Oracle Cloud Infrastructure v3.10.0, Nov 5 25

Oracle Cloud Infrastructure v3.10.0 published on Wednesday, Nov 5, 2025 by Pulumi

oci.AiLanguage.Job

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Explain and create an oci.AiLanguage.Job resource

Oracle Cloud Infrastructure v3.10.0 published on Wednesday, Nov 5, 2025 by Pulumi

pulumi/pulumi-oci

Example Usage

Example coming soon!

Example coming soon!

Example coming soon!

Example coming soon!

Example coming soon!

resources:
  testJob:
    type: oci:AiLanguage:Job
    name: test_job
    properties:
      compartmentId: ${compartmentId}
      inputLocation:
        bucket: ${jobInputLocationBucket}
        locationType: ${jobInputLocationLocationType}
        namespace: ${jobInputLocationNamespace}
        objectNames: ${jobInputLocationObjectNames}
        prefix: ${jobInputLocationPrefix}
      modelMetadataDetails:
        - configuration:
            - configurationMap: ${jobModelMetadataDetailsConfigurationConfigurationMap}
          endpointId: ${testEndpoint.id}
          languageCode: ${jobModelMetadataDetailsLanguageCode}
          modelId: ${testModel.id}
          modelType: ${jobModelMetadataDetailsModelType}
      outputLocation:
        bucket: ${jobOutputLocationBucket}
        namespace: ${jobOutputLocationNamespace}
        prefix: ${jobOutputLocationPrefix}
      description: ${jobDescription}
      displayName: ${jobDisplayName}
      inputConfiguration:
        configuration:
          - config: ${jobInputConfigurationConfigurationConfig}
        documentTypes: ${jobInputConfigurationDocumentTypes}

Create Job Resource

Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.

Constructor syntax

new Job(name: string, args: JobArgs, opts?: CustomResourceOptions);

@overload
def Job(resource_name: str,
        args: JobArgs,
        opts: Optional[ResourceOptions] = None)

@overload
def Job(resource_name: str,
        opts: Optional[ResourceOptions] = None,
        compartment_id: Optional[str] = None,
        input_location: Optional[JobInputLocationArgs] = None,
        model_metadata_details: Optional[Sequence[JobModelMetadataDetailArgs]] = None,
        output_location: Optional[JobOutputLocationArgs] = None,
        description: Optional[str] = None,
        display_name: Optional[str] = None,
        input_configuration: Optional[JobInputConfigurationArgs] = None)

func NewJob(ctx *Context, name string, args JobArgs, opts ...ResourceOption) (*Job, error)

public Job(string name, JobArgs args, CustomResourceOptions? opts = null)

public Job(String name, JobArgs args)
public Job(String name, JobArgs args, CustomResourceOptions options)

type: oci:AiLanguage:Job
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

Parameters

name string: The unique name of the resource.
args JobArgs: The arguments to resource properties.
opts CustomResourceOptions: Bag of options to control resource's behavior.

resource_name str: The unique name of the resource.
args JobArgs: The arguments to resource properties.
opts ResourceOptions: Bag of options to control resource's behavior.

ctx Context: Context object for the current deployment.
name string: The unique name of the resource.
args JobArgs: The arguments to resource properties.
opts ResourceOption: Bag of options to control resource's behavior.

name string: The unique name of the resource.
args JobArgs: The arguments to resource properties.
opts CustomResourceOptions: Bag of options to control resource's behavior.

name String: The unique name of the resource.
args JobArgs: The arguments to resource properties.
options CustomResourceOptions: Bag of options to control resource's behavior.

Constructor example

The following reference example uses placeholder values for all input properties.

var jobResource = new Oci.AiLanguage.Job("jobResource", new()
{
    CompartmentId = "string",
    InputLocation = new Oci.AiLanguage.Inputs.JobInputLocationArgs
    {
        Bucket = "string",
        LocationType = "string",
        Namespace = "string",
        ObjectNames = new[]
        {
            "string",
        },
    },
    ModelMetadataDetails = new[]
    {
        new Oci.AiLanguage.Inputs.JobModelMetadataDetailArgs
        {
            Configuration = 
            {
                { "string", 
                {
                    { "string", "string" },
                } },
            },
            EndpointId = "string",
            LanguageCode = "string",
            ModelId = "string",
            ModelType = "string",
        },
    },
    OutputLocation = new Oci.AiLanguage.Inputs.JobOutputLocationArgs
    {
        Bucket = "string",
        Namespace = "string",
        Prefix = "string",
    },
    Description = "string",
    DisplayName = "string",
    InputConfiguration = new Oci.AiLanguage.Inputs.JobInputConfigurationArgs
    {
        Configuration = 
        {
            { "string", 
            {
                { "string", "string" },
            } },
        },
        DocumentTypes = new[]
        {
            "string",
        },
    },
});

