Reference API

vespa.application

Vespa

class vespa.application.Vespa(url: str, port: Optional[int] = None, deployment_message: Optional[List[str]] = None, cert: Optional[str] = None, key: Optional[str] = None, output_file: IO = <_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>, application_package: Optional[vespa.package.ApplicationPackage] = None)

__init__(url: str, port: Optional[int] = None, deployment_message: Optional[List[str]] = None, cert: Optional[str] = None, key: Optional[str] = None, output_file: IO = <_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>, application_package: Optional[vespa.package.ApplicationPackage] = None) → None

Establish a connection with an existing Vespa application.

Parameters:

url – Vespa instance URL.
port – Vespa instance port.
deployment_message – Message returned by Vespa engine after deployment. Used internally by deploy methods.
cert – Path to certificate and key file in case the ‘key’ parameter is none. If ‘key’ is not None, this should be the path of the certificate file.
key – Path to the key file.
output_file – Output file to write output messages.
application_package – Application package definition used to deploy the application.

>>> Vespa(url = "https://cord19.vespa.ai")  # doctest: +SKIP

>>> Vespa(url = "http://localhost", port = 8080)
Vespa(http://localhost, 8080)

>>> Vespa(url = "https://api.vespa-external.aws.oath.cloud", port = 4443, cert = "/path/to/cert-and-key.pem")  # doctest: +SKIP

application_package: Get application package definition, if available.

asyncio(connections: Optional[int] = 100, total_timeout: int = 10) → vespa.application.VespaAsync

Access Vespa asynchronous connection layer

Parameters:	connections – Number of allowed concurrent connections total_timeout – Total timeout in secs.
Returns:	Instance of Vespa asynchronous layer.

delete_all_docs(content_cluster_name: str, schema: str, namespace: str = None) → requests.models.Response

Delete all documents associated with the schema

Parameters:	content_cluster_name – Name of content cluster to GET from, or visit. schema – The schema that we are deleting data from. namespace – The namespace that we are deleting data from. If no namespace is provided the schema is used.
Returns:	Response of the HTTP DELETE request.

delete_batch(batch: List[Dict], schema: Optional[str] = None, asynchronous=True, connections: Optional[int] = 100, total_timeout: int = 100, namespace: Optional[str] = None)

Delete a batch of data from a Vespa app.

Parameters:

batch – A list of dict containing the key ‘id’.
schema – The schema that we are deleting data from. The schema is optional in case it is possible to infer the schema from the application package.
asynchronous – Set True to get data in async mode. Default to True.
connections – Number of allowed concurrent connections, valid only if asynchronous=True.
total_timeout – Total timeout in secs for each of the concurrent requests when using asynchronous=True.
namespace – The namespace that we are deleting data from. If no namespace is provided the schema is used.

Returns:

List of HTTP POST responses

delete_data(schema: str, data_id: str, namespace: str = None) → vespa.io.VespaResponse

Delete a data point from a Vespa app.

Parameters:	schema – The schema that we are deleting data from. data_id – Unique id associated with this data point. namespace – The namespace that we are deleting data from. If no namespace is provided the schema is used.
Returns:	Response of the HTTP DELETE request.

feed_batch(batch: List[Dict], schema: Optional[str] = None, asynchronous=True, connections: Optional[int] = 100, total_timeout: int = 240, namespace: Optional[str] = None, batch_size=1000, output: bool = True)

Feed a batch of data to a Vespa app.

Parameters:

batch – A list of dict containing the keys ‘id’ and ‘fields’ to be used in the feed_data_point().
schema – The schema that we are sending data to. The schema is optional in case it is possible to infer the schema from the application package.
asynchronous – Set True to send data in async mode. Default to True.
connections – Number of allowed concurrent connections, valid only if asynchronous=True.
total_timeout – Total timeout in secs for each of the concurrent requests when using asynchronous=True.
namespace – The namespace that we are sending data to. If no namespace is provided the schema is used.
batch_size – The number of documents to feed per batch.
output – Prints a final message about the feed result.

Returns:

List of HTTP POST responses

feed_data_point(schema: str, data_id: str, fields: Dict, namespace: str = None) → vespa.io.VespaResponse

Feed a data point to a Vespa app.

Parameters:	schema – The schema that we are sending data to. data_id – Unique id associated with this data point. fields – Dict containing all the fields required by the schema. namespace – The namespace that we are sending data to.
Returns:	Response of the HTTP POST request.

feed_df(df: pandas.core.frame.DataFrame, include_id: bool = True, id_field='id', **kwargs)

Feed data contained in a DataFrame.

Parameters:	df – A DataFrame containing a required ‘id’ column and the remaining fields to be fed. include_id – Include id on the fields to be fed. Default to True. id_field – Name of the column containing the id field. kwargs – Additional parameters are passed to `feed_batch()`.
Returns:	List of HTTP POST responses

get_application_status() → Optional[requests.models.Response]

Get application status.

Returns:

get_batch(batch: List[Dict], schema: Optional[str] = None, asynchronous=True, connections: Optional[int] = 100, total_timeout: int = 100, namespace: Optional[str] = None)

Get a batch of data from a Vespa app.

Parameters:

batch – A list of dict containing the key ‘id’.
schema – The schema that we are getting data from. The schema is optional in case it is possible to infer the schema from the application package.
asynchronous – Set True to get data in async mode. Default to True.
connections – Number of allowed concurrent connections, valid only if asynchronous=True.
total_timeout – Total timeout in secs for each of the concurrent requests when using asynchronous=True.
namespace – The namespace that we are getting data from. If no namespace is provided the schema is used.

Returns:

List of HTTP POST responses

get_data(schema: str, data_id: str, namespace: str = None) → vespa.io.VespaResponse

Get a data point from a Vespa app.

Parameters:	schema – The schema that we are getting data from. data_id – Unique id associated with this data point. namespace – The namespace that we are getting data from. If no namespace is provided the schema is used.
Returns:	Response of the HTTP GET request.

get_model_endpoint(model_id: Optional[str] = None) → Optional[requests.models.Response]: Get model evaluation endpoints.

get_model_from_application_package(model_name: str): Get model definition from application package, if available.

predict(x, model_id, function_name='output_0')

Obtain a stateless model evaluation.

Parameters:	x – Input where the format depends on the task that the model is serving. model_id – The id of the model used to serve the prediction. function_name – The name of the output function to be evaluated.
Returns:	Model prediction.

query(body: Optional[Dict] = None) → vespa.io.VespaQueryResponse

Send a query request to the Vespa application.

Send ‘body’ containing all the request parameters.

