API¶

Projects¶

class agml_api.projects.v1.api.projects_api.ProjectsApi(api_client=None)¶

NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech

Do not edit the class manually.

create_project(new_project_request: NewProjectRequest, _request_timeout: None | Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])] | Tuple[Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])], Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])]] = None, _request_auth: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _content_type: Annotated[str, Strict(strict=True)] | None = None, _headers: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _host_index: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=0)])] = 0) → ProjectInformation¶

Create Project

Create a new ML project.

Parameters:: new_project_request (NewProjectRequest) – (required)
Returns:: Returns the result object.

delete_project(project_id: Annotated[str, Strict(strict=True)], _request_timeout: None | Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])] | Tuple[Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])], Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])]] = None, _request_auth: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _content_type: Annotated[str, Strict(strict=True)] | None = None, _headers: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _host_index: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=0)])] = 0) → None¶

Delete Project

Delete an existing ML project.

Parameters:: project_id (str) – (required)
Returns:: Returns the result object.

get_project(project_id: Annotated[str, FieldInfo(annotation=NoneType, required=True, metadata=[Strict(strict=True), MinLen(min_length=1)])], _request_timeout: None | Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])] | Tuple[Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])], Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])]] = None, _request_auth: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _content_type: Annotated[str, Strict(strict=True)] | None = None, _headers: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _host_index: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=0)])] = 0) → ProjectInformation¶

Get Project

Retrieve the information of an ML project by id.

Parameters:: project_id (str) – (required)
Returns:: Returns the result object.

list_agml_models(project_name: Annotated[str, Strict(strict=True)] | None = None, project_id: Annotated[str, Strict(strict=True)] | None = None, job_id: Annotated[str, Strict(strict=True)] | None = None, model_tasks: List[MLModelTask] | None = None, limit: Annotated[int, Strict(strict=True)] | None = None, offset: Annotated[int, Strict(strict=True)] | None = None, _request_timeout: None | Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])] | Tuple[Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])], Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])]] = None, _request_auth: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _content_type: Annotated[str, Strict(strict=True)] | None = None, _headers: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _host_index: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=0)])] = 0) → MLModelListResponse¶

List Agml Models

Retrieve a list of trained ML Models by project (based on either the project name or the project id). This can be narrowed by an associated training job, as well as the task types the models were trained for. The results are paginated using a limit and offset.

Parameters:

project_name (str)
project_id (str)
job_id (str)
model_tasks (List[MLModelTask])
limit (int)
offset (int)

Returns:

Returns the result object.

list_projects(limit: Annotated[int, Strict(strict=True)] | None = None, offset: Annotated[int, Strict(strict=True)] | None = None, _request_timeout: None | Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])] | Tuple[Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])], Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])]] = None, _request_auth: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _content_type: Annotated[str, Strict(strict=True)] | None = None, _headers: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _host_index: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=0)])] = 0) → ListProjectsResponse¶

List Projects

Get a list of ML projects. The results are paginated and can be controlled using the limit and offset parameters.

Parameters:

limit (int)
offset (int)

Returns:

Returns the result object.

update_project(project_id: Annotated[str, Strict(strict=True)], update_project_request: UpdateProjectRequest, _request_timeout: None | Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])] | Tuple[Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])], Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])]] = None, _request_auth: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _content_type: Annotated[str, Strict(strict=True)] | None = None, _headers: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _host_index: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=0)])] = 0) → ProjectInformation¶

Update Project

Update an existing ML project.

Parameters:

project_id (str) – (required)
update_project_request (UpdateProjectRequest) – (required)

Returns:

Returns the result object.