example, err := ailanguage.NewJob(ctx, "jobResource", &ailanguage.JobArgs{
	CompartmentId: pulumi.String("string"),
	InputLocation: &ailanguage.JobInputLocationArgs{
		Bucket:       pulumi.String("string"),
		LocationType: pulumi.String("string"),
		Namespace:    pulumi.String("string"),
		ObjectNames: pulumi.StringArray{
			pulumi.String("string"),
		},
	},
	ModelMetadataDetails: ailanguage.JobModelMetadataDetailArray{
		&ailanguage.JobModelMetadataDetailArgs{
			Configuration: pulumi.StringMapMap{
				"string": pulumi.StringMap{
					"string": pulumi.String("string"),
				},
			},
			EndpointId:   pulumi.String("string"),
			LanguageCode: pulumi.String("string"),
			ModelId:      pulumi.String("string"),
			ModelType:    pulumi.String("string"),
		},
	},
	OutputLocation: &ailanguage.JobOutputLocationArgs{
		Bucket:    pulumi.String("string"),
		Namespace: pulumi.String("string"),
		Prefix:    pulumi.String("string"),
	},
	Description: pulumi.String("string"),
	DisplayName: pulumi.String("string"),
	InputConfiguration: &ailanguage.JobInputConfigurationArgs{
		Configuration: pulumi.StringMapMap{
			"string": pulumi.StringMap{
				"string": pulumi.String("string"),
			},
		},
		DocumentTypes: pulumi.StringArray{
			pulumi.String("string"),
		},
	},
})

var jobResource = new com.pulumi.oci.AiLanguage.Job("jobResource", com.pulumi.oci.AiLanguage.JobArgs.builder()
    .compartmentId("string")
    .inputLocation(JobInputLocationArgs.builder()
        .bucket("string")
        .locationType("string")
        .namespace("string")
        .objectNames("string")
        .build())
    .modelMetadataDetails(JobModelMetadataDetailArgs.builder()
        .configuration(Map.of("string", Map.of("string", "string")))
        .endpointId("string")
        .languageCode("string")
        .modelId("string")
        .modelType("string")
        .build())
    .outputLocation(JobOutputLocationArgs.builder()
        .bucket("string")
        .namespace("string")
        .prefix("string")
        .build())
    .description("string")
    .displayName("string")
    .inputConfiguration(JobInputConfigurationArgs.builder()
        .configuration(Map.of("string", Map.of("string", "string")))
        .documentTypes("string")
        .build())
    .build());

job_resource = oci.ailanguage.Job("jobResource",
    compartment_id="string",
    input_location={
        "bucket": "string",
        "location_type": "string",
        "namespace": "string",
        "object_names": ["string"],
    },
    model_metadata_details=[{
        "configuration": {
            "string": {
                "string": "string",
            },
        },
        "endpoint_id": "string",
        "language_code": "string",
        "model_id": "string",
        "model_type": "string",
    }],
    output_location={
        "bucket": "string",
        "namespace": "string",
        "prefix": "string",
    },
    description="string",
    display_name="string",
    input_configuration={
        "configuration": {
            "string": {
                "string": "string",
            },
        },
        "document_types": ["string"],
    })

const jobResource = new oci.ailanguage.Job("jobResource", {
    compartmentId: "string",
    inputLocation: {
        bucket: "string",
        locationType: "string",
        namespace: "string",
        objectNames: ["string"],
    },
    modelMetadataDetails: [{
        configuration: {
            string: {
                string: "string",
            },
        },
        endpointId: "string",
        languageCode: "string",
        modelId: "string",
        modelType: "string",
    }],
    outputLocation: {
        bucket: "string",
        namespace: "string",
        prefix: "string",
    },
    description: "string",
    displayName: "string",
    inputConfiguration: {
        configuration: {
            string: {
                string: "string",
            },
        },
        documentTypes: ["string"],
    },
});

type: oci:AiLanguage:Job
properties:
    compartmentId: string
    description: string
    displayName: string
    inputConfiguration:
        configuration:
            string:
                string: string
        documentTypes:
            - string
    inputLocation:
        bucket: string
        locationType: string
        namespace: string
        objectNames:
            - string
    modelMetadataDetails:
        - configuration:
            string:
                string: string
          endpointId: string
          languageCode: string
          modelId: string
          modelType: string
    outputLocation:
        bucket: string
        namespace: string
        prefix: string

Job Resource Properties

To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

Inputs

In Python, inputs that are objects can be passed either as argument classes or as dictionary literals.

The Job resource accepts the following input properties:

CompartmentId string: (Updatable) The OCID of the compartment where you want to create the job.
InputLocation JobInputLocation: document location and other meta data about documents For TXT only ObjectStoragePrefixLocation supported For CSV only ObjectStorageFileNameLocation is supported For this release only one file is supported for ObjectStorageFileNameLocation i.e CSV file type
ModelMetadataDetails List<JobModelMetadataDetail>: training model details For this release only one model is allowed to be input here. One of the three modelType, ModelId, endpointId should be given other wise error will be thrown from API
OutputLocation JobOutputLocation: Object storage output location to write inference results
Description string: (Updatable) A short description of the job.
DisplayName string: (Updatable) A user-friendly display name for the job.
InputConfiguration JobInputConfiguration: input documents configuration by default TXT files will be processed and this behaviour will not change in future after adding new types