Parameters:	body – Dict containing all the request parameters.
Returns:	The response from the Vespa application.

query_batch(body_batch: Optional[List[Dict]] = None, asynchronous=True, connections: Optional[int] = 100, total_timeout: int = 100, **kwargs)

Send queries in batch to a Vespa app.

Parameters:

body_batch – A list of dict containing all the request parameters. Set to None if using ‘query_batch’. of the field to use to recall and the second element is a list of the values to be recalled.
asynchronous – Set True to send data in async mode. Default to True.
connections – Number of allowed concurrent connections, valid only if asynchronous=True.
total_timeout – Total timeout in secs for each of the concurrent requests when using asynchronous=True.
kwargs – Additional parameters to be sent along the request.

Returns:

List of HTTP POST responses

update_batch(batch: List[Dict], schema: Optional[str] = None, asynchronous=True, connections: Optional[int] = 100, total_timeout: int = 100, namespace: Optional[str] = None)

Update a batch of data in a Vespa app.

Parameters:

batch – A list of dict containing the keys ‘id’, ‘fields’ and ‘create’ (create defaults to False).
schema – The schema that we are updating data to. The schema is optional in case it is possible to infer the schema from the application package.
asynchronous – Set True to update data in async mode. Default to True.
connections – Number of allowed concurrent connections, valid only if asynchronous=True.
total_timeout – Total timeout in secs for each of the concurrent requests when using asynchronous=True.
namespace – The namespace that we are updating data. If no namespace is provided the schema is used.

Returns:

List of HTTP POST responses

update_data(schema: str, data_id: str, fields: Dict, create: bool = False, namespace: str = None) → vespa.io.VespaResponse

Update a data point in a Vespa app.

Parameters:	schema – The schema that we are updating data. data_id – Unique id associated with this data point. fields – Dict containing all the fields you want to update. create – If true, updates to non-existent documents will create an empty document to update namespace – The namespace that we are updating data. If no namespace is provided the schema is used.
Returns:	Response of the HTTP PUT request.

wait_for_application_up(max_wait: int) → None

Wait for application ready.

Parameters:	max_wait – Seconds to wait for the application endpoint
Raises:	RuntimeError – If the application did not start in the time given by the :max_wait: param.
Returns:

VespaSync

class vespa.application.VespaSync(app: vespa.application.Vespa, pool_maxsize: int = 10)

__init__(app: vespa.application.Vespa, pool_maxsize: int = 10) → None: Initialize self. See help(type(self)) for accurate signature.

delete_all_docs(content_cluster_name: str, schema: str, namespace: str = None) → requests.models.Response

Delete all documents associated with the schema

Parameters:	content_cluster_name – Name of content cluster to GET from, or visit. schema – The schema that we are deleting data from. namespace – The namespace that we are deleting data from.
Returns:	Response of the HTTP DELETE request.
Raises:	HTTPError – if one occurred

delete_data(schema: str, data_id: str, namespace: str = None) → vespa.io.VespaResponse

Delete a data point from a Vespa app.

Parameters:	schema – The schema that we are deleting data from. data_id – Unique id associated with this data point. namespace – The namespace that we are deleting data from.
Returns:	Response of the HTTP DELETE request.
Raises:	HTTPError – if one occurred

feed_data_point(schema: str, data_id: str, fields: Dict, namespace: str = None) → vespa.io.VespaResponse

Feed a data point to a Vespa app.

Parameters:	schema – The schema that we are sending data to. data_id – Unique id associated with this data point. fields – Dict containing all the fields required by the schema. namespace – The namespace that we are sending data to. If no namespace is provided the schema is used.
Returns:	Response of the HTTP POST request.
Raises:	HTTPError – if one occurred

get_data(schema: str, data_id: str, namespace: str = None) → vespa.io.VespaResponse

Get a data point from a Vespa app.

Parameters:	schema – The schema that we are getting data from. data_id – Unique id associated with this data point. namespace – The namespace that we are getting data from.
Returns:	Response of the HTTP GET request.
Raises:	HTTPError – if one occurred

get_model_endpoint(model_id: Optional[str] = None) → Optional[dict]: Get model evaluation endpoints.

predict(model_id, function_name, encoded_tokens)

Obtain a stateless model evaluation.

Parameters:	model_id – The id of the model used to serve the prediction. function_name – The name of the output function to be evaluated. encoded_tokens – URL-encoded input to the model
Returns:	Model prediction.

query(body: Optional[Dict] = None) → vespa.io.VespaQueryResponse

Send a query request to the Vespa application.

Send ‘body’ containing all the request parameters.

Parameters:	body – Dict containing all the request parameters.
Returns:	Either the request body if debug_request is True or the result from the Vespa application
Raises:	HTTPError – if one occurred

update_data(schema: str, data_id: str, fields: Dict, create: bool = False, namespace: str = None) → vespa.io.VespaResponse

Update a data point in a Vespa app.

Parameters:	schema – The schema that we are updating data. data_id – Unique id associated with this data point. fields – Dict containing all the fields you want to update. create – If true, updates to non-existent documents will create an empty document to update namespace – The namespace that we are updating data.
Returns:	Response of the HTTP PUT request.
Raises:	HTTPError – if one occurred

VespaAsync

class vespa.application.VespaAsync(app: vespa.application.Vespa, connections: Optional[int] = 100, total_timeout: int = 10)

__init__(app: vespa.application.Vespa, connections: Optional[int] = 100, total_timeout: int = 10) → None: Initialize self. See help(type(self)) for accurate signature.

Utility functions

vespa.application.parse_feed_df(df: pandas.core.frame.DataFrame, include_id: bool, id_field='id', id_prefix='') → List[Dict[str, Any]]

Convert a DataFrame into batch format for feeding

Parameters:	df – DataFrame with the following required columns [“id”]. Additional columns are assumed to be fields. include_id – Include id on the fields to be fed. id_field – Name of the column containing the id field. id_prefix – Add a string prefix to ID field, e.g. “id:namespace:schema::”
Returns:	List of Dict containing ‘id’ and ‘fields’.

vespa.application.df_to_vespafeed(df: pandas.core.frame.DataFrame, schema_name: str, id_field='id', namespace='') → str

Convert a DataFrame into a string in Vespa JSON feed format, see https://docs.vespa.ai/en/reference/document-json-format.html

Parameters:	df – DataFrame with the following required columns [“id”]. Additional columns are assumed to be fields. schema_name – Schema name id_field – Name of the column containing the id field. namespace – Set if namespace != schema_name
Returns:	JSON string in Vespa feed format

vespa.application.raise_for_status(response: requests.models.Response) → None

Raises an appropriate error if necessary.