ArangoGraphML Projects API

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The version of the OpenAPI document: v1 Generated by OpenAPI Generator (https://openapi-generator.tech)

Do not edit the class manually.

class agml_api.projects.v1.models.ClassificationModelInfo(*, jobID: Annotated[str, Strict(strict=True)], modelID: Annotated[str, Strict(strict=True)], modelDisplayName: Annotated[str, Strict(strict=True)] | None = None, modelName: Annotated[str, Strict(strict=True)], modelStatistics: Dict[str, Any], targetCollection: Annotated[str, Strict(strict=True)], targetField: Annotated[str, Strict(strict=True)])¶: Information about a Node Classification model.

class agml_api.projects.v1.models.GraphEmbeddingModelInfo(*, jobID: Annotated[str, Strict(strict=True)], modelID: Annotated[str, Strict(strict=True)], modelDisplayName: Annotated[str, Strict(strict=True)] | None = None, modelName: Annotated[str, Strict(strict=True)], modelStatistics: Dict[str, Any], modelTasks: List[Annotated[str, Strict(strict=True)]])¶: Information about a Graph Embedding model.

class agml_api.projects.v1.models.HTTPValidationError(*, detail: List[ValidationError] | None = None)¶

class agml_api.projects.v1.models.ListProjectsResponse(*, projects: List[ProjectInformation], offset: Annotated[int, Strict(strict=True)], limit: Annotated[int, Strict(strict=True)])¶: A paginated list of ML Projects.

class agml_api.projects.v1.models.LocationInner(*args, anyof_schema_1_validator: Annotated[str, Strict(strict=True)] | None = None, anyof_schema_2_validator: Annotated[int, Strict(strict=True)] | None = None, actual_instance: Any = None, any_of_schemas: List[str] = typing.Literal['int', 'str'])¶

class agml_api.projects.v1.models.MLModelListResponse(*, models: List[ModelsInner])¶: A list of existing ML Models.

class agml_api.projects.v1.models.MLModelTask(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)¶: Enum for supported ML tasks: CLASSIFICATION, GRAPH_EMBEDDINGS

class agml_api.projects.v1.models.ModelListResponse(*, models: List[ClassificationModelInfo])¶

class agml_api.projects.v1.models.ModelsInner(*args, anyof_schema_1_validator: ClassificationModelInfo | None = None, anyof_schema_2_validator: GraphEmbeddingModelInfo | None = None, actual_instance: Any = None, any_of_schemas: List[str] = typing.Literal['ClassificationModelInfo', 'GraphEmbeddingModelInfo'])¶

class agml_api.projects.v1.models.NewProjectRequest(*, name: Annotated[str, Strict(strict=True), MinLen(min_length=1)])¶: Request to create a new ML Project.

class agml_api.projects.v1.models.ProjectInformation(*, id: Annotated[str, Strict(strict=True)], name: Annotated[str, Strict(strict=True)])¶: Information about an ML Project.

class agml_api.projects.v1.models.UpdateProjectRequest(*, id: Annotated[str, Strict(strict=True), MinLen(min_length=1)], name: Annotated[str, Strict(strict=True), MinLen(min_length=1)])¶: Request to update an ML Project.

class agml_api.projects.v1.models.ValidationError(*, loc: List[LocationInner], msg: Annotated[str, Strict(strict=True)], type: Annotated[str, Strict(strict=True)])¶

Jobs¶

class agml_api.jobs.v1.api.jobs_api.JobsApi(api_client=None)¶

NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech

Do not edit the class manually.

cancel_job(job_id: Annotated[str, Strict(strict=True)], _request_timeout: None | Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])] | Tuple[Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])], Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])]] = None, _request_auth: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _content_type: Annotated[str, Strict(strict=True)] | None = None, _headers: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _host_index: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=0)])] = 0) → str¶

Cancel Job

Cancel a running job

Parameters:: job_id (str) – (required)
Returns:: Returns the result object.

featurize(featurization_job_request: FeaturizationJobRequest, _request_timeout: None | Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])] | Tuple[Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])], Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])]] = None, _request_auth: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _content_type: Annotated[str, Strict(strict=True)] | None = None, _headers: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _host_index: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=0)])] = 0) → FeaturizationJobResponse¶

Featurize

Submit a featurization job based on the provided featurization specification

Parameters:: featurization_job_request (FeaturizationJobRequest) – (required)
Returns:: Returns the result object.

generate(batch_embeddings_job_request: BatchEmbeddingsJobRequest, _request_timeout: None | Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])] | Tuple[Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])], Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])]] = None, _request_auth: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _content_type: Annotated[str, Strict(strict=True)] | None = None, _headers: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _host_index: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=0)])] = 0) → BatchEmbeddingsJobResponse¶

Generate

Submit an embeddings generation job based on the provided specification.

