Reference API

Create an Application Package

The first step to create a Vespa application is to create an instance of ApplicationPackage.

class vespa.package.ApplicationPackage(name: str, schema: Optional[vespa.package.Schema] = None, query_profile: Optional[vespa.package.QueryProfile] = None, query_profile_type: Optional[vespa.package.QueryProfileType] = None)
__init__(name: str, schema: Optional[vespa.package.Schema] = None, query_profile: Optional[vespa.package.QueryProfile] = None, query_profile_type: Optional[vespa.package.QueryProfileType] = None) → None

Create a Vespa Application Package.

Check the Vespa documentation for more detailed information about application packages.

Parameters:
  • name – Application name.
  • schema – Schema 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.

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

>>> ApplicationPackage(name="test_app")
ApplicationPackage('test_app', Schema('test_app', Document(None), 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_model_ranking(model_config: vespa.ml.ModelConfig, include_model_summary_features=False, **kwargs) → None

Add ranking profile based on a specific model config.

Parameters:
  • model_config – Model config instance specifying the model to be used on the RankProfile.
  • include_model_summary_features – True to include model specific summary features, such as inputs and outputs that are useful for debugging. Default to False as this requires an extra model evaluation when fetching summary features.
  • kwargs – Further arguments to be passed to RankProfile.
Returns:

None

Schema and Document

An ApplicationPackage instance comes with a default Schema that contains a default Document, meaning that you usually do not need to create those yourself.

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)
__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) → 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.

To create a Schema:

>>> Schema(name="schema_name", document=Document())
Schema('schema_name', Document(None), None, None, [])
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_model(model: vespa.package.OnnxModel) → None

Add a OnnxModel to the Schema. :param model: model to be added. :return: 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.
class vespa.package.Document(fields: Optional[List[vespa.package.Field]] = None)
__init__(fields: Optional[List[vespa.package.Field]] = 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)

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

Add Field’s to the document.

Parameters:fields – fields to be added
Returns:

Create a Field

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.

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)
__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) → None

Create a Vespa field.

Check the Vespa documentation for more detailed information about fields.

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

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

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

Create a 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'])

Create a RankProfile

class vespa.package.RankProfile(name: str, first_phase: str, inherits: Optional[str] = None, constants: Optional[Dict[KT, VT]] = None, functions: Optional[List[vespa.package.Function]] = None, summary_features: Optional[List[T]] = None, second_phase: Optional[vespa.package.SecondPhaseRanking] = None)
__init__(name: str, first_phase: str, inherits: Optional[str] = None, constants: Optional[Dict[KT, VT]] = None, functions: Optional[List[vespa.package.Function]] = None, summary_features: Optional[List[T]] = None, second_phase: Optional[vespa.package.SecondPhaseRanking] = None) → 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.

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.

  • second_phase – Optional config specifying the second phase of ranking. See SecondPhaseRanking.
 
>>> RankProfile(name = "default", first_phase = "nativeRank(title, body)")
RankProfile('default', 'nativeRank(title, body)', None, None, None, None, None)

>>> RankProfile(name = "new", first_phase = "BM25(title)", inherits = "default")
RankProfile('new', 'BM25(title)', 'default', 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)

>>> 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'], SecondPhaseRanking('1.25 * bm25(title) + 3.75 * bm25(body)', 10))

Query Profile

A QueryProfile is a named collection of search request parameters given in the configuration. The search 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.

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.

Create a QueryProfileType

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])')
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.

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"
...     )
... )

Create a QueryProfile

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)
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.