If the response contains an error message, VespaError is raised along with HTTPError to provide more details.

Parameters:	response – Response object from Vespa API.
Raises:	HTTPError – If status_code is between 400 and 599. VespaError – If the response JSON contains an error message.

vespa.deployment

VespaDeployment

class vespa.deployment.VespaDeployment

read_app_package_from_disk(application_root: pathlib.Path) → bytes

Read the contents of an application package on disk into a zip file.

Parameters:	application_root – Application package directory root
Returns:	The zipped application package as bytes.

VespaDocker

class vespa.deployment.VespaDocker(port: int = 8080, container_memory: Union[str, int] = 4294967296, output_file: IO = <_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>, container: Optional[docker.models.containers.Container] = None, container_image: str = 'vespaengine/vespa', cfgsrv_port: int = 19071, debug_port: int = 5005)

__init__(port: int = 8080, container_memory: Union[str, int] = 4294967296, output_file: IO = <_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>, container: Optional[docker.models.containers.Container] = None, container_image: str = 'vespaengine/vespa', cfgsrv_port: int = 19071, debug_port: int = 5005) → None

Manage Docker deployments.

Parameters:

port – Container port.
cfgsrv_port – Config Server port.
debug_port – Port to connect to, to debug the vespa container.
output_file – Output file to write output messages.
container_memory – Docker container memory available to the application.
container – Used when instantiating VespaDocker from a running container.
container_image – Docker container image.

deploy(application_package: vespa.package.ApplicationPackage, debug: bool = False) → vespa.application.Vespa

Deploy the application package into a Vespa container.

Parameters:	application_package – ApplicationPackage to be deployed. debug – Add the configured debug_port to the docker port mapping.
Returns:	a Vespa connection instance.

deploy_from_disk(application_name: str, application_root: pathlib.Path, debug: bool = False) → vespa.application.Vespa

Deploy from a directory tree. Used when making changes to application package files not supported by pyvespa - this is why this method is not found in the ApplicationPackage class.

Parameters:	application_name – Application package name. application_root – Application package directory root debug – Add the configured debug_port to the docker port mapping.
Returns:	a Vespa connection instance.

static from_container_name_or_id(name_or_id: str, output_file: IO = <_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>) → vespa.deployment.VespaDocker

Instantiate VespaDocker from a running container.

Parameters:	name_or_id – Name or id of the container. output_file – Output file to write output messages.
Raises:	ValueError – Exception if container not found
Returns:	VespaDocker instance associated with the running container.

restart_services() → None

Restart Vespa services inside the docker image, it is equivalent to calling self.stop_services() followed by self.start_services().

Raises:	RuntimeError – if a container has not been set
Returns:	None

start_services() → None

Start Vespa services inside the docker image, first waiting for the Config Server, then for other services.

Raises:	RuntimeError – if a container has not been set
Returns:	None

stop_services() → None

Stop Vespa services inside the docker image, first stopping the services, then stopping the Config Server.

Raises:	RuntimeError – if a container has not been set
Returns:	None

wait_for_config_server_start(max_wait: int) → None

Waits for Config Server to start inside the Docker image

Parameters:	max_wait – Seconds to wait for the application endpoint
Raises:	RuntimeError – Raises runtime error if the config server does not start within max_wait
Returns:

VespaCloud

class vespa.deployment.VespaCloud(tenant: str, application: str, application_package: vespa.package.ApplicationPackage, key_location: Optional[str] = None, key_content: Optional[str] = None, output_file: IO = <_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>)

__init__(tenant: str, application: str, application_package: vespa.package.ApplicationPackage, key_location: Optional[str] = None, key_content: Optional[str] = None, output_file: IO = <_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>) → None

Deploy application to the Vespa Cloud (cloud.vespa.ai)

Parameters:

tenant – Tenant name registered in the Vespa Cloud.
application – Application name registered in the Vespa Cloud.
application_package – ApplicationPackage to be deployed.
key_location – Location of the private key used for signing HTTP requests to the Vespa Cloud.
key_content – Content of the private key used for signing HTTP requests to the Vespa Cloud. Use only when key file is not available.
output_file – Output file to write output messages.

delete(instance: str) → None

Delete the specified instance from the dev environment in the Vespa Cloud.

Parameters:	instance – Name of the instance to delete.
Returns:

deploy(instance: str, disk_folder: Optional[str] = None) → vespa.application.Vespa

Deploy the given application package as the given instance in the Vespa Cloud dev environment.

Parameters:	instance – Name of this instance of the application, in the Vespa Cloud. disk_folder – Disk folder to save the required Vespa config files. Default to application name folder within user’s current working directory.
Returns:	a Vespa connection instance.

deploy_from_disk(instance: str, application_root: pathlib.Path) → vespa.application.Vespa

Deploy from a directory tree. Used when making changes to application package files not supported by pyvespa. NB: Requires certificate and key to be generated with ‘vespa auth cert’.

Parameters:	instance – Name of the instance where the application is to be run application_root – Application package directory root
Returns:	a Vespa connection instance.

vespa.exceptions

VespaError

class vespa.exceptions.VespaError: Vespa returned an error response

vespa.io

VespaQueryResponse

class vespa.io.VespaQueryResponse(json, status_code, url, request_body=None)

__init__(json, status_code, url, request_body=None) → None: Initialize self. See help(type(self)) for accurate signature.

get_hits(format_function=<function trec_format>, **kwargs) → pandas.core.frame.DataFrame

Get Vespa hits according to format_function format.

Parameters:	format_function – function to format the raw Vespa result. Should take raw vespa result as first argument. kwargs – Extra arguments to be passed to format_function.
Returns:	Output of the format_function.

get_json()

For debugging when the response does not have hits.

Returns:	JSON object with full response

get_status_code()

For debugging when the response does not have hits.

Returns:	HTTP status code

VespaResponse

class vespa.io.VespaResponse(json, status_code, url, operation_type)

__init__(json, status_code, url, operation_type) → None: Initialize self. See help(type(self)) for accurate signature.

Utility functions

vespa.io.trec_format(vespa_result, id_field: Optional[str] = None, qid: int = 0) → pandas.core.frame.DataFrame

Function to format Vespa output according to TREC format.

TREC format include qid, doc_id, score and rank.