Parameters:: batch_embeddings_job_request (BatchEmbeddingsJobRequest) – (required)
Returns:: Returns the result object.

get_job(job_id: Annotated[str, Strict(strict=True)], _request_timeout: None | Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])] | Tuple[Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])], Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])]] = None, _request_auth: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _content_type: Annotated[str, Strict(strict=True)] | None = None, _headers: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _host_index: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=0)])] = 0) → ResponseGetJob¶

Get Job

Retrieve the information of a job by id.

Parameters:: job_id (str) – (required)
Returns:: Returns the result object.

get_job_status(job_id: Annotated[str, Strict(strict=True)], _request_timeout: None | Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])] | Tuple[Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])], Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])]] = None, _request_auth: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _content_type: Annotated[str, Strict(strict=True)] | None = None, _headers: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _host_index: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=0)])] = 0) → str¶

Get Job Status

Parameters:: job_id (str) – (required)
Returns:: Returns the result object.

list_jobs(project_name: Annotated[str, Strict(strict=True)] | None = None, job_type: Annotated[str, Strict(strict=True)] | None = None, status: Any | None = None, limit: Annotated[int, Strict(strict=True)] | None = None, offset: Annotated[int, Strict(strict=True)] | None = None, _request_timeout: None | Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])] | Tuple[Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])], Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])]] = None, _request_auth: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _content_type: Annotated[str, Strict(strict=True)] | None = None, _headers: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _host_index: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=0)])] = 0) → ListJobsResponse¶

List Jobs

Get a list of jobs. These can be filtered by the name of a specific project and the status of a job (e.g. QUEUED, FEATURIZING, TRAINING_MODEL, COMPLETED etc.). The results are paginated and can be controlled using the limit and offset parameters.

Parameters:

project_name (str)
job_type (str)
status (Status)
limit (int)
offset (int)

Returns:

Returns the result object.

predict(prediction_job_request: PredictionJobRequest, _request_timeout: None | Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])] | Tuple[Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])], Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])]] = None, _request_auth: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _content_type: Annotated[str, Strict(strict=True)] | None = None, _headers: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _host_index: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=0)])] = 0) → PredictionJobResponse¶

Predict

Submit a prediction job based on the provided prediction specification.

Parameters:: prediction_job_request (PredictionJobRequest) – (required)
Returns:: Returns the result object.

train(training_job_request: TrainingJobRequest, _request_timeout: None | Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])] | Tuple[Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])], Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])]] = None, _request_auth: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _content_type: Annotated[str, Strict(strict=True)] | None = None, _headers: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _host_index: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=0)])] = 0) → TrainingJobResponse¶

Train

Submit a train job based on the provided train specification

Parameters:: training_job_request (TrainingJobRequest) – (required)
Returns:: Returns the result object.

update_jobs_status(job_id: Annotated[str, Strict(strict=True)], k8_s_job_status: K8SJobStatus, _request_timeout: None | Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])] | Tuple[Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])], Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])]] = None, _request_auth: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _content_type: Annotated[str, Strict(strict=True)] | None = None, _headers: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _host_index: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=0)])] = 0) → str¶

Update Jobs Status

Update the job’s K8s status

Parameters:

job_id (str) – (required)
k8_s_job_status (K8SJobStatus) – (required)

Returns:

Returns the result object.

ArangoGraphML Jobs API

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The version of the OpenAPI document: v1 Generated by OpenAPI Generator (https://openapi-generator.tech)

Do not edit the class manually.

class agml_api.jobs.v1.models.AlreadySubmittedMessage(*, message: Annotated[str, Strict(strict=True)])¶: Indicates that a job has already been submitted.