CompartmentId string: (Updatable) The OCID of the compartment where you want to create the job.
InputLocation JobInputLocationArgs: document location and other meta data about documents For TXT only ObjectStoragePrefixLocation supported For CSV only ObjectStorageFileNameLocation is supported For this release only one file is supported for ObjectStorageFileNameLocation i.e CSV file type
ModelMetadataDetails []JobModelMetadataDetailArgs: training model details For this release only one model is allowed to be input here. One of the three modelType, ModelId, endpointId should be given other wise error will be thrown from API
OutputLocation JobOutputLocationArgs: Object storage output location to write inference results
Description string: (Updatable) A short description of the job.
DisplayName string: (Updatable) A user-friendly display name for the job.
InputConfiguration JobInputConfigurationArgs: input documents configuration by default TXT files will be processed and this behaviour will not change in future after adding new types

compartmentId String: (Updatable) The OCID of the compartment where you want to create the job.
inputLocation JobInputLocation: document location and other meta data about documents For TXT only ObjectStoragePrefixLocation supported For CSV only ObjectStorageFileNameLocation is supported For this release only one file is supported for ObjectStorageFileNameLocation i.e CSV file type
modelMetadataDetails List<JobModelMetadataDetail>: training model details For this release only one model is allowed to be input here. One of the three modelType, ModelId, endpointId should be given other wise error will be thrown from API
outputLocation JobOutputLocation: Object storage output location to write inference results
description String: (Updatable) A short description of the job.
displayName String: (Updatable) A user-friendly display name for the job.
inputConfiguration JobInputConfiguration: input documents configuration by default TXT files will be processed and this behaviour will not change in future after adding new types

compartmentId string: (Updatable) The OCID of the compartment where you want to create the job.
inputLocation JobInputLocation: document location and other meta data about documents For TXT only ObjectStoragePrefixLocation supported For CSV only ObjectStorageFileNameLocation is supported For this release only one file is supported for ObjectStorageFileNameLocation i.e CSV file type
modelMetadataDetails JobModelMetadataDetail[]: training model details For this release only one model is allowed to be input here. One of the three modelType, ModelId, endpointId should be given other wise error will be thrown from API
outputLocation JobOutputLocation: Object storage output location to write inference results
description string: (Updatable) A short description of the job.
displayName string: (Updatable) A user-friendly display name for the job.
inputConfiguration JobInputConfiguration: input documents configuration by default TXT files will be processed and this behaviour will not change in future after adding new types

compartment_id str: (Updatable) The OCID of the compartment where you want to create the job.
input_location JobInputLocationArgs: document location and other meta data about documents For TXT only ObjectStoragePrefixLocation supported For CSV only ObjectStorageFileNameLocation is supported For this release only one file is supported for ObjectStorageFileNameLocation i.e CSV file type
model_metadata_details Sequence[JobModelMetadataDetailArgs]: training model details For this release only one model is allowed to be input here. One of the three modelType, ModelId, endpointId should be given other wise error will be thrown from API
output_location JobOutputLocationArgs: Object storage output location to write inference results
description str: (Updatable) A short description of the job.
display_name str: (Updatable) A user-friendly display name for the job.
input_configuration JobInputConfigurationArgs: input documents configuration by default TXT files will be processed and this behaviour will not change in future after adding new types

compartmentId String: (Updatable) The OCID of the compartment where you want to create the job.
inputLocation Property Map: document location and other meta data about documents For TXT only ObjectStoragePrefixLocation supported For CSV only ObjectStorageFileNameLocation is supported For this release only one file is supported for ObjectStorageFileNameLocation i.e CSV file type
modelMetadataDetails List<Property Map>: training model details For this release only one model is allowed to be input here. One of the three modelType, ModelId, endpointId should be given other wise error will be thrown from API
outputLocation Property Map: Object storage output location to write inference results
description String: (Updatable) A short description of the job.
displayName String: (Updatable) A user-friendly display name for the job.
inputConfiguration Property Map: input documents configuration by default TXT files will be processed and this behaviour will not change in future after adding new types

Outputs

All input properties are implicitly available as output properties. Additionally, the Job resource produces the following output properties:

CompletedDocuments int: Number of documents processed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
CreatedBy string: The OCID of the user who created the job.
FailedDocuments int: Number of documents failed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
Id string: The provider-assigned unique ID for this managed resource.
LifecycleDetails string: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
PendingDocuments int: Number of documents still to process. For CSV this signifies number of rows and for TXT this signifies number of files.
PercentComplete int: How much progress the operation has made, vs the total amount of work that must be performed.
State string: The current state of the Job.
TimeAccepted string: Job accepted time.
TimeCompleted string: Job finished time.
TimeStarted string: Job started time.
TotalDocuments int: Total number of documents given as input for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
TtlInDays int: Time to live duration in days for Job. Job will be available till max 90 days.
WarningsCount int: warnings count

CompletedDocuments int: Number of documents processed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
CreatedBy string: The OCID of the user who created the job.
FailedDocuments int: Number of documents failed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
Id string: The provider-assigned unique ID for this managed resource.
LifecycleDetails string: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
PendingDocuments int: Number of documents still to process. For CSV this signifies number of rows and for TXT this signifies number of files.
PercentComplete int: How much progress the operation has made, vs the total amount of work that must be performed.
State string: The current state of the Job.
TimeAccepted string: Job accepted time.
TimeCompleted string: Job finished time.
TimeStarted string: Job started time.
TotalDocuments int: Total number of documents given as input for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
TtlInDays int: Time to live duration in days for Job. Job will be available till max 90 days.
WarningsCount int: warnings count