Parameters:	vespa_result – raw Vespa result from query. id_field – Name of the Vespa field to use as ‘doc_id’ value. qid – custom query id.
Returns:	pandas DataFrame with columns qid, doc_id, score and rank.

vespa.package

ApplicationPackage

class vespa.package.ApplicationPackage(name: str, schema: Optional[List[vespa.package.Schema]] = None, query_profile: Optional[vespa.package.QueryProfile] = None, query_profile_type: Optional[vespa.package.QueryProfileType] = None, stateless_model_evaluation: bool = False, create_schema_by_default: bool = True, create_query_profile_by_default: bool = True, configurations: Optional[List[vespa.package.ApplicationConfiguration]] = None, validations: Optional[List[vespa.package.Validation]] = None, components: Optional[List[vespa.package.Component]] = None)

__init__(name: str, schema: Optional[List[vespa.package.Schema]] = None, query_profile: Optional[vespa.package.QueryProfile] = None, query_profile_type: Optional[vespa.package.QueryProfileType] = None, stateless_model_evaluation: bool = False, create_schema_by_default: bool = True, create_query_profile_by_default: bool = True, configurations: Optional[List[vespa.package.ApplicationConfiguration]] = None, validations: Optional[List[vespa.package.Validation]] = None, components: Optional[List[vespa.package.Component]] = None) → None

Create an Application Package. An ApplicationPackage instance comes with a default Schema that contains a default Document

Parameters:

name – Application name. Cannot contain ‘-’ or ‘_’.
schema – List of Schema`s of the application. If `None, an empty Schema with the same name of the application will be created by default.
query_profile – QueryProfile of the application. If None, a QueryProfile named default with QueryProfileType named root will be created by default.
query_profile_type – QueryProfileType of the application. If None, a empty QueryProfileType named root will be created by default.
stateless_model_evaluation – Enable stateless model evaluation. Default to False.
create_schema_by_default – Include a Schema with the same name as the application if no Schema is provided in the schema argument.
create_query_profile_by_default – Include a default QueryProfile and QueryProfileType in case it is not explicitly defined by the user in the query_profile and query_profile_type parameters.
configurations – List of ApplicationConfiguration that contains configurations for the application.
validations – Optional list of Validation to be overridden.
components – List of Component that contains configurations for application components.

The easiest way to get started is to create a default application package:

>>> ApplicationPackage(name="testapp")
ApplicationPackage('testapp', [Schema('testapp', Document(None, None, None), None, None, [], False, None, [], None)], QueryProfile(None), QueryProfileType(None))

It will create a default Schema, QueryProfile and QueryProfileType that you can then populate with specifics of your application.

add_schema(*schemas) → None

Add Schema’s to the application package.

Parameters:	schemas – schemas to be added
Returns:

to_files(root: pathlib.Path) → None

Export the application package as a directory tree.

Parameters:	root – Directory to export files to
Returns:

to_zip() → _io.BytesIO: Return the application package as zipped bytes, to be used in a subsequent deploy :return: BytesIO buffer

to_zipfile(zfile: pathlib.Path) → None

Export the application package as a deployable zipfile. See application packages for deployment options.

Parameters:	zfile – Filename to export to
Returns:

ApplicationConfiguration

class vespa.package.ApplicationConfiguration(name: str, value: Union[str, Dict[str, Union[Dict, str]]])

__init__(name: str, value: Union[str, Dict[str, Union[Dict, str]]]) → str

Create a Vespa Schema.

Check the Config documentation for more detailed information about generic configuration.

Parameters:	name – Configuration name. value – Either a string or a Dict (it may be a nested dict) of values.

Example:

>>> ApplicationConfiguration(
...     name="container.handler.observability.application-userdata",
...     value={"version": "my-version"}
... )
ApplicationConfiguration(name="container.handler.observability.application-userdata")

Document

class vespa.package.Document(fields: Optional[List[vespa.package.Field]] = None, inherits: Optional[str] = None, structs: Optional[List[vespa.package.Struct]] = None)

__init__(fields: Optional[List[vespa.package.Field]] = None, inherits: Optional[str] = None, structs: Optional[List[vespa.package.Struct]] = None) → None

Create a Vespa Document.

Check the Vespa documentation for more detailed information about documents.

Parameters:	fields – A list of `Field` to include in the document’s schema.

To create a Document:

>>> Document()
Document(None, None, None)

>>> Document(fields=[Field(name="title", type="string")])
Document([Field('title', 'string', None, None, None, None, None, None, None, None, True, None, None, None, [])], None, None)

>>> Document(fields=[Field(name="title", type="string")], inherits="context")
Document([Field('title', 'string', None, None, None, None, None, None, None, None, True, None, None, None, [])], context, None)

add_fields(*fields) → None

Add Field’s to the document.

Parameters:	fields – fields to be added
Returns:

add_structs(*structs) → None

Add Struct’s to the document.

Parameters:	structs – structs to be added
Returns:

DocumentSummary

class vespa.package.DocumentSummary(name: str, inherits: Optional[str] = None, summary_fields: Optional[List[vespa.package.Summary]] = None, from_disk: Optional[Literal[True]] = None, omit_summary_features: Optional[Literal[True]] = None)

__init__(name: str, inherits: Optional[str] = None, summary_fields: Optional[List[vespa.package.Summary]] = None, from_disk: Optional[Literal[True]] = None, omit_summary_features: Optional[Literal[True]] = None) → None

Create a Document Summary. Check the Vespa documentation for more detailed information about documment-summary.

Parameters:	name – Name of the document-summary. inherits – Name of another document-summary from which this inherits from. summary_fields – List of summaries used in this document-summary. from_disk – Marks this document-summary as accessing fields on disk. omit_summary_features – Specifies that summary-features should be omitted from this document summary.

>>> DocumentSummary(
...     name="document-summary",
... )
DocumentSummary('document-summary', None, None, None, None)

>>> DocumentSummary(
...     name="which-inherits",
...     inherits="base-document-summary",
... )
DocumentSummary('which-inherits', 'base-document-summary', None, None, None)

>>> DocumentSummary(
...     name="with-field",
...     summary_fields=[Summary("title", "string", [("source", "title")])]
... )
DocumentSummary('with-field', None, [Summary('title', 'string', [('source', 'title')])], None, None)

>>> DocumentSummary(
...     name="with-bools",
...     from_disk=True,
...     omit_summary_features=True,
... )
DocumentSummary('with-bools', None, None, True, True)

Field

class vespa.package.Field(name: str, type: str, indexing: Optional[List[str]] = None, index: Optional[str] = None, attribute: Optional[List[str]] = None, ann: Optional[vespa.package.HNSW] = None, match: Optional[List[Union[str, Tuple[str, str]]]] = None, weight: Optional[int] = None, bolding: Optional[Literal[True]] = None, summary: Optional[vespa.package.Summary] = None, is_document_field: Optional[bool] = True, **kwargs)

__init__(name: str, type: str, indexing: Optional[List[str]] = None, index: Optional[str] = None, attribute: Optional[List[str]] = None, ann: Optional[vespa.package.HNSW] = None, match: Optional[List[Union[str, Tuple[str, str]]]] = None, weight: Optional[int] = None, bolding: Optional[Literal[True]] = None, summary: Optional[vespa.package.Summary] = None, is_document_field: Optional[bool] = True, **kwargs) → None

Create a Vespa field.