class agml_api.jobs.v1.models.BatchEmbeddingsJobRequest(*, projectName: Annotated[str, Strict(strict=True)] | None = None, projectID: Annotated[str, Strict(strict=True)] | None = None, task: Annotated[str, Strict(strict=True)] | None = None, databaseName: Annotated[str, Strict(strict=True)], modelID: Annotated[str, Strict(strict=True)], featureStoreGraphName: Annotated[str, Strict(strict=True)] | None = None, collection: Annotated[str, Strict(strict=True)] | None = None, embeddingsField: Annotated[str, Strict(strict=True)] | None = None, filterByAttribute: Annotated[str, Strict(strict=True)] | None = None, schedule: Annotated[str, Strict(strict=True)] | None = None, featurizeNewDocuments: Annotated[bool, Strict(strict=True)] | None = False, featurizeOutdatedDocuments: Annotated[bool, Strict(strict=True)] | None = False)¶: Request specification to create an Embeddings Generation job.

class agml_api.jobs.v1.models.BatchEmbeddingsJobResponse(*, jobID: Annotated[str, Strict(strict=True)], message: Annotated[str, Strict(strict=True)] | None = None, schedule: Annotated[str, Strict(strict=True)] | None = None)¶

class agml_api.jobs.v1.models.ClassificationTaskSpec(*, targetCollection: Annotated[str, Strict(strict=True)], inputFeatures: Annotated[str, Strict(strict=True)], labelField: Annotated[str, Strict(strict=True)], metrics: List[RequestTrainingMetric] | None = None, batchSize: Annotated[int, Strict(strict=True)] | None = 64)¶: Specification for a Node Classification task.

class agml_api.jobs.v1.models.DimensionalityReduction(*, disabled: Annotated[bool, Strict(strict=True)] | None = False, size: Annotated[int, Strict(strict=True)] | None = 512)¶: Configuration for Dimensionality Reduction in Featurization.

class agml_api.jobs.v1.models.Feature(*, featureType: Annotated[str, Strict(strict=True)], featureGenerator: FeatureGenerator | None = None)¶: Configuration for a Feature in Featurization.

class agml_api.jobs.v1.models.FeatureGenerator(*, method: Annotated[str, Strict(strict=True)], featureName: Annotated[str, Strict(strict=True)] | None = None, additional_properties: Dict[str, Any] = {})¶: Configuration for a Feature Generator in Featurization.

class agml_api.jobs.v1.models.FeatureTypeDefaultsInput(*, missing: MissingStrategy | None = None, mismatch: MismatchStrategy | None = None)¶: Default configuration for handling missing & mismatched values in Featurization.

class agml_api.jobs.v1.models.FeatureTypeDefaultsOutput(*, missing: MissingStrategy | None = None, mismatch: MismatchStrategy | None = None)¶: Default configuration for handling missing & mismatched values in Featurization.

class agml_api.jobs.v1.models.FeaturizationConfigurationInput(*, featurePrefix: Annotated[str, Strict(strict=True)] | None = 'feat_', dimensionalityReduction: DimensionalityReduction | None = None, outputName: Annotated[str, Strict(strict=True)] | None = 'x', defaultsPerFeatureType: Dict[str, FeatureTypeDefaultsInput] | None = None)¶: Feature-related configuration for Featurization.

class agml_api.jobs.v1.models.FeaturizationConfigurationOutput(*, featurePrefix: Annotated[str, Strict(strict=True)] | None = 'feat_', dimensionalityReduction: DimensionalityReduction | None = None, outputName: Annotated[str, Strict(strict=True)] | None = 'x', defaultsPerFeatureType: Dict[str, FeatureTypeDefaultsOutput] | None = None)¶: Feature-related configuration for Featurization.

class agml_api.jobs.v1.models.FeaturizationError(*, msg: Annotated[str, Strict(strict=True)], docs: List[Any] | None = None)¶: Error encountered during Featurization.

class agml_api.jobs.v1.models.FeaturizationJobConfiguration(*, batchSize: Annotated[int, Strict(strict=True)] | None = 32, runAnalysisChecks: Annotated[bool, Strict(strict=True)] | None = True, skipLabels: Annotated[bool, Strict(strict=True)] | None = False, overwriteFSGraph: Annotated[bool, Strict(strict=True)] | None = False, writeToSourceGraph: Annotated[bool, Strict(strict=True)] | None = True, useFeatureStore: Annotated[bool, Strict(strict=True)] | None = False)¶: Job-related configuration for Featurization.

