gen_ai_hub.batch_service.models.response module

Response models for the LLM Batch Service API.

class gen_ai_hub.batch_service.models.response.BatchStatus(*values)

Bases: str, Enum

Enumeration of possible lifecycle states for a batch job.

Variables:

  • PENDING – Job has been accepted and is waiting to be scheduled.

  • RUNNING – Job is actively being processed.

  • COMPLETED – Job finished successfully.

  • FAILED – Job terminated with an error.

  • CANCELLED – Job was cancelled by the user.

  • CANCELLING – Cancellation has been requested and is in progress.

PENDING = 'PENDING'
RUNNING = 'RUNNING'
COMPLETED = 'COMPLETED'
FAILED = 'FAILED'
CANCELLED = 'CANCELLED'
CANCELLING = 'CANCELLING'
class gen_ai_hub.batch_service.models.response.BatchCreateResponse(*, id: str, created_at: str | None = None, status: str | None = None, message: str | None = None, **extra_data: Any)

Bases: ResponseBaseModel

Response returned by POST /llm-batch-service/v1/batches.

Confirms that the batch job has been accepted and provides the assigned identifier and initial status.

Parameters:

  • id (str) – Unique identifier (UUID) of the created batch job.

  • created_at (str, optional) – ISO 8601 timestamp of when the job was created.

  • status (str, optional) – Initial status of the job, typically "PENDING".

  • message (str, optional) – Human-readable confirmation message from the service.

id: str
created_at: str | None
status: str | None
message: str | None
model_config: ClassVar[ConfigDict] = {'extra': 'allow', 'frozen': False}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.batch_service.models.response.BatchSummary(*, id: str, type: str | None = None, provider: str | None = None, created_at: str | None = None, status: str | None = None, **extra_data: Any)

Bases: ResponseBaseModel

Summary entry for a single batch job as returned in a list response.

Parameters:

  • id (str) – Unique identifier (UUID) of the batch job.

  • type (str, optional) – Batch processing type (e.g. "llm-native").

  • provider (str, optional) – LLM provider name (e.g. "azure-openai").

  • created_at (str, optional) – ISO 8601 timestamp of when the job was created.

  • status (str, optional) – Current status of the job.

id: str
type: str | None
provider: str | None
created_at: str | None
status: str | None
model_config: ClassVar[ConfigDict] = {'extra': 'allow', 'frozen': False}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.batch_service.models.response.BatchListResponse(*, count: int | None = None, resources: list[BatchSummary] | None = None, **extra_data: Any)

Bases: ResponseBaseModel

Response returned by GET /llm-batch-service/v1/batches.

Contains a count and a list of batch job summaries for the current resource group.

Parameters:

  • count (int, optional) – Total number of batch jobs.

  • resources (list[BatchSummary], optional) – List of batch job summaries.

count: int | None
resources: list[BatchSummary] | None
model_config: ClassVar[ConfigDict] = {'extra': 'allow', 'frozen': False}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.batch_service.models.response.BatchStatusDetail(*, current_status: str | None = None, target_status: str | None = None, updated_at: str | None = None, message: str | None = None, **extra_data: Any)

Bases: ResponseBaseModel

Status block embedded inside BatchDetailResponse.

Parameters:

  • current_status (str, optional) – The job’s current lifecycle status.

  • target_status (str, optional) – The terminal status the job is expected to reach.

  • updated_at (str, optional) – ISO 8601 timestamp of the last status change.

  • message (str, optional) – Optional human-readable description of the current status.

current_status: str | None
target_status: str | None
updated_at: str | None
message: str | None
model_config: ClassVar[ConfigDict] = {'extra': 'allow', 'frozen': False}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.batch_service.models.response.BatchInputDetail(*, uri: str | None = None, **extra_data: Any)

Bases: ResponseBaseModel

Input configuration as returned in a batch detail response.

Parameters:

uri (str, optional) – Object-store URI of the input .jsonl file.

uri: str | None
model_config: ClassVar[ConfigDict] = {'extra': 'allow', 'frozen': False}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.batch_service.models.response.BatchOutputDetail(*, uri: str | None = None, **extra_data: Any)

Bases: ResponseBaseModel

Output configuration as returned in a batch detail response.