Check the Vespa documentation for more detailed information about fields.

Once we have an ApplicationPackage instance containing a Schema and a Document, we usually want to add fields so that we can store our data in a structured manner. We can accomplish that by creating Field instances and adding those to the ApplicationPackage instance via Schema and Document methods.

Key query_command:
Parameters:	name – Field name. type – Field data type. indexing – Configures how to process data of a field during indexing. index – Sets index parameters. Content in fields with index are normalized and tokenized by default. attribute – Specifies a property of an index structure attribute. ann – Add configuration for approximate nearest neighbor. match – Set properties that decide how the matching method for this field operate. weight – Sets the weight of the field, using when calculating Rank Scores. bolding – Whether to highlight matching query terms in the summary. summary – Add configuration for summary of the field. is_document_field – Whether the field is a document field or part of the schema. True by default.
Key stemming:	Add configuration for stemming of the field.
Key rank:	Add configuration for ranking calculations of the field.
	Add configuration for query-command of the field.
Key struct_fields:
	Add struct-fields to the field.

>>> Field(name = "title", type = "string", indexing = ["index", "summary"], index = "enable-bm25")
Field('title', 'string', ['index', 'summary'], 'enable-bm25', None, None, None, None, None, None, True, None, None, None, [])

>>> Field(
...     name = "abstract",
...     type = "string",
...     indexing = ["attribute"],
...     attribute=["fast-search", "fast-access"]
... )
Field('abstract', 'string', ['attribute'], None, ['fast-search', 'fast-access'], None, None, None, None, None, True, None, None, None, [])

>>> Field(name="tensor_field",
...     type="tensor<float>(x[128])",
...     indexing=["attribute"],
...     ann=HNSW(
...         distance_metric="euclidean",
...         max_links_per_node=16,
...         neighbors_to_explore_at_insert=200,
...     ),
... )
Field('tensor_field', 'tensor<float>(x[128])', ['attribute'], None, None, HNSW('euclidean', 16, 200), None, None, None, None, True, None, None, None, [])

>>> Field(
...     name = "abstract",
...     type = "string",
...     match = ["exact", ("exact-terminator", '"@%"',)],
... )
Field('abstract', 'string', None, None, None, None, ['exact', ('exact-terminator', '"@%"')], None, None, None, True, None, None, None, [])

>>> Field(
...     name = "abstract",
...     type = "string",
...     weight = 200,
... )
Field('abstract', 'string', None, None, None, None, None, 200, None, None, True, None, None, None, [])

>>> Field(
...     name = "abstract",
...     type = "string",
...     bolding = True,
... )
Field('abstract', 'string', None, None, None, None, None, None, True, None, True, None, None, None, [])

>>> Field(
...     name = "abstract",
...     type = "string",
...     summary = Summary(None, None, ["dynamic", ["bolding", "on"]]),
... )
Field('abstract', 'string', None, None, None, None, None, None, None, Summary(None, None, ['dynamic', ['bolding', 'on']]), True, None, None, None, [])

>>> Field(
...     name = "abstract",
...     type = "string",
...     stemming = "shortest",
... )
Field('abstract', 'string', None, None, None, None, None, None, None, None, True, 'shortest', None, None, [])

>>> Field(
...     name = "abstract",
...     type = "string",
...     rank = "filter",
... )
Field('abstract', 'string', None, None, None, None, None, None, None, None, True, None, 'filter', None, [])

>>> Field(
...     name = "abstract",
...     type = "string",
...     query_command = ['"exact %%"'],
... )
Field('abstract', 'string', None, None, None, None, None, None, None, None, True, None, None, ['"exact %%"'], [])

>>> Field(
...     name = "abstract",
...     type = "string",
...     struct_fields = [
...         StructField(
...             name = "first_name",
...             indexing = ["attribute"],
...             attribute = ["fast-search"],
...         ),
...     ],
... )
Field('abstract', 'string', None, None, None, None, None, None, None, None, True, None, None, None, [StructField('first_name', ['attribute'], ['fast-search'], None, None, None)])

add_struct_fields(*struct_fields) → None

Add StructField’s to the Field.

Parameters:	struct_fields – struct-fields to be added

FieldSet

class vespa.package.FieldSet(name: str, fields: List[str])

__init__(name: str, fields: List[str]) → None

Create a Vespa field set.

A fieldset groups fields together for searching. Check the Vespa documentation for more detailed information about field sets.

Parameters:	name – Name of the fieldset fields – Field names to be included in the fieldset.

>>> FieldSet(name="default", fields=["title", "body"])
FieldSet('default', ['title', 'body'])

FirstPhaseRanking

class vespa.package.FirstPhaseRanking(expression: str, keep_rank_count: Optional[int] = None, rank_score_drop_limit: Optional[float] = None)

__init__(expression: str, keep_rank_count: Optional[int] = None, rank_score_drop_limit: Optional[float] = None) → None

Create a Vespa first phase ranking configuration.

This is the initial ranking performed on all matching documents. Check the Vespa documentation for more detailed information about first phase ranking configuration.

Parameters:	expression – Specify the ranking expression to be used for first phase of ranking. Check also the Vespa documentation for ranking expression. keep_rank_count – How many documents to keep the first phase top rank values for. Default value is 10000. rank_score_drop_limit – Drop all hits with a first phase rank score less than or equal to this floating point number.

>>> FirstPhaseRanking("myFeature * 10")
FirstPhaseRanking('myFeature * 10', None, None)

>>> FirstPhaseRanking(expression="myFeature * 10", keep_rank_count=50, rank_score_drop_limit=10)
FirstPhaseRanking('myFeature * 10', 50, 10)

Function

class vespa.package.Function(name: str, expression: str, args: Optional[List[str]] = None)

__init__(name: str, expression: str, args: Optional[List[str]] = None) → None

Create a Vespa rank function.

Define a named function that can be referenced as a part of the ranking expression, or (if having no arguments) as a feature. Check the Vespa documentation for more detailed information about rank functions.