class agml_api.jobs.v1.models.FeaturizationJobInformation(*, jobID: Annotated[str, Strict(strict=True)], jobStatus: Annotated[str, Strict(strict=True)], projectName: Annotated[str, Strict(strict=True)], projectID: Annotated[str, Strict(strict=True)], databaseName: Annotated[str, Strict(strict=True)], jobState: Dict[str, Any], metagraph: FeaturizationMetagraphOutput, jobConfiguration: FeaturizationJobConfiguration, featurizationConfiguration: FeaturizationConfigurationOutput, result: FeaturizationResult | None, warnings: List[FeaturizationWarning] | None, errors: List[FeaturizationError] | None, timeSubmitted: Annotated[str, Strict(strict=True)] | None, timeStarted: Annotated[str, Strict(strict=True)] | None, timeEnded: Annotated[str, Strict(strict=True)] | None, jobConditions: List[Dict[str, Any]] | None, featurizationType: Annotated[str, Strict(strict=True)])¶: A client-facing representation of a Featurization job.

class agml_api.jobs.v1.models.FeaturizationJobPhase(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)¶: Enum for the phases of a Featurization job: QUEUED, RUNNING, ANALYZING, FEATURIZING, COMPLETED, CANCELLED, ANALYSIS_ERROR, FAILED.

class agml_api.jobs.v1.models.FeaturizationJobRequest(*, featurizationName: Annotated[str, Strict(strict=True)], databaseName: Annotated[str, Strict(strict=True)], projectName: Annotated[str, Strict(strict=True)], graphName: Annotated[str, Strict(strict=True)], featureSetID: Annotated[str, Strict(strict=True)] | None = None, metagraph: FeaturizationMetagraphInput | None = None, jobConfiguration: FeaturizationJobConfiguration | None = None, featurizationConfiguration: FeaturizationConfigurationInput | None = None)¶: Request specification to create a Featurization Job.

class agml_api.jobs.v1.models.FeaturizationJobResponse(*, jobID: Annotated[str, Strict(strict=True)], message: Annotated[str, Strict(strict=True)] | None = None)¶: The initial response to a Featurization job request.

class agml_api.jobs.v1.models.FeaturizationMetagraphInput(*, vertexCollections: Dict[str, FeaturizationVertexCollectionInput], edgeCollections: Dict[str, Dict[str, Any]])¶: Configuration for the Metagraph in Featurization.

class agml_api.jobs.v1.models.FeaturizationMetagraphOutput(*, vertexCollections: Dict[str, FeaturizationVertexCollectionOutput], edgeCollections: Dict[str, Dict[str, Any]])¶: Configuration for the Metagraph in Featurization.

class agml_api.jobs.v1.models.FeaturizationOutput(*, featureSetID: Annotated[str, Strict(strict=True)], outputDBName: Annotated[str, Strict(strict=True)], graph: Annotated[str, Strict(strict=True)], vertexCollections: Dict[str, Any], edgeCollections: Dict[str, Any], labelField: Annotated[str, Strict(strict=True)] | None, inputField: Annotated[str, Strict(strict=True)] | None, isFeatureStore: Annotated[bool, Strict(strict=True)], targetCollection: Annotated[str, Strict(strict=True)] | None = None)¶: Output of a Featurization Job for a given Feature Set.

class agml_api.jobs.v1.models.FeaturizationResult(*, featureSetID: Annotated[str, Strict(strict=True)], outputDBName: Annotated[str, Strict(strict=True)], graph: Annotated[str, Strict(strict=True)], vertexCollections: Dict[str, Any], edgeCollections: Dict[str, Any], labelField: Annotated[str, Strict(strict=True)] | None, inputField: Annotated[str, Strict(strict=True)] | None, isFeatureStore: Annotated[bool, Strict(strict=True)], targetCollection: Annotated[str, Strict(strict=True)] | None = None, featureSetIds: List[Annotated[str, Strict(strict=True)]], featureSetIdToOutputs: Dict[str, FeaturizationOutput])¶: Result of a Featurization Job.