completedDocuments Integer: Number of documents processed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
createdBy String: The OCID of the user who created the job.
failedDocuments Integer: Number of documents failed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
id String: The provider-assigned unique ID for this managed resource.
lifecycleDetails String: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
pendingDocuments Integer: Number of documents still to process. For CSV this signifies number of rows and for TXT this signifies number of files.
percentComplete Integer: How much progress the operation has made, vs the total amount of work that must be performed.
state String: The current state of the Job.
timeAccepted String: Job accepted time.
timeCompleted String: Job finished time.
timeStarted String: Job started time.
totalDocuments Integer: Total number of documents given as input for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
ttlInDays Integer: Time to live duration in days for Job. Job will be available till max 90 days.
warningsCount Integer: warnings count

completedDocuments number: Number of documents processed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
createdBy string: The OCID of the user who created the job.
failedDocuments number: Number of documents failed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
id string: The provider-assigned unique ID for this managed resource.
lifecycleDetails string: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
pendingDocuments number: Number of documents still to process. For CSV this signifies number of rows and for TXT this signifies number of files.
percentComplete number: How much progress the operation has made, vs the total amount of work that must be performed.
state string: The current state of the Job.
timeAccepted string: Job accepted time.
timeCompleted string: Job finished time.
timeStarted string: Job started time.
totalDocuments number: Total number of documents given as input for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
ttlInDays number: Time to live duration in days for Job. Job will be available till max 90 days.
warningsCount number: warnings count

completed_documents int: Number of documents processed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
created_by str: The OCID of the user who created the job.
failed_documents int: Number of documents failed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
id str: The provider-assigned unique ID for this managed resource.
lifecycle_details str: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
pending_documents int: Number of documents still to process. For CSV this signifies number of rows and for TXT this signifies number of files.
percent_complete int: How much progress the operation has made, vs the total amount of work that must be performed.
state str: The current state of the Job.
time_accepted str: Job accepted time.
time_completed str: Job finished time.
time_started str: Job started time.
total_documents int: Total number of documents given as input for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
ttl_in_days int: Time to live duration in days for Job. Job will be available till max 90 days.
warnings_count int: warnings count

completedDocuments Number: Number of documents processed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
createdBy String: The OCID of the user who created the job.
failedDocuments Number: Number of documents failed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
id String: The provider-assigned unique ID for this managed resource.
lifecycleDetails String: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
pendingDocuments Number: Number of documents still to process. For CSV this signifies number of rows and for TXT this signifies number of files.
percentComplete Number: How much progress the operation has made, vs the total amount of work that must be performed.
state String: The current state of the Job.
timeAccepted String: Job accepted time.
timeCompleted String: Job finished time.
timeStarted String: Job started time.
totalDocuments Number: Total number of documents given as input for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
ttlInDays Number: Time to live duration in days for Job. Job will be available till max 90 days.
warningsCount Number: warnings count

Look up Existing Job Resource

Get an existing Job resource’s state with the given name, ID, and optional extra properties used to qualify the lookup.

public static get(name: string, id: Input<ID>, state?: JobState, opts?: CustomResourceOptions): Job

@staticmethod
def get(resource_name: str,
        id: str,
        opts: Optional[ResourceOptions] = None,
        compartment_id: Optional[str] = None,
        completed_documents: Optional[int] = None,
        created_by: Optional[str] = None,
        description: Optional[str] = None,
        display_name: Optional[str] = None,
        failed_documents: Optional[int] = None,
        input_configuration: Optional[JobInputConfigurationArgs] = None,
        input_location: Optional[JobInputLocationArgs] = None,
        lifecycle_details: Optional[str] = None,
        model_metadata_details: Optional[Sequence[JobModelMetadataDetailArgs]] = None,
        output_location: Optional[JobOutputLocationArgs] = None,
        pending_documents: Optional[int] = None,
        percent_complete: Optional[int] = None,
        state: Optional[str] = None,
        time_accepted: Optional[str] = None,
        time_completed: Optional[str] = None,
        time_started: Optional[str] = None,
        total_documents: Optional[int] = None,
        ttl_in_days: Optional[int] = None,
        warnings_count: Optional[int] = None) -> Job

func GetJob(ctx *Context, name string, id IDInput, state *JobState, opts ...ResourceOption) (*Job, error)

public static Job Get(string name, Input<string> id, JobState? state, CustomResourceOptions? opts = null)

public static Job get(String name, Output<String> id, JobState state, CustomResourceOptions options)

resources:  _:    type: oci:AiLanguage:Job    get:      id: ${id}

name: The unique name of the resulting resource.
id: The unique provider ID of the resource to lookup.
state: Any extra arguments used during the lookup.
opts: A bag of options that control this resource's behavior.

resource_name: The unique name of the resulting resource.
id: The unique provider ID of the resource to lookup.

name: The unique name of the resulting resource.
id: The unique provider ID of the resource to lookup.
state: Any extra arguments used during the lookup.
opts: A bag of options that control this resource's behavior.

name: The unique name of the resulting resource.
id: The unique provider ID of the resource to lookup.
state: Any extra arguments used during the lookup.
opts: A bag of options that control this resource's behavior.

name: The unique name of the resulting resource.
id: The unique provider ID of the resource to lookup.
state: Any extra arguments used during the lookup.
opts: A bag of options that control this resource's behavior.