Parameters:	name – Name of the function. expression – String representing a Vespa expression. args – Optional. List of arguments to be used in the function expression.

>>> Function(
...     name="myfeature",
...     expression="fieldMatch(bar) + freshness(foo)",
...     args=["foo", "bar"]
... )
Function('myfeature', 'fieldMatch(bar) + freshness(foo)', ['foo', 'bar'])

It is possible to define functions with multi-line expressions:

>>> Function(
...     name="token_type_ids",
...     expression="tensor<float>(d0[1],d1[128])(\n"
...                "    if (d1 < question_length,\n"
...                "        0,\n"
...                "    if (d1 < question_length + doc_length,\n"
...                "        1,\n"
...                "        TOKEN_NONE\n"
...                "    )))",
... )
Function('token_type_ids', 'tensor<float>(d0[1],d1[128])(\n    if (d1 < question_length,\n        0,\n    if (d1 < question_length + doc_length,\n        1,\n        TOKEN_NONE\n    )))', None)

HNSW

class vespa.package.HNSW(distance_metric: Literal[euclidean, angular, dotproduct, prenormalized-angular, hamming, geodegrees] = 'euclidean', max_links_per_node=16, neighbors_to_explore_at_insert=200)

__init__(distance_metric: Literal[euclidean, angular, dotproduct, prenormalized-angular, hamming, geodegrees] = 'euclidean', max_links_per_node=16, neighbors_to_explore_at_insert=200)

Configure Vespa HNSW indexes Check the Vespa documentation

Parameters:	distance_metric – Distance metric to use when computing distance between vectors. Default is ‘euclidean’. max_links_per_node – Specifies how many links per HNSW node to select when building the graph. Default is 16. neighbors_to_explore_at_insert – Specifies how many neighbors to explore when inserting a document in the HNSW graph. Default is 200.

ImportedField

class vespa.package.ImportedField(name: str, reference_field: str, field_to_import: str)

__init__(name: str, reference_field: str, field_to_import: str) → None

Imported field from a reference document.

Useful to implement parent/child relationships.

Parameters:	name – Field name. reference_field – field of type reference that points to the document that contains the field to be imported. field_to_import – Field name to be imported, as defined in the reference document.

>>> ImportedField(
...     name="global_category_ctrs",
...     reference_field="category_ctr_ref",
...     field_to_import="ctrs",
... )
ImportedField('global_category_ctrs', 'category_ctr_ref', 'ctrs')

OnnxModel

class vespa.package.OnnxModel(model_name: str, model_file_path: str, inputs: Dict[str, str], outputs: Dict[str, str])

__init__(model_name: str, model_file_path: str, inputs: Dict[str, str], outputs: Dict[str, str]) → None

Create a Vespa ONNX model config.

Vespa has support for advanced ranking models through it’s tensor API. If you have your model in the ONNX format, Vespa can import the models and use them directly. Check the Vespa documentation for more detailed information about field sets.

Parameters:

model_name – Unique model name to use as id when referencing the model.
model_file_path – ONNX model file path.
inputs – Dict mapping the ONNX input names as specified in the ONNX file to valid Vespa inputs, which can be a document field (attribute(field_name)), a query parameter (query(query_param)), a constant (constant(name)) and a user-defined function (function_name).
outputs – Dict mapping the ONNX output names as specified in the ONNX file to the name used in Vespa to specify the output. If this is omitted, the first output in the ONNX file will be used.

>>> OnnxModel(
...     model_name="bert",
...     model_file_path="bert.onnx",
...     inputs={
...         "input_ids": "input_ids",
...         "token_type_ids": "token_type_ids",
...         "attention_mask": "attention_mask",
...     },
...     outputs={"logits": "logits"},
... )
OnnxModel('bert', 'bert.onnx', {'input_ids': 'input_ids', 'token_type_ids': 'token_type_ids', 'attention_mask': 'attention_mask'}, {'logits': 'logits'})

QueryProfile

class vespa.package.QueryProfile(fields: Optional[List[vespa.package.QueryField]] = None)

__init__(fields: Optional[List[vespa.package.QueryField]] = None) → None

Create a Vespa Query Profile.

Check the Vespa documentation for more detailed information about query profiles.

A QueryProfile is a named collection of query request parameters given in the configuration. The query request can specify a query profile whose parameters will be used as parameters of that request. The query profiles may optionally be type checked. Type checking is turned on by referencing a QueryProfileType from the query profile.

Parameters:	fields – A list of `QueryField`.

>>> QueryProfile(fields=[QueryField(name="maxHits", value=1000)])
QueryProfile([QueryField('maxHits', 1000)])

add_fields(*fields) → None

Add QueryField’s to the Query Profile.

Parameters:	fields – fields to be added

>>> query_profile = QueryProfile()
>>> query_profile.add_fields(QueryField(name="maxHits", value=1000))

QueryField

class vespa.package.QueryField(name: str, value: Union[str, int, float])

__init__(name: str, value: Union[str, int, float]) → None

Create a field to be included in a QueryProfile.

Parameters:	name – Field name. value – Field value.

>>> QueryField(name="maxHits", value=1000)
QueryField('maxHits', 1000)

QueryProfileType

class vespa.package.QueryProfileType(fields: Optional[List[vespa.package.QueryTypeField]] = None)

__init__(fields: Optional[List[vespa.package.QueryTypeField]] = None) → None

Create a Vespa Query Profile Type.

Check the Vespa documentation for more detailed information about query profile types.

An ApplicationPackage instance comes with a default QueryProfile named default that is associated with a QueryProfileType named root, meaning that you usually do not need to create those yourself, only add fields to them when required.

Parameters:	fields – A list of `QueryTypeField`.

>>> QueryProfileType(
...     fields = [
...         QueryTypeField(
...             name="ranking.features.query(tensor_bert)",
...             type="tensor<float>(x[768])"
...         )
...     ]
... )
QueryProfileType([QueryTypeField('ranking.features.query(tensor_bert)', 'tensor<float>(x[768])')])

add_fields(*fields) → None

Add QueryTypeField’s to the Query Profile Type.

Parameters:	fields – fields to be added

>>> query_profile_type = QueryProfileType()
>>> query_profile_type.add_fields(
...     QueryTypeField(
...         name="age",
...         type="integer"
...     ),
...     QueryTypeField(
...         name="profession",
...         type="string"
...     )
... )

QueryTypeField

class vespa.package.QueryTypeField(name: str, type: str)

__init__(name: str, type: str) → None

Create a field to be included in a QueryProfileType.

Parameters:	name – Field name. type – Field type.

