': 'And',
...     'operands': [
...         {
...             'path': ["wordCount"],
...             'operator': 'GreaterThan',
...             'valueInt': 1000
...         },
...         {
...             'path': ["wordCount"],
...             'operator': 'LessThan',
...             'valueInt': 1500
...         }
...     ]
... }

Returns

weaviate.gql.get.GetBuilder: The updated GetBuilder.

class weaviate.gql.get.GroupBy(path: List[str], groups: int, objects_per_group: int)[source]

Bases: object

groups: int

objects_per_group: int

path: List[str]

class weaviate.gql.get.Hybrid(query: str, alpha: float | None, vector: List[float] | None, properties: List[str] | None, fusion_type: weaviate.gql.get.HybridFusion | None)[source]

Bases: object

alpha: float | None

fusion_type: HybridFusion | None

properties: List[str] | None

query: str

vector: List[float] | None

class weaviate.gql.get.HybridFusion(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]

Bases: str, BaseEnum

RANKED = 'rankedFusion'

RELATIVE_SCORE = 'relativeScoreFusion'

class weaviate.gql.get.LinkTo(link_on: str, linked_class: str, properties: Sequence[Union[str, ForwardRef('LinkTo')]])[source]

Bases: object

link_on: str

linked_class: str

properties: Sequence[str | LinkTo]

weaviate.gql.multi_get module

GraphQL Get command.

class weaviate.gql.multi_get.MultiGetBuilder(get_builder: List[GetBuilder], connection: Connection)[source]

Bases: GraphQL

GetBuilder class used to create GraphQL queries.

Initialize a MultiGetBuilder class instance.

Parameters

get_builderlist of GetBuilder: GetBuilder objects for a single request each.
connectionweaviate.connect.Connection: Connection object to an active and running Weaviate instance.

Examples

To create a ‘multi_get’ object using several ‘get’ request at the same time use:

>>>    client.query.multi_get(
... [
...    client.query.get("Ship", ["name"]).with_alias("one"),
...    client.query.get("Ship", ["size"]).with_alias("two"),
...    client.query.get("Person", ["name"])
... ]
with_alias() needs to be used if the same 'class_name' is used twice during the same 'multi_get' request.

Raises

TypeError: If ‘get_builder’ is of wrong type.

build() → str[source]

Build query filter as a string.

Returns

str: The GraphQL query as a string.

weaviate.gql.query module

GraphQL query module.

class weaviate.gql.query.Query(connection: Connection)[source]

Bases: object

Query class used to make get and/or aggregate GraphQL queries.

Initialize a Classification class instance.

Parameters

connectionweaviate.connect.Connection: Connection object to an active and running Weaviate instance.

aggregate(class_name: str) → AggregateBuilder[source]

Instantiate an AggregateBuilder for GraphQL aggregate requests.

Parameters

class_namestr: Class name of the objects to be aggregated.

Returns

AggregateBuilder: An AggregateBuilder to make GraphQL aggregate requests from weaviate.

get(class_name: str, properties: Sequence[str | LinkTo] | str | None = None) → GetBuilder[source]

Instantiate a GetBuilder for GraphQL get requests.

Parameters

class_namestr: Class name of the objects to interact with.
propertieslist of str and ReferenceProperty, str or None: Properties of the objects to get, by default None

Returns

GetBuilder: A GetBuilder to make GraphQL get requests from weaviate.

multi_get(get_builder: List[GetBuilder]) → MultiGetBuilder[source]

Instantiate a MultiGetBuilder for GraphQL multi_get requests. Bundles multiple get requests into one.

Parameters

get_builderlist of GetBuilder: List of GetBuilder objects for a single request each.

Returns

MultiGetBuilder: A MultiGetBuilder to make GraphQL get multiple requests from weaviate.

raw(gql_query: str) → Dict[str, Any][source]

Allows to send simple graph QL string queries. Be cautious of injection risks when generating query strings.

Parameters

gql_querystr: GraphQL query as a string.

Returns

dict: Data response of the query.

Examples

>>> query = """
... {
...     Get {
...         Article(limit: 2) {
...         title
...         hasAuthors {
...             ... on Author {
...                 name
...                 }
...             }
...         }
...     }
... }
... """
>>> client.query.raw(query)
{
"data": {
    "Get": {
    "Article": [
        {
        "hasAuthors": [
            {
            "name": "Jonathan Wilson"
            }
        ],
        "title": "Sergio Agüero has been far more than a great goalscorer for
                    Manchester City"
        },
        {
        "hasAuthors": [
            {
            "name": "Emma Elwick-Bates"
            }
        ],
        "title": "At Swarovski, Giovanna Engelbert Is Crafting Jewels As Exuberantly
                    Joyful As She Is"
        }
    ]
    }
},
"errors": null
}

Raises

TypeError: If ‘gql_query’ is not of type str.
requests.ConnectionError: If the network connection to weaviate fails.
weaviate.UnexpectedStatusCodeException: If weaviate reports a none OK status.