The following state arguments are supported:

CompartmentId string: (Updatable) The OCID of the compartment where you want to create the job.
CompletedDocuments int: Number of documents processed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
CreatedBy string: The OCID of the user who created the job.
Description string: (Updatable) A short description of the job.
DisplayName string: (Updatable) A user-friendly display name for the job.
FailedDocuments int: Number of documents failed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
InputConfiguration JobInputConfiguration: input documents configuration by default TXT files will be processed and this behaviour will not change in future after adding new types
InputLocation JobInputLocation: document location and other meta data about documents For TXT only ObjectStoragePrefixLocation supported For CSV only ObjectStorageFileNameLocation is supported For this release only one file is supported for ObjectStorageFileNameLocation i.e CSV file type
LifecycleDetails string: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
ModelMetadataDetails List<JobModelMetadataDetail>: training model details For this release only one model is allowed to be input here. One of the three modelType, ModelId, endpointId should be given other wise error will be thrown from API
OutputLocation JobOutputLocation: Object storage output location to write inference results
PendingDocuments int: Number of documents still to process. For CSV this signifies number of rows and for TXT this signifies number of files.
PercentComplete int: How much progress the operation has made, vs the total amount of work that must be performed.
State string: The current state of the Job.
TimeAccepted string: Job accepted time.
TimeCompleted string: Job finished time.
TimeStarted string: Job started time.
TotalDocuments int: Total number of documents given as input for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
TtlInDays int: Time to live duration in days for Job. Job will be available till max 90 days.
WarningsCount int: warnings count

CompartmentId string: (Updatable) The OCID of the compartment where you want to create the job.
CompletedDocuments int: Number of documents processed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
CreatedBy string: The OCID of the user who created the job.
Description string: (Updatable) A short description of the job.
DisplayName string: (Updatable) A user-friendly display name for the job.
FailedDocuments int: Number of documents failed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
InputConfiguration JobInputConfigurationArgs: input documents configuration by default TXT files will be processed and this behaviour will not change in future after adding new types
InputLocation JobInputLocationArgs: document location and other meta data about documents For TXT only ObjectStoragePrefixLocation supported For CSV only ObjectStorageFileNameLocation is supported For this release only one file is supported for ObjectStorageFileNameLocation i.e CSV file type
LifecycleDetails string: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
ModelMetadataDetails []JobModelMetadataDetailArgs: training model details For this release only one model is allowed to be input here. One of the three modelType, ModelId, endpointId should be given other wise error will be thrown from API
OutputLocation JobOutputLocationArgs: Object storage output location to write inference results
PendingDocuments int: Number of documents still to process. For CSV this signifies number of rows and for TXT this signifies number of files.
PercentComplete int: How much progress the operation has made, vs the total amount of work that must be performed.
State string: The current state of the Job.
TimeAccepted string: Job accepted time.
TimeCompleted string: Job finished time.
TimeStarted string: Job started time.
TotalDocuments int: Total number of documents given as input for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
TtlInDays int: Time to live duration in days for Job. Job will be available till max 90 days.
WarningsCount int: warnings count

compartmentId String: (Updatable) The OCID of the compartment where you want to create the job.
completedDocuments Integer: Number of documents processed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
createdBy String: The OCID of the user who created the job.
description String: (Updatable) A short description of the job.
displayName String: (Updatable) A user-friendly display name for the job.
failedDocuments Integer: Number of documents failed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
inputConfiguration JobInputConfiguration: input documents configuration by default TXT files will be processed and this behaviour will not change in future after adding new types
inputLocation JobInputLocation: document location and other meta data about documents For TXT only ObjectStoragePrefixLocation supported For CSV only ObjectStorageFileNameLocation is supported For this release only one file is supported for ObjectStorageFileNameLocation i.e CSV file type
lifecycleDetails String: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
modelMetadataDetails List<JobModelMetadataDetail>: training model details For this release only one model is allowed to be input here. One of the three modelType, ModelId, endpointId should be given other wise error will be thrown from API
outputLocation JobOutputLocation: Object storage output location to write inference results
pendingDocuments Integer: Number of documents still to process. For CSV this signifies number of rows and for TXT this signifies number of files.
percentComplete Integer: How much progress the operation has made, vs the total amount of work that must be performed.
state String: The current state of the Job.
timeAccepted String: Job accepted time.
timeCompleted String: Job finished time.
timeStarted String: Job started time.
totalDocuments Integer: Total number of documents given as input for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
ttlInDays Integer: Time to live duration in days for Job. Job will be available till max 90 days.
warningsCount Integer: warnings count

compartmentId string: (Updatable) The OCID of the compartment where you want to create the job.
completedDocuments number: Number of documents processed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
createdBy string: The OCID of the user who created the job.
description string: (Updatable) A short description of the job.
displayName string: (Updatable) A user-friendly display name for the job.
failedDocuments number: Number of documents failed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
inputConfiguration JobInputConfiguration: input documents configuration by default TXT files will be processed and this behaviour will not change in future after adding new types
inputLocation JobInputLocation: document location and other meta data about documents For TXT only ObjectStoragePrefixLocation supported For CSV only ObjectStorageFileNameLocation is supported For this release only one file is supported for ObjectStorageFileNameLocation i.e CSV file type
lifecycleDetails string: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
modelMetadataDetails JobModelMetadataDetail[]: training model details For this release only one model is allowed to be input here. One of the three modelType, ModelId, endpointId should be given other wise error will be thrown from API
outputLocation JobOutputLocation: Object storage output location to write inference results
pendingDocuments number: Number of documents still to process. For CSV this signifies number of rows and for TXT this signifies number of files.
percentComplete number: How much progress the operation has made, vs the total amount of work that must be performed.
state string: The current state of the Job.
timeAccepted string: Job accepted time.
timeCompleted string: Job finished time.
timeStarted string: Job started time.
totalDocuments number: Total number of documents given as input for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
ttlInDays number: Time to live duration in days for Job. Job will be available till max 90 days.
warningsCount number: warnings count