>>> QueryTypeField(
...     name="ranking.features.query(title_bert)",
...     type="tensor<float>(x[768])"
... )
QueryTypeField('ranking.features.query(title_bert)', 'tensor<float>(x[768])')

RankProfile

class vespa.package.RankProfile(name: str, first_phase: Union[str, vespa.package.FirstPhaseRanking], inherits: Optional[str] = None, constants: Optional[Dict] = None, functions: Optional[List[vespa.package.Function]] = None, summary_features: Optional[List] = None, match_features: Optional[List] = None, second_phase: Optional[vespa.package.SecondPhaseRanking] = None, **kwargs)

__init__(name: str, first_phase: Union[str, vespa.package.FirstPhaseRanking], inherits: Optional[str] = None, constants: Optional[Dict] = None, functions: Optional[List[vespa.package.Function]] = None, summary_features: Optional[List] = None, match_features: Optional[List] = None, second_phase: Optional[vespa.package.SecondPhaseRanking] = None, **kwargs) → None

Create a Vespa rank profile.

Rank profiles are used to specify an alternative ranking of the same data for different purposes, and to experiment with new rank settings. Check the Vespa documentation for more detailed information about rank profiles.

Key rank_properties:
Parameters:	name – Rank profile name. first_phase – The config specifying the first phase of ranking. More info about first phase ranking. inherits – The inherits attribute is optional. If defined, it contains the name of one other rank profile in the same schema. Values not defined in this rank profile will then be inherited. constants – Dict of constants available in ranking expressions, resolved and optimized at configuration time. More info about constants. functions – Optional list of `Function` representing rank functions to be included in the rank profile. summary_features – List of rank features to be included with each hit. More info about summary features. match_features – List of rank features to be included with each hit. More info about match features. second_phase – Optional config specifying the second phase of ranking. See `SecondPhaseRanking`.
Key weight:	A list of tuples containing the field and their weight
Key rank_type:	A list of tuples containing a field and the rank-type-name. More info about rank-type.
	A list of tuples containing a field and its configuration. More info about rank-properties.

>>> RankProfile(name = "default", first_phase = "nativeRank(title, body)")
RankProfile('default', 'nativeRank(title, body)', None, None, None, None, None, None, None, None, None, None)

>>> RankProfile(name = "new", first_phase = "BM25(title)", inherits = "default")
RankProfile('new', 'BM25(title)', 'default', None, None, None, None, None, None, None, None, None)

>>> RankProfile(
...     name = "new",
...     first_phase = "BM25(title)",
...     inherits = "default",
...     constants={"TOKEN_NONE": 0, "TOKEN_CLS": 101, "TOKEN_SEP": 102},
...     summary_features=["BM25(title)"]
... )
RankProfile('new', 'BM25(title)', 'default', {'TOKEN_NONE': 0, 'TOKEN_CLS': 101, 'TOKEN_SEP': 102}, None, ['BM25(title)'], None, None, None, None, None, None)

>>> RankProfile(
...     name="bert",
...     first_phase="bm25(title) + bm25(body)",
...     second_phase=SecondPhaseRanking(expression="1.25 * bm25(title) + 3.75 * bm25(body)", rerank_count=10),
...     inherits="default",
...     constants={"TOKEN_NONE": 0, "TOKEN_CLS": 101, "TOKEN_SEP": 102},
...     functions=[
...         Function(
...             name="question_length",
...             expression="sum(map(query(query_token_ids), f(a)(a > 0)))"
...         ),
...         Function(
...             name="doc_length",
...             expression="sum(map(attribute(doc_token_ids), f(a)(a > 0)))"
...         )
...     ],
...     summary_features=["question_length", "doc_length"]
... )
RankProfile('bert', 'bm25(title) + bm25(body)', 'default', {'TOKEN_NONE': 0, 'TOKEN_CLS': 101, 'TOKEN_SEP': 102}, [Function('question_length', 'sum(map(query(query_token_ids), f(a)(a > 0)))', None), Function('doc_length', 'sum(map(attribute(doc_token_ids), f(a)(a > 0)))', None)], ['question_length', 'doc_length'], None, SecondPhaseRanking('1.25 * bm25(title) + 3.75 * bm25(body)', 10), None, None, None, None)

>>> RankProfile(
...     name = "default",
...     first_phase = "nativeRank(title, body)",
...     weight = [("title", 200), ("body", 100)]
... )
RankProfile('default', 'nativeRank(title, body)', None, None, None, None, None, None, [('title', 200), ('body', 100)], None, None, None)

>>> RankProfile(
...     name = "default",
...     first_phase = "nativeRank(title, body)",
...     rank_type = [("body", "about")]
... )
RankProfile('default', 'nativeRank(title, body)', None, None, None, None, None, None, None, [('body', 'about')], None, None)

>>> RankProfile(
...     name = "default",
...     first_phase = "nativeRank(title, body)",
...     rank_properties = [("fieldMatch(title).maxAlternativeSegmentations", "10")]
... )
RankProfile('default', 'nativeRank(title, body)', None, None, None, None, None, None, None, None, [('fieldMatch(title).maxAlternativeSegmentations', '10')], None)

>>> RankProfile(
...    name = "default",
...    first_phase = FirstPhaseRanking(expression="nativeRank(title, body)", keep_rank_count=50)
... )
RankProfile('default', FirstPhaseRanking('nativeRank(title, body)', 50, None), None, None, None, None, None, None, None, None, None, None)

Schema

class vespa.package.Schema(name: str, document: vespa.package.Document, fieldsets: Optional[List[vespa.package.FieldSet]] = None, rank_profiles: Optional[List[vespa.package.RankProfile]] = None, models: Optional[List[vespa.package.OnnxModel]] = None, global_document: bool = False, imported_fields: Optional[List[vespa.package.ImportedField]] = None, document_summaries: Optional[List[vespa.package.DocumentSummary]] = None, **kwargs)

__init__(name: str, document: vespa.package.Document, fieldsets: Optional[List[vespa.package.FieldSet]] = None, rank_profiles: Optional[List[vespa.package.RankProfile]] = None, models: Optional[List[vespa.package.OnnxModel]] = None, global_document: bool = False, imported_fields: Optional[List[vespa.package.ImportedField]] = None, document_summaries: Optional[List[vespa.package.DocumentSummary]] = None, **kwargs) → None

Create a Vespa Schema.

Check the Vespa documentation for more detailed information about schemas.