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))

Deploying your application

class vespa.package.VespaDocker(port: int = 8080, output_file: IO = <_io.TextIOWrapper name='<stdout>' mode='w' encoding='UTF-8'>)
__init__(port: int = 8080, output_file: IO = <_io.TextIOWrapper name='<stdout>' mode='w' encoding='UTF-8'>) → None

Manage Docker deployments. :param output_file: Output file to write output messages.

deploy(application_package: vespa.package.ApplicationPackage, disk_folder: str, container_memory: str = '4G') → vespa.application.Vespa

Deploy the application package into a Vespa container. :param application_package: ApplicationPackage to be deployed. :param disk_folder: Disk folder to save the required Vespa config files. :param container_memory: Docker container memory available to the application. :return: a Vespa connection instance.

deploy_from_disk(application_name: str, disk_folder: str, container_memory: str = '4G', application_folder: Optional[str] = None) → vespa.application.Vespa

Deploy disk-based application package into a Vespa container. :param application_name: Name of the application. :param disk_folder: The disk_folder will be mapped to the /app directory inside the Docker container. :param container_memory: Docker container memory available to the application. :param application_folder: The folder inside disk_folder containing the application files. If None,

we assume disk_folder to be the application folder.
Returns:a Vespa connection instance.
static export_application_package(disk_folder: str, application_package: vespa.package.ApplicationPackage) → None

Export application package to disk. :param disk_folder: Desired application path. Directory will be created if not already exist. :param application_package: Application package to export. :return: None. Application package file will be stored on disk_folder.

restart_services()

Restart Vespa services.

Returns:None
start_services()

Start Vespa services.

Returns:None
stop_services()

Stop Vespa services.

Returns:None
class vespa.package.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)

Delete the specified instance from the dev environment in the Vespa Cloud. :param instance: Name of the instance to delete. :return:

deploy(instance: str, disk_folder: str) → 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.
Returns:

a Vespa connection instance.

vespa.application module

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

Bases: object

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

Establish a connection with a 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.
  • output_file – Output file to write output messages.
 
>>> 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
static annotate_data(hits, query_id, id_field, relevant_id, fields, relevant_score, default_score)
collect_training_data(labeled_data: List[Dict[KT, VT]], id_field: str, query_model: vespa.query.QueryModel, number_additional_docs: int, relevant_score: int = 1, default_score: int = 0, show_progress: Optional[int] = None, **kwargs) → pandas.core.frame.DataFrame

Collect training data based on a set of labelled data.

Parameters:
  • labeled_data – Labelled data containing query, query_id and relevant ids.
  • id_field – The Vespa field representing the document id.
  • query_model – Query model.
  • number_additional_docs – Number of additional documents to retrieve for each relevant document.
  • relevant_score – Score to assign to relevant documents. Default to 1.
  • default_score – Score to assign to the additional documents that are not relevant. Default to 0.
  • show_progress – Prints the the current point being collected every show_progress step. Default to None, in which case progress is not printed.
  • kwargs – Extra keyword arguments to be included in the Vespa Query.
Returns:

DataFrame containing document id (document_id), query id (query_id), scores (relevant) and vespa rank features returned by the Query model RankProfile used.
collect_training_data_point(query: str, query_id: str, relevant_id: str, id_field: str, query_model: vespa.query.QueryModel, number_additional_docs: int, fields: List[str], relevant_score: int = 1, default_score: int = 0, **kwargs) → List[Dict[KT, VT]]

Collect training data based on a single query

Parameters:
  • query – Query string.
  • query_id – Query id represented as str.
  • relevant_id – Relevant id represented as a str.
  • id_field – The Vespa field representing the document id.
  • query_model – Query model.
  • number_additional_docs – Number of additional documents to retrieve for each relevant document.
  • fields – Which fields should be retrieved.
  • relevant_score – Score to assign to relevant documents. Default to 1.
  • default_score – Score to assign to the additional documents that are not relevant. Default to 0.
  • kwargs – Extra keyword arguments to be included in the Vespa Query.
Returns:

List of dicts containing the document id (document_id), query id (query_id), scores (relevant) and vespa rank features returned by the Query model RankProfile used.
delete_data(schema: str, data_id: str) → requests.models.Response

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.
Returns:

Response of the HTTP DELETE request.
evaluate(labeled_data: List[Dict[KT, VT]], eval_metrics: List[vespa.evaluation.EvalMetric], query_model: vespa.query.QueryModel, id_field: str, default_score: int = 0, **kwargs) → pandas.core.frame.DataFrame
Parameters:
  • labeled_data – Labelled data containing query, query_id and relevant ids.
  • eval_metrics – A list of evaluation metrics.
  • query_model – Query model.
  • id_field – The Vespa field representing the document id.
  • default_score – Score to assign to the additional documents that are not relevant. Default to 0.
  • kwargs – Extra keyword arguments to be included in the Vespa Query.
Returns:

DataFrame containing query_id and metrics according to the selected evaluation metrics.
evaluate_query(eval_metrics: List[vespa.evaluation.EvalMetric], query_model: vespa.query.QueryModel, query_id: str, query: str, id_field: str, relevant_docs: List[Dict[KT, VT]], default_score: int = 0, **kwargs) → Dict[KT, VT]

Evaluate a query according to evaluation metrics

Parameters:
  • eval_metrics – A list of evaluation metrics.
  • query_model – Query model.
  • query_id – Query id represented as str.
  • query – Query string.
  • id_field – The Vespa field representing the document id.
  • relevant_docs – A list with dicts where each dict contains a doc id a optionally a doc score.
  • default_score – Score to assign to the additional documents that are not relevant. Default to 0.
  • kwargs – Extra keyword arguments to be included in the Vespa Query.
Returns:

Dict containing query_id and metrics according to the selected evaluation metrics.
feed_data_point(schema: str, data_id: str, fields: Dict[KT, VT]) → requests.models.Response

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.
Returns:

Response of the HTTP POST request.
get_application_status() → Optional[requests.models.Response]

Get application status.

Returns:
get_data(schema: str, data_id: str) → requests.models.Response

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.
Returns:

Response of the HTTP GET request.
query(body: Optional[Dict[KT, VT]] = None, query: Optional[str] = None, query_model: Optional[vespa.query.QueryModel] = None, debug_request: bool = False, recall: Optional[Tuple] = None, **kwargs) → vespa.query.VespaResult

Send a query request to the Vespa application.

Either send ‘body’ containing all the request parameters or specify ‘query’ and ‘query_model’.

Parameters:
  • body – Dict containing all the request parameters.
  • query – Query string
  • query_model – Query model
  • debug_request – return request body for debugging instead of sending the request.
  • recall – Tuple of size 2 where the first element is the name of the field to use to recall and the second element is a list of the values to be recalled.
  • kwargs – Additional parameters to be sent along the request.
Returns:

Either the request body if debug_request is True or the result from the Vespa application
update_data(schema: str, data_id: str, fields: Dict[KT, VT], create: bool = False) → requests.models.Response

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
Returns:

Response of the HTTP PUT request.

vespa.evaluation module

class vespa.evaluation.EvalMetric

Bases: object

__init__() → None

Initialize self. See help(type(self)) for accurate signature.

evaluate_query(query_results, relevant_docs, id_field, default_score) → Dict[KT, VT]
class vespa.evaluation.MatchRatio

Bases: vespa.evaluation.EvalMetric

__init__() → None

Computes the ratio of documents retrieved by the match phase.

evaluate_query(query_results: vespa.query.VespaResult, relevant_docs: List[Dict[KT, VT]], id_field: str, default_score: int) → Dict[KT, VT]

Evaluate query results.

Parameters:
  • query_results – Raw query results returned by Vespa.
  • relevant_docs – A list with dicts where each dict contains a doc id a optionally a doc score.
  • id_field – The Vespa field representing the document id.
  • default_score – Score to assign to the additional documents that are not relevant. Default to 0.
Returns:

Dict containing the number of retrieved docs (_retrieved_docs), the number of docs available in the corpus (_docs_available) and the match ratio (_value).
class vespa.evaluation.NormalizedDiscountedCumulativeGain(at: int)

Bases: vespa.evaluation.EvalMetric

__init__(at: int)

Compute the normalized discounted cumulative gain at position at

Parameters:at – Maximum position on the resulting list to look for relevant docs.
evaluate_query(query_results: vespa.query.VespaResult, relevant_docs: List[Dict[KT, VT]], id_field: str, default_score: int) → Dict[KT, VT]

Evaluate query results.