compartment_id str: (Updatable) The OCID of the compartment where you want to create the job.
completed_documents int: Number of documents processed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
created_by str: The OCID of the user who created the job.
description str: (Updatable) A short description of the job.
display_name str: (Updatable) A user-friendly display name for the job.
failed_documents int: Number of documents failed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
input_configuration JobInputConfigurationArgs: input documents configuration by default TXT files will be processed and this behaviour will not change in future after adding new types
input_location JobInputLocationArgs: document location and other meta data about documents For TXT only ObjectStoragePrefixLocation supported For CSV only ObjectStorageFileNameLocation is supported For this release only one file is supported for ObjectStorageFileNameLocation i.e CSV file type
lifecycle_details str: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
model_metadata_details Sequence[JobModelMetadataDetailArgs]: training model details For this release only one model is allowed to be input here. One of the three modelType, ModelId, endpointId should be given other wise error will be thrown from API
output_location JobOutputLocationArgs: Object storage output location to write inference results
pending_documents int: Number of documents still to process. For CSV this signifies number of rows and for TXT this signifies number of files.
percent_complete int: How much progress the operation has made, vs the total amount of work that must be performed.
state str: The current state of the Job.
time_accepted str: Job accepted time.
time_completed str: Job finished time.
time_started str: Job started time.
total_documents int: Total number of documents given as input for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
ttl_in_days int: Time to live duration in days for Job. Job will be available till max 90 days.
warnings_count int: warnings count

compartmentId String: (Updatable) The OCID of the compartment where you want to create the job.
completedDocuments Number: Number of documents processed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
createdBy String: The OCID of the user who created the job.
description String: (Updatable) A short description of the job.
displayName String: (Updatable) A user-friendly display name for the job.
failedDocuments Number: Number of documents failed for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
inputConfiguration Property Map: input documents configuration by default TXT files will be processed and this behaviour will not change in future after adding new types
inputLocation Property Map: document location and other meta data about documents For TXT only ObjectStoragePrefixLocation supported For CSV only ObjectStorageFileNameLocation is supported For this release only one file is supported for ObjectStorageFileNameLocation i.e CSV file type
lifecycleDetails String: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
modelMetadataDetails List<Property Map>: training model details For this release only one model is allowed to be input here. One of the three modelType, ModelId, endpointId should be given other wise error will be thrown from API
outputLocation Property Map: Object storage output location to write inference results
pendingDocuments Number: Number of documents still to process. For CSV this signifies number of rows and for TXT this signifies number of files.
percentComplete Number: How much progress the operation has made, vs the total amount of work that must be performed.
state String: The current state of the Job.
timeAccepted String: Job accepted time.
timeCompleted String: Job finished time.
timeStarted String: Job started time.
totalDocuments Number: Total number of documents given as input for prediction. For CSV this signifies number of rows and for TXT this signifies number of files.
ttlInDays Number: Time to live duration in days for Job. Job will be available till max 90 days.
warningsCount Number: warnings count

Supporting Types

JobInputConfiguration, JobInputConfigurationArgs

Configuration Dictionary<string, ImmutableDictionary<string, string>>: meta data about documents For CSV valid JSON format is {"CSV" :{inputColumn: "reviewDetails", rowId: "reviewId", copyColumnsToOutput: ["reviewId" "userId"] , delimiter: ","} Note: In future if new file types added we will update here in documentation about input file meta data
DocumentTypes List<string>: Type of documents supported for this release only TXT,CSV and one element is allowed here. for future scope this is marked as list

Configuration map[string]map[string]string: meta data about documents For CSV valid JSON format is {"CSV" :{inputColumn: "reviewDetails", rowId: "reviewId", copyColumnsToOutput: ["reviewId" "userId"] , delimiter: ","} Note: In future if new file types added we will update here in documentation about input file meta data
DocumentTypes []string: Type of documents supported for this release only TXT,CSV and one element is allowed here. for future scope this is marked as list

configuration Map<String,Map<String,String>>: meta data about documents For CSV valid JSON format is {"CSV" :{inputColumn: "reviewDetails", rowId: "reviewId", copyColumnsToOutput: ["reviewId" "userId"] , delimiter: ","} Note: In future if new file types added we will update here in documentation about input file meta data
documentTypes List<String>: Type of documents supported for this release only TXT,CSV and one element is allowed here. for future scope this is marked as list

configuration {[key: string]: {[key: string]: string}}: meta data about documents For CSV valid JSON format is {"CSV" :{inputColumn: "reviewDetails", rowId: "reviewId", copyColumnsToOutput: ["reviewId" "userId"] , delimiter: ","} Note: In future if new file types added we will update here in documentation about input file meta data
documentTypes string[]: Type of documents supported for this release only TXT,CSV and one element is allowed here. for future scope this is marked as list