Parameters:

uri (str, optional) – Object-store URI of the output directory.

uri: str | None
model_config: ClassVar[ConfigDict] = {'extra': 'allow', 'frozen': False}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.batch_service.models.response.BatchDetailResponse(*, id: str | None = None, type: str | None = None, provider: str | None = None, created_at: str | None = None, input: BatchInputDetail | None = None, output: BatchOutputDetail | None = None, spec: dict | None = None, status: BatchStatusDetail | None = None, **extra_data: Any)

Bases: ResponseBaseModel

Response returned by GET /llm-batch-service/v1/batches/{batch_id}.

Provides the full configuration and current status of a specific batch job.

Parameters:

  • id (str, optional) – Unique identifier (UUID) of the batch job.

  • type (str, optional) – Batch processing type (e.g. "llm-native").

  • provider (str, optional) – LLM provider name (e.g. "azure-openai").

  • created_at (str, optional) – ISO 8601 timestamp of when the job was created.

  • input (BatchInputDetail, optional) – Input file configuration.

  • output (BatchOutputDetail, optional) – Output directory configuration.

  • spec (dict, optional) – Raw job specification dict as stored by the service.

  • status (BatchStatusDetail, optional) – Current status details.

id: str | None
type: str | None
provider: str | None
created_at: str | None
input: BatchInputDetail | None
output: BatchOutputDetail | None
spec: dict | None
status: BatchStatusDetail | None
model_config: ClassVar[ConfigDict] = {'extra': 'allow', 'frozen': False}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.batch_service.models.response.BatchStatusResponse(*, current_status: str | None = None, target_status: str | None = None, updated_at: str | None = None, message: str | None = None, **extra_data: Any)

Bases: ResponseBaseModel

Response returned by GET /llm-batch-service/v1/batches/{batch_id}/status.

Parameters:

  • current_status (str, optional) – The job’s current lifecycle status.

  • target_status (str, optional) – The terminal status the job is expected to reach.

  • updated_at (str, optional) – ISO 8601 timestamp of the last status change.

  • message (str, optional) – Optional human-readable description of the current status.

current_status: str | None
target_status: str | None
updated_at: str | None
message: str | None
model_config: ClassVar[ConfigDict] = {'extra': 'allow', 'frozen': False}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.batch_service.models.response.BatchCancelResponse(*, id: str | None = None, created_at: str | None = None, message: str | None = None, **extra_data: Any)

Bases: ResponseBaseModel

Response returned by PATCH /llm-batch-service/v1/batches/{batch_id}/cancel.

Confirms that the cancellation request has been accepted. The job will transition to CANCELLING and eventually CANCELLED.

Parameters:

  • id (str, optional) – Unique identifier (UUID) of the batch job.

  • created_at (str, optional) – ISO 8601 timestamp of when the job was originally created.

  • message (str, optional) – Human-readable confirmation that cancellation was scheduled.

id: str | None
created_at: str | None
message: str | None
model_config: ClassVar[ConfigDict] = {'extra': 'allow', 'frozen': False}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.batch_service.models.response.BatchDeleteResponse(*, id: str | None = None, created_at: str | None = None, message: str | None = None, **extra_data: Any)

Bases: ResponseBaseModel

Response returned by DELETE /llm-batch-service/v1/batches/{batch_id}.

Confirms that the batch job record has been deleted. Only jobs in a terminal state (COMPLETED, FAILED, or CANCELLED) can be deleted.

Parameters:

  • id (str, optional) – Unique identifier (UUID) of the deleted batch job.

  • created_at (str, optional) – ISO 8601 timestamp of when the job was originally created.

  • message (str, optional) – Human-readable confirmation of the deletion.

id: str | None
created_at: str | None
message: str | None
model_config: ClassVar[ConfigDict] = {'extra': 'allow', 'frozen': False}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.batch_service.models.response.ErrorResponse(*, request_id: str, message: str, **extra_data: Any)

Bases: ResponseBaseModel

Error response body returned by the batch service on 4xx/5xx responses.

Parameters:

  • request_id (str) – Unique request identifier, useful for tracing the error in service logs.

  • message (str) – Human-readable description of the error.

request_id: str
message: str
model_config: ClassVar[ConfigDict] = {'extra': 'allow', 'frozen': False}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].