Parameters:
  • query_results – Raw query results returned by Vespa.
  • relevant_docs – A list with dicts where each dict contains a doc id a optionally a doc score.
  • id_field – The Vespa field representing the document id.
  • default_score – Score to assign to the additional documents that are not relevant. Default to 0.
Returns:

Dict containing the ideal discounted cumulative gain (_ideal_dcg), the discounted cumulative gain (_dcg) and the normalized discounted cumulative gain (_value).
class vespa.evaluation.Recall(at: int)

Bases: vespa.evaluation.EvalMetric

__init__(at: int) → None

Compute the recall at position at

Parameters:at – Maximum position on the resulting list to look for relevant docs.
evaluate_query(query_results: vespa.query.VespaResult, relevant_docs: List[Dict[KT, VT]], id_field: str, default_score: int) → Dict[KT, VT]

Evaluate query results.

Parameters:
  • query_results – Raw query results returned by Vespa.
  • relevant_docs – A list with dicts where each dict contains a doc id a optionally a doc score.
  • id_field – The Vespa field representing the document id.
  • default_score – Score to assign to the additional documents that are not relevant. Default to 0.
Returns:

Dict containing the recall value (_value).
class vespa.evaluation.ReciprocalRank(at: int)

Bases: vespa.evaluation.EvalMetric

__init__(at: int)

Compute the reciprocal rank at position at

Parameters:at – Maximum position on the resulting list to look for relevant docs.
evaluate_query(query_results: vespa.query.VespaResult, relevant_docs: List[Dict[KT, VT]], id_field: str, default_score: int) → Dict[KT, VT]

Evaluate query results.

Parameters:
  • query_results – Raw query results returned by Vespa.
  • relevant_docs – A list with dicts where each dict contains a doc id a optionally a doc score.
  • id_field – The Vespa field representing the document id.
  • default_score – Score to assign to the additional documents that are not relevant. Default to 0.
Returns:

Dict containing the reciprocal rank value (_value).

vespa.query module

class vespa.query.AND

Bases: vespa.query.MatchFilter

__init__() → None

Filter that match document containing all the query terms.

create_match_filter(query: str) → str

Create part of the YQL expression related to the filter.

Parameters:query – Query input.
Returns:Part of the YQL expression related to the filter.
get_query_properties(query: Optional[str] = None) → Dict[KT, VT]

Relevant request properties associated with the filter.

Parameters:query – Query input.
Returns:dict containing the relevant request properties associated with the filter.
class vespa.query.ANN(doc_vector: str, query_vector: str, hits: int, label: str, approximate: bool = True)

Bases: vespa.query.MatchFilter

__init__(doc_vector: str, query_vector: str, hits: int, label: str, approximate: bool = True) → None

Match documents according to the nearest neighbor operator.

Reference: https://docs.vespa.ai/documentation/reference/query-language-reference.html#nearestneighbor

Parameters:
  • doc_vector – Name of the document field to be used in the distance calculation.
  • query_vector – Name of the query field to be used in the distance calculation.
  • hits – Lower bound on the number of hits to return.
  • label – A label to identify this specific operator instance.
  • approximate – True to use approximate nearest neighbor and False to use brute force. Default to True.
create_match_filter(query: str) → str

Create part of the YQL expression related to the filter.

Parameters:query – Query input.
Returns:Part of the YQL expression related to the filter.
get_query_properties(query: Optional[str] = None) → Dict[str, str]

Relevant request properties associated with the filter.

Parameters:query – Query input.
Returns:dict containing the relevant request properties associated with the filter.
class vespa.query.MatchFilter

Bases: object

Abstract class for match filters.

create_match_filter(query: str) → str

Create part of the YQL expression related to the filter.

Parameters:query – Query input.
Returns:Part of the YQL expression related to the filter.
get_query_properties(query: Optional[str] = None) → Dict[KT, VT]

Relevant request properties associated with the filter.