class agml_api.jobs.v1.models.FeaturizationVertexCollectionInput(*, features: Dict[str, Feature], config: FeaturizationConfigurationInput | None = None)¶: Configuration for a Vertex Collection in Featurization.

class agml_api.jobs.v1.models.FeaturizationVertexCollectionOutput(*, features: Dict[str, Feature], config: FeaturizationConfigurationOutput | None = None)¶: Configuration for a Vertex Collection in Featurization.

class agml_api.jobs.v1.models.FeaturizationWarning(*, msg: Annotated[str, Strict(strict=True)], docs: List[Any] | None = None)¶: Warning encountered during Featurization.

class agml_api.jobs.v1.models.GraphEmbeddingsTaskSpec(*, targetCollection: Annotated[str, Strict(strict=True)] | None = None, embeddingLevel: Annotated[str, Strict(strict=True)] | None = 'NODE_EMBEDDINGS', embeddingSize: Annotated[int, Strict(strict=True)] | None = 128, embeddingTrainingType: Annotated[str, Strict(strict=True)] | None = 'UNSUPERVISED', batchSize: Annotated[int, Strict(strict=True)] | None = 64, generateEmbeddings: Annotated[bool, Strict(strict=True)] | None = False, bestModelSelection: Annotated[str, Strict(strict=True)] | None = 'BEST_LOSS', persistModels: Annotated[str, Strict(strict=True)] | None = 'ALL_MODELS', modelConfigurations: Dict[str, Any] | None = None)¶: Specification for a Graph Embeddings task.

class agml_api.jobs.v1.models.HTTPValidationError(*, detail: List[ValidationError] | None = None)¶

class agml_api.jobs.v1.models.JobsInner(*args, anyof_schema_1_validator: TrainingJobInformation | None = None, anyof_schema_2_validator: FeaturizationJobInformation | None = None, anyof_schema_3_validator: PredictionJobInformation | None = None, actual_instance: Any = None, any_of_schemas: List[str] = typing.Literal['FeaturizationJobInformation', 'PredictionJobInformation', 'TrainingJobInformation'])¶

class agml_api.jobs.v1.models.K8SConditionStatus(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)¶

class agml_api.jobs.v1.models.K8SJobCondition(*, status: K8SConditionStatus, type: K8SJobConditionType, lastProbeTime: Annotated[str, Strict(strict=True)] | None = None, lastTransitionTime: Annotated[str, Strict(strict=True)] | None = None, reason: Annotated[str, Strict(strict=True)] | None = None, message: Annotated[str, Strict(strict=True)] | None = None)¶

class agml_api.jobs.v1.models.K8SJobConditionType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)¶

class agml_api.jobs.v1.models.K8SJobStatus(*, startTime: Annotated[str, Strict(strict=True)] | None = None, completionTime: Annotated[str, Strict(strict=True)] | None = None, active: Annotated[int, Strict(strict=True)] | None = None, failed: Annotated[int, Strict(strict=True)] | None = None, succeeded: Annotated[int, Strict(strict=True)] | None = None, conditions: List[K8SJobCondition] | None = None)¶

class agml_api.jobs.v1.models.ListJobsResponse(*, jobs: List[JobsInner], offset: Annotated[int, Strict(strict=True)], limit: Annotated[int, Strict(strict=True)])¶: A paginated list of Featurization, Training, or Prediction jobs.

class agml_api.jobs.v1.models.LocationInner(*args, anyof_schema_1_validator: Annotated[str, Strict(strict=True)] | None = None, anyof_schema_2_validator: Annotated[int, Strict(strict=True)] | None = None, actual_instance: Any = None, any_of_schemas: List[str] = typing.Literal['int', 'str'])¶

class agml_api.jobs.v1.models.MLSpec(*, classification: ClassificationTaskSpec | None = None, graphEmbeddings: GraphEmbeddingsTaskSpec | None = None)¶: Machine Learning Specification.

class agml_api.jobs.v1.models.MismatchStrategy(*, strategy: MismatchStrategyType, replacement: Any | None = None)¶: Configuration for handling mismatched values in Featurization.