Parameters:

name – Schema name.
document – Vespa Document associated with the Schema.
fieldsets – A list of FieldSet associated with the Schema.
rank_profiles – A list of RankProfile associated with the Schema.
models – A list of OnnxModel associated with the Schema.
global_document – Set to True to copy the documents to all content nodes. Default to False.
imported_fields – A list of ImportedField defining fields from global documents to be imported.
document_summaries – A list of DocumentSummary associated with the schema.

Key stemming:

The default stemming setting. Defaults to ‘best’.

To create a Schema:

>>> Schema(name="schema_name", document=Document())
Schema('schema_name', Document(None, None, None), None, None, [], False, None, [], None)

add_document_summary(document_summary: vespa.package.DocumentSummary) → None

Add a DocumentSummary to the Schema.

Parameters:	document_summary – document summary to be added.

add_field_set(field_set: vespa.package.FieldSet) → None

Add a FieldSet to the Schema.

Parameters:	field_set – field sets to be added.

add_fields(*fields) → None

Add Field to the Schema’s Document.

Parameters:	fields – fields to be added.

add_imported_field(imported_field: vespa.package.ImportedField) → None

Add a ImportedField to the Schema.

Parameters:	imported_field – imported field to be added.

add_model(model: vespa.package.OnnxModel) → None

Add a OnnxModel to the Schema.

Parameters:	model – model to be added.
Returns:	None.

add_rank_profile(rank_profile: vespa.package.RankProfile) → None

Add a RankProfile to the Schema.

Parameters:	rank_profile – rank profile to be added.
Returns:	None.

SecondPhaseRanking

class vespa.package.SecondPhaseRanking(expression: str, rerank_count: int = 100)

__init__(expression: str, rerank_count: int = 100) → None

Create a Vespa second phase ranking configuration.

This is the optional reranking performed on the best hits from the first phase. Check the Vespa documentation for more detailed information about second phase ranking configuration.

Parameters:	expression – Specify the ranking expression to be used for second phase of ranking. Check also the Vespa documentation for ranking expression. rerank_count – Specifies the number of hits to be reranked in the second phase. Default value is 100.

>>> SecondPhaseRanking(expression="1.25 * bm25(title) + 3.75 * bm25(body)", rerank_count=10)
SecondPhaseRanking('1.25 * bm25(title) + 3.75 * bm25(body)', 10)

Struct

class vespa.package.Struct(name: str, fields: Optional[List[vespa.package.Field]] = None)

__init__(name: str, fields: Optional[List[vespa.package.Field]] = None)

Create a Vespa struct. A struct defines a composite type. Check the Vespa documentation for more detailed information about structs.

Parameters:	name – Name of the struct fields – Field names to be included in the fieldset

>>> Struct("person")
Struct('person', None)

>>> Struct(
...     "person",
...     [
...         Field("first_name", "string"),
...         Field("last_name", "string"),
...     ],
... )
Struct('person', [Field('first_name', 'string', None, None, None, None, None, None, None, None, True, None, None, None, []), Field('last_name', 'string', None, None, None, None, None, None, None, None, True, None, None, None, [])])

StructField

class vespa.package.StructField(name: str, **kwargs)

__init__(name: str, **kwargs) → None

Create a Vespa struct-field. Check the Vespa documentation for more detailed information about struct-fields.

Key query_command:
Parameters:	name – Struct-field name.
Key indexing:	Configures how to process data of a struct-field during indexing.
Key attribute:	Specifies a property of an index structure attribute.
Key match:	Set properties that decide how the matching method for this field operate.
	Add configuration for query-command of the field.
Key summary:	Add configuration for summary of the field.

>>> StructField(
...     name = "first_name",
... )
StructField('first_name', None, None, None, None, None)

>>> StructField(
...     name = "first_name",
...     indexing = ["attribute"],
...     attribute = ["fast-search"],
... )
StructField('first_name', ['attribute'], ['fast-search'], None, None, None)

>>> StructField(
...     name = "last_name",
...     match = ["exact", ("exact-terminator", '"@%"')],
...     query_command = ['"exact %%"'],
...     summary = Summary(None, None, fields=["dynamic", ("bolding", "on")])
... )
StructField('last_name', None, None, ['exact', ('exact-terminator', '"@%"')], ['"exact %%"'], Summary(None, None, ['dynamic', ('bolding', 'on')]))

Summary

class vespa.package.Summary(name: Optional[str] = None, type: Optional[str] = None, fields: Optional[List[Union[str, Tuple[str, Union[List[str], str]]]]] = None)

__init__(name: Optional[str] = None, type: Optional[str] = None, fields: Optional[List[Union[str, Tuple[str, Union[List[str], str]]]]] = None) → None

Configures a summary Field.

Parameters:

name – Name of the summary field, can be None if used inside a Field, which then uses the name of the Field.
type – Type of the summary field, can be None if used inside a Field, which then uses the type of the Field.
fields – List of properties used to configure the summary, can be single properties (like “summary: dynamic”, common in Field), or composite values (like “source: another_field”)

>>> Summary(None, None, ["dynamic"])
Summary(None, None, ['dynamic'])

>>> Summary(
...     "title",
...     "string",
...     [("source", "title")]
... )
Summary('title', 'string', [('source', 'title')])

>>> Summary(
...     "title",
...     "string",
...     [("source", ["title", "abstract"])]
... )
Summary('title', 'string', [('source', ['title', 'abstract'])])

>>> Summary(
...     name = "artist",
...     type = "string",
... )
Summary('artist', 'string', None)

as_lines

Returns the object as a List of str, each str representing a line of configuration that can be used during schema generation as such:

` {% for line in field.summary.as_lines %} {{ line }} {% endfor %} `

>>> Summary(None, None, ["dynamic"]).as_lines
['summary: dynamic']

>>> Summary(
...     "artist",
...     "string",
... ).as_lines
['summary artist type string {}']

>>> Summary(
...     "artist",
...     "string",
...     [("bolding", "on"), ("sources", "artist")],
... ).as_lines
['summary artist type string {', '    bolding: on', '    sources: artist', '}']

Validation

class vespa.package.Validation(validation_id: Union[vespa.package.ValidationID, str], until: str, comment: Optional[str] = None)

__init__(validation_id: Union[vespa.package.ValidationID, str], until: str, comment: Optional[str] = None)

Represents a validation to be overridden on application.

Check the Vespa documentation for more detailed information about validations.

Parameters:

validation_id – ID of the validation.
until – The last day this change is allowed, as a ISO-8601-format date in UTC, e.g. 2016-01-30. Dates may at most be 30 days in the future, but should be as close to now as possible for safety, while allowing time for review and propagation to all deployed zones. allow-tags with dates in the past are ignored.
comment – Optional text explaining the reason for the change to humans.