configuration Mapping[str, Mapping[str, str]]: meta data about documents For CSV valid JSON format is {"CSV" :{inputColumn: "reviewDetails", rowId: "reviewId", copyColumnsToOutput: ["reviewId" "userId"] , delimiter: ","} Note: In future if new file types added we will update here in documentation about input file meta data
document_types Sequence[str]: Type of documents supported for this release only TXT,CSV and one element is allowed here. for future scope this is marked as list

configuration Map<Map<String>>: meta data about documents For CSV valid JSON format is {"CSV" :{inputColumn: "reviewDetails", rowId: "reviewId", copyColumnsToOutput: ["reviewId" "userId"] , delimiter: ","} Note: In future if new file types added we will update here in documentation about input file meta data
documentTypes List<String>: Type of documents supported for this release only TXT,CSV and one element is allowed here. for future scope this is marked as list

JobInputLocation, JobInputLocationArgs

Bucket string: Object Storage bucket name.
LocationType string: locationType
Namespace string: Object Storage namespace name.
ObjectNames List<string>: List of objects to be processed

Bucket string: Object Storage bucket name.
LocationType string: locationType
Namespace string: Object Storage namespace name.
ObjectNames []string: List of objects to be processed

bucket String: Object Storage bucket name.
locationType String: locationType
namespace String: Object Storage namespace name.
objectNames List<String>: List of objects to be processed

bucket string: Object Storage bucket name.
locationType string: locationType
namespace string: Object Storage namespace name.
objectNames string[]: List of objects to be processed

bucket str: Object Storage bucket name.
location_type str: locationType
namespace str: Object Storage namespace name.
object_names Sequence[str]: List of objects to be processed

bucket String: Object Storage bucket name.
locationType String: locationType
namespace String: Object Storage namespace name.
objectNames List<String>: List of objects to be processed

JobModelMetadataDetail, JobModelMetadataDetailArgs

Configuration Dictionary<string, ImmutableDictionary<string, string>>

model configuration details For PII : < ENTITY_TYPE , ConfigurationDetails> ex."ORACLE":{ "mode" : "MASK","maskingCharacter" : "&","leaveCharactersUnmasked": 3,"isUnmaskedFromEnd" : true } For language translation : { "targetLanguageCodes" : ConfigurationDetails}

EndpointId string

Unique identifier endpoint OCID that should be used for inference

LanguageCode string

Language code supported

auto : Automatically detect language
ar : Arabic
pt-BR : Brazilian Portuguese
cs : Czech
da : Danish
nl : Dutch
en : English
fi : Finnish
fr : French
fr-CA : Canadian French
de : German
it : Italian
ja : Japanese
ko : Korean
no : Norwegian
pl : Polish
ro : Romanian
zh-CN : Simplified Chinese
es : Spanish
sv : Swedish
zh-TW : Traditional Chinese
tr : Turkish
el : Greek
he : Hebrew

ModelId string

Unique identifier model OCID that should be used for inference

ModelType string

model type to used for inference allowed values are

LANGUAGE_SENTIMENT_ANALYSIS
LANGUAGE_DETECTION
TEXT_CLASSIFICATION
NAMED_ENTITY_RECOGNITION
KEY_PHRASE_EXTRACTION
LANGUAGE_PII_ENTITIES
LANGUAGE_TRANSLATION

Configuration map[string]map[string]string

EndpointId string

Unique identifier endpoint OCID that should be used for inference

LanguageCode string

Language code supported

auto : Automatically detect language
ar : Arabic
pt-BR : Brazilian Portuguese
cs : Czech
da : Danish
nl : Dutch
en : English
fi : Finnish
fr : French
fr-CA : Canadian French
de : German
it : Italian
ja : Japanese
ko : Korean
no : Norwegian
pl : Polish
ro : Romanian
zh-CN : Simplified Chinese
es : Spanish
sv : Swedish
zh-TW : Traditional Chinese
tr : Turkish
el : Greek
he : Hebrew

ModelId string

Unique identifier model OCID that should be used for inference

ModelType string

model type to used for inference allowed values are

LANGUAGE_SENTIMENT_ANALYSIS
LANGUAGE_DETECTION
TEXT_CLASSIFICATION
NAMED_ENTITY_RECOGNITION
KEY_PHRASE_EXTRACTION
LANGUAGE_PII_ENTITIES
LANGUAGE_TRANSLATION

configuration Map<String,Map<String,String>>

endpointId String

Unique identifier endpoint OCID that should be used for inference

languageCode String

Language code supported

auto : Automatically detect language
ar : Arabic
pt-BR : Brazilian Portuguese
cs : Czech
da : Danish
nl : Dutch
en : English
fi : Finnish
fr : French
fr-CA : Canadian French
de : German
it : Italian
ja : Japanese
ko : Korean
no : Norwegian
pl : Polish
ro : Romanian
zh-CN : Simplified Chinese
es : Spanish
sv : Swedish
zh-TW : Traditional Chinese
tr : Turkish
el : Greek
he : Hebrew

modelId String

Unique identifier model OCID that should be used for inference

modelType String

model type to used for inference allowed values are

LANGUAGE_SENTIMENT_ANALYSIS
LANGUAGE_DETECTION
TEXT_CLASSIFICATION
NAMED_ENTITY_RECOGNITION
KEY_PHRASE_EXTRACTION
LANGUAGE_PII_ENTITIES
LANGUAGE_TRANSLATION