class agml_api.jobs.v1.models.MismatchStrategyType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)¶: Enum for handling mismatched values in Featurization: REPLACE, RAISE, COERCE_REPLACE, COERCE_RAISE.

class agml_api.jobs.v1.models.MissingStrategy(*, strategy: MissingStrategyType, replacement: Any | None = None)¶: Configuration for handling missing values in Featurization.

class agml_api.jobs.v1.models.MissingStrategyType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)¶: Enum for handling missing values in Featurization: REPLACE, RAISE.

class agml_api.jobs.v1.models.PredictionJobConfiguration(*, dataLoadBatchSize: Annotated[int, Strict(strict=True)] | None = 500000, dataLoadParallelism: Annotated[int, Strict(strict=True)] | None = 10)¶: Job-related configuration for a prediction job.

class agml_api.jobs.v1.models.PredictionJobInformation(*, jobID: Annotated[str, Strict(strict=True)], jobStatus: Annotated[str, Strict(strict=True)], projectName: Annotated[str, Strict(strict=True)], projectID: Annotated[str, Strict(strict=True)], databaseName: Annotated[str, Strict(strict=True)], modelID: Annotated[str, Strict(strict=True)], jobStateInformation: Dict[str, Any] | None, timeSubmitted: Annotated[str, Strict(strict=True)] | None, timeStarted: Annotated[str, Strict(strict=True)] | None, timeEnded: Annotated[str, Strict(strict=True)] | None, predictionType: Annotated[str, Strict(strict=True)])¶: A client-facing representation of a Prediction job.

class agml_api.jobs.v1.models.PredictionJobPhase(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)¶: Enum for the phases of a Prediction job: QUEUED, RUNNING, LOADING_DATA, CREATING_PREDICTIONS, STORING_RESULTS, COMPLETED, CANCELLED, FAILED.

class agml_api.jobs.v1.models.PredictionJobRequest(*, projectName: Annotated[str, Strict(strict=True)] | None = None, projectID: Annotated[str, Strict(strict=True)] | None = None, databaseName: Annotated[str, Strict(strict=True)], modelID: Annotated[str, Strict(strict=True)], featureStoreGraphName: Annotated[str, Strict(strict=True)] | None = None, collection: Annotated[str, Strict(strict=True)] | None = None, predictionField: Annotated[str, Strict(strict=True)] | None = None, filterByAttribute: Annotated[str, Strict(strict=True)] | None = None, schedule: Annotated[str, Strict(strict=True)] | None = None, featurizeNewDocuments: Annotated[bool, Strict(strict=True)] | None = False, featurizeOutdatedDocuments: Annotated[bool, Strict(strict=True)] | None = False, jobConfiguration: PredictionJobConfiguration | None = None)¶: Request specification to create a Prediction job.

class agml_api.jobs.v1.models.PredictionJobResponse(*, jobID: Annotated[str, Strict(strict=True)], message: Annotated[str, Strict(strict=True)] | None = None, schedule: Annotated[str, Strict(strict=True)] | None = None)¶: The initial response to a Prediction job request.

class agml_api.jobs.v1.models.RequestTrainingMetric(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)¶: Enum for the metrics that can be requested for training: ACCURACY, F1, ROCAUC, PRECISION, RECALL, CONFUSION.

class agml_api.jobs.v1.models.ResponseGetJob(*args, anyof_schema_1_validator: TrainingJobInformation | None = None, anyof_schema_2_validator: FeaturizationJobInformation | None = None, anyof_schema_3_validator: PredictionJobInformation | None = None, actual_instance: Any = None, any_of_schemas: List[str] = typing.Literal['FeaturizationJobInformation', 'PredictionJobInformation', 'TrainingJobInformation'])¶

class agml_api.jobs.v1.models.Status(*args, anyof_schema_1_validator: TrainingJobPhase | None = None, anyof_schema_2_validator: FeaturizationJobPhase | None = None, anyof_schema_3_validator: PredictionJobPhase | None = None, actual_instance: Any = None, any_of_schemas: List[str] = typing.Literal['FeaturizationJobPhase', 'PredictionJobPhase', 'TrainingJobPhase'])¶

class agml_api.jobs.v1.models.TrainingJobConfiguration(*, dataLoadParallelism: Annotated[int, Strict(strict=True)] | None = 10, dataLoadBatchSize: Annotated[int, Strict(strict=True)] | None = 500000)¶: Job-related configuration for a Training job.