Parameters:query – Query input.
Returns:dict containing the relevant request properties associated with the filter.
class vespa.query.OR

Bases: vespa.query.MatchFilter

__init__() → None

Filter that match any document containing at least one query term.

create_match_filter(query: str) → str

Create part of the YQL expression related to the filter.

Parameters:query – Query input.
Returns:Part of the YQL expression related to the filter.
get_query_properties(query: Optional[str] = None) → Dict[KT, VT]

Relevant request properties associated with the filter.

Parameters:query – Query input.
Returns:dict containing the relevant request properties associated with the filter.
class vespa.query.QueryModel(query_properties: Optional[List[vespa.query.QueryProperty]] = None, match_phase: vespa.query.MatchFilter = <vespa.query.AND object>, rank_profile: vespa.query.RankProfile = <vespa.query.RankProfile object>)

Bases: object

__init__(query_properties: Optional[List[vespa.query.QueryProperty]] = None, match_phase: vespa.query.MatchFilter = <vespa.query.AND object>, rank_profile: vespa.query.RankProfile = <vespa.query.RankProfile object>) → None

Define a query model.

Parameters:
  • query_properties – Optional list of QueryProperty.
  • match_phase – Define the match criteria. One of the MatchFilter options available.
  • rank_profile – Define the rank criteria.
create_body(query: str) → Dict[str, str]

Create the appropriate request body to be sent to Vespa.

Parameters:query – Query input.
Returns:dict representing the request body.
class vespa.query.QueryProperty

Bases: object

Abstract class for query property.

get_query_properties(query: Optional[str] = None) → Dict[KT, VT]

Extract query property syntax.

Parameters:query – Query input.
Returns:dict containing the relevant request properties to be included in the query.
class vespa.query.QueryRankingFeature(name: str, mapping: Callable[[str], List[float]])

Bases: vespa.query.QueryProperty

__init__(name: str, mapping: Callable[[str], List[float]]) → None

Include ranking.feature.query into a Vespa query.

Parameters:
  • name – Name of the feature.
  • mapping – Function mapping a string to a list of floats.
get_query_properties(query: Optional[str] = None) → Dict[str, str]

Extract query property syntax.

Parameters:query – Query input.
Returns:dict containing the relevant request properties to be included in the query.
class vespa.query.RankProfile(name: str = 'default', list_features: bool = False)

Bases: object

__init__(name: str = 'default', list_features: bool = False) → None

Define a rank profile.

Parameters:
  • name – Name of the rank profile as defined in a Vespa search definition.
  • list_features – Should the ranking features be returned. Either ‘true’ or ‘false’.
class vespa.query.Union(*args)

Bases: vespa.query.MatchFilter

__init__(*args) → None

Match documents that belongs to the union of many match filters.

Parameters:args – Match filters to be taken the union of.
create_match_filter(query: str) → str

Create part of the YQL expression related to the filter.

Parameters:query – Query input.
Returns:Part of the YQL expression related to the filter.
get_query_properties(query: Optional[str] = None) → Dict[str, str]

Relevant request properties associated with the filter.

Parameters:query – Query input.
Returns:dict containing the relevant request properties associated with the filter.
class vespa.query.VespaResult(vespa_result, request_body=None)

Bases: object

__init__(vespa_result, request_body=None)

Initialize self. See help(type(self)) for accurate signature.

hits
json
number_documents_indexed
number_documents_retrieved
request_body
class vespa.query.WeakAnd(hits: int, field: str = 'default')

Bases: vespa.query.MatchFilter

__init__(hits: int, field: str = 'default') → None

Match documents according to the weakAND algorithm.

Reference: https://docs.vespa.ai/documentation/using-wand-with-vespa.html

Parameters:
  • hits – Lower bound on the number of hits to be retrieved.
  • field – Which Vespa field to search.
create_match_filter(query: str) → str

Create part of the YQL expression related to the filter.

Parameters:query – Query input.
Returns:Part of the YQL expression related to the filter.
get_query_properties(query: Optional[str] = None) → Dict[KT, VT]

Relevant request properties associated with the filter.

Parameters:query – Query input.
Returns:dict containing the relevant request properties associated with the filter.