configuration {[key: string]: {[key: string]: string}}

endpointId string

Unique identifier endpoint OCID that should be used for inference

languageCode string

Language code supported

auto : Automatically detect language
ar : Arabic
pt-BR : Brazilian Portuguese
cs : Czech
da : Danish
nl : Dutch
en : English
fi : Finnish
fr : French
fr-CA : Canadian French
de : German
it : Italian
ja : Japanese
ko : Korean
no : Norwegian
pl : Polish
ro : Romanian
zh-CN : Simplified Chinese
es : Spanish
sv : Swedish
zh-TW : Traditional Chinese
tr : Turkish
el : Greek
he : Hebrew

modelId string

Unique identifier model OCID that should be used for inference

modelType string

model type to used for inference allowed values are

LANGUAGE_SENTIMENT_ANALYSIS
LANGUAGE_DETECTION
TEXT_CLASSIFICATION
NAMED_ENTITY_RECOGNITION
KEY_PHRASE_EXTRACTION
LANGUAGE_PII_ENTITIES
LANGUAGE_TRANSLATION

configuration Mapping[str, Mapping[str, str]]

endpoint_id str

Unique identifier endpoint OCID that should be used for inference

language_code str

Language code supported

auto : Automatically detect language
ar : Arabic
pt-BR : Brazilian Portuguese
cs : Czech
da : Danish
nl : Dutch
en : English
fi : Finnish
fr : French
fr-CA : Canadian French
de : German
it : Italian
ja : Japanese
ko : Korean
no : Norwegian
pl : Polish
ro : Romanian
zh-CN : Simplified Chinese
es : Spanish
sv : Swedish
zh-TW : Traditional Chinese
tr : Turkish
el : Greek
he : Hebrew

model_id str

Unique identifier model OCID that should be used for inference

model_type str

model type to used for inference allowed values are

LANGUAGE_SENTIMENT_ANALYSIS
LANGUAGE_DETECTION
TEXT_CLASSIFICATION
NAMED_ENTITY_RECOGNITION
KEY_PHRASE_EXTRACTION
LANGUAGE_PII_ENTITIES
LANGUAGE_TRANSLATION

configuration Map<Map<String>>

endpointId String

Unique identifier endpoint OCID that should be used for inference

languageCode String

Language code supported

auto : Automatically detect language
ar : Arabic
pt-BR : Brazilian Portuguese
cs : Czech
da : Danish
nl : Dutch
en : English
fi : Finnish
fr : French
fr-CA : Canadian French
de : German
it : Italian
ja : Japanese
ko : Korean
no : Norwegian
pl : Polish
ro : Romanian
zh-CN : Simplified Chinese
es : Spanish
sv : Swedish
zh-TW : Traditional Chinese
tr : Turkish
el : Greek
he : Hebrew

modelId String

Unique identifier model OCID that should be used for inference

modelType String

model type to used for inference allowed values are

LANGUAGE_SENTIMENT_ANALYSIS
LANGUAGE_DETECTION
TEXT_CLASSIFICATION
NAMED_ENTITY_RECOGNITION
KEY_PHRASE_EXTRACTION
LANGUAGE_PII_ENTITIES
LANGUAGE_TRANSLATION

JobOutputLocation, JobOutputLocationArgs

Bucket string

Object Storage bucket name.

Namespace string

Object Storage namespace name.

Prefix string

The prefix (directory) in an Object Storage bucket.

** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values

Bucket string

Object Storage bucket name.

Namespace string

Object Storage namespace name.

Prefix string

The prefix (directory) in an Object Storage bucket.

** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values

bucket String

Object Storage bucket name.

namespace String

Object Storage namespace name.

prefix String

The prefix (directory) in an Object Storage bucket.

** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values

bucket string

Object Storage bucket name.

namespace string

Object Storage namespace name.

prefix string

The prefix (directory) in an Object Storage bucket.

** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values

bucket str

Object Storage bucket name.

namespace str

Object Storage namespace name.

prefix str

The prefix (directory) in an Object Storage bucket.

** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values

bucket String

Object Storage bucket name.

namespace String

Object Storage namespace name.

prefix String

The prefix (directory) in an Object Storage bucket.

** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values

Import

Jobs can be imported using the id, e.g.

$ pulumi import oci:AiLanguage/job:Job test_job "id"

To learn more about importing existing cloud resources, see Importing resources.

Package Details

Repository: oci pulumi/pulumi-oci
License: Apache-2.0
Notes: This Pulumi package is based on the oci Terraform Provider.

Oracle Cloud Infrastructure v3.10.0 published on Wednesday, Nov 5, 2025 by Pulumi

pulumi/pulumi-oci

oci.AiLanguage.Job

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Example Usage

Create Job Resource

Constructor syntax

Parameters

Constructor example

Job Resource Properties

Inputs

Outputs

Look up Existing Job Resource

Supporting Types

JobInputConfiguration, JobInputConfigurationArgs

JobInputLocation, JobInputLocationArgs

JobModelMetadataDetail, JobModelMetadataDetailArgs

JobOutputLocation, JobOutputLocationArgs

Import

Package Details

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