class agml_api.jobs.v1.models.TrainingJobInformation(*, jobID: Annotated[str, Strict(strict=True)], jobStatus: Annotated[str, Strict(strict=True)], projectName: Annotated[str, Strict(strict=True)], projectID: Annotated[str, Strict(strict=True)], databaseName: Annotated[str, Strict(strict=True)], mlSpec: Dict[str, Any], metagraph: Dict[str, Any], jobState: Dict[str, Any] | None, timeSubmitted: Annotated[str, Strict(strict=True)] | None, timeStarted: Annotated[str, Strict(strict=True)] | None, timeEnded: Annotated[str, Strict(strict=True)] | None, jobConditions: List[Dict[str, Any]] | None, trainingType: Annotated[str, Strict(strict=True)])¶: A client-facing representation of a Training job.

class agml_api.jobs.v1.models.TrainingJobPhase(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)¶: Enum for the phases of a Training job: QUEUED, RUNNING, LOADING_DATA, TRAINING_MODEL, STORING_RESULTS, COMPLETED, CANCELLED, FAILED.

class agml_api.jobs.v1.models.TrainingJobRequest(*, databaseName: Annotated[str, Strict(strict=True)] | None = None, projectName: Annotated[str, Strict(strict=True)] | None = None, mlSpec: MLSpec, usesFeatureStore: Annotated[bool, Strict(strict=True)] | None = False, metagraph: TrainingMetagraph | None = None, featureSetID: Annotated[str, Strict(strict=True)] | None = None, jobConfiguration: TrainingJobConfiguration | None = None)¶: Request specification to create a Training job.

class agml_api.jobs.v1.models.TrainingJobResponse(*, jobID: Annotated[str, Strict(strict=True)], message: Annotated[str, Strict(strict=True)] | None = None)¶: The initial response to a Training job request.

class agml_api.jobs.v1.models.TrainingMetagraph(*, graph: Annotated[str, Strict(strict=True)], vertexCollections: Dict[str, Dict[str, Annotated[str, Strict(strict=True)]]], edgeCollections: Dict[str, Any])¶: Configuration for the Metagraph in Training.

class agml_api.jobs.v1.models.ValidationError(*, loc: List[LocationInner], msg: Annotated[str, Strict(strict=True)], type: Annotated[str, Strict(strict=True)])¶

Metadata¶

class agml_api.metadata.v1.api.metadata_api.MetadataApi(api_client=None)¶

NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech

Do not edit the class manually.

get_label_mappings(feature_set_id: Annotated[str, Strict(strict=True)], _request_timeout: None | Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])] | Tuple[Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])], Annotated[float, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Gt(gt=0)])]] = None, _request_auth: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _content_type: Annotated[str, Strict(strict=True)] | None = None, _headers: Dict[Annotated[str, Strict(strict=True)], Any] | None = None, _host_index: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=0)])] = 0) → Dict[str, Dict[str, int]]¶

Get Label Mappings

Get the label mappings for the specified feature set

Parameters:: feature_set_id (str) – (required)
Returns:: Returns the result object.

ArangoGraphML Metadata API

No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator)

The version of the OpenAPI document: v1 Generated by OpenAPI Generator (https://openapi-generator.tech)

Do not edit the class manually.

class agml_api.metadata.v1.models.HTTPValidationError(*, detail: List[ValidationError] | None = None)¶

class agml_api.metadata.v1.models.LocationInner(*args, anyof_schema_1_validator: Annotated[str, Strict(strict=True)] | None = None, anyof_schema_2_validator: Annotated[int, Strict(strict=True)] | None = None, actual_instance: Any = None, any_of_schemas: List[str] = typing.Literal['int', 'str'])¶

class agml_api.metadata.v1.models.ValidationError(*, loc: List[LocationInner], msg: Annotated[str, Strict(strict=True)], type: Annotated[str, Strict(strict=True)])¶