gen_ai_hub.batch_service package

class gen_ai_hub.batch_service.BatchCreateRequest(*, type: Literal['llm-native'], input: BatchInput, output: BatchOutput, spec: BatchSpec)

Bases: ABCBaseModel

Request body sent to POST /llm-batch-service/v1/batches.

Describes a new batch processing job: where to read input from, where to write output, and which model to use.

Parameters:

• type (Literal["llm-native"]) – Batch processing type. Currently only "llm-native" is supported.

• input (BatchInput) – Input file configuration.

• output (BatchOutput) – Output directory configuration.

• spec (BatchSpec) – LLM provider and model specification.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'frozen': False}

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

type: Literal['llm-native']

input: BatchInput

output: BatchOutput

spec: BatchSpec

class gen_ai_hub.batch_service.BatchInput(*, uri: str)

Bases: ABCBaseModel

Input configuration for a batch job.

Points to the .jsonl file in an object store that contains the individual LLM requests to be processed.

Parameters:

uri (str) – Fully qualified object-store URI of the input file. Must point to a .jsonl file (e.g. ai://my-store/input/requests.jsonl).

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'frozen': False}

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

uri: str

class gen_ai_hub.batch_service.BatchOutput(*, uri: str)

Bases: ABCBaseModel

Output configuration for a batch job.

Points to the directory in an object store where results will be written once the job completes.

Parameters:

uri (str) – Fully qualified object-store URI of the output directory (e.g. ai://my-store/output/).

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'frozen': False}

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

uri: str

class gen_ai_hub.batch_service.BatchSpec(*, provider: str, model: str)

Bases: ABCBaseModel

Specification of the LLM to use for a batch job.

Parameters:

• provider (str) – LLM provider name as registered in SAP AI Core (e.g. "azure-openai").

• model (str) – Model name to use for inference (e.g. "gpt-4.1-mini").

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'frozen': False}

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

provider: str

model: str

class gen_ai_hub.batch_service.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.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.

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

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

id: str

created_at: str | None

status: str | None

message: str | None

class gen_ai_hub.batch_service.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.

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

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

id: str

type: str | None

provider: str | None

created_at: str | None

status: str | None

class gen_ai_hub.batch_service.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.

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

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

count: int | None

resources: list[BatchSummary] | None

class gen_ai_hub.batch_service.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.

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

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

current_status: str | None

target_status: str | None

updated_at: str | None

message: str | None

class gen_ai_hub.batch_service.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.

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

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

uri: str | None

class gen_ai_hub.batch_service.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.

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

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

uri: str | None

class gen_ai_hub.batch_service.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.

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

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

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

class gen_ai_hub.batch_service.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.

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

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

current_status: str | None

target_status: str | None

updated_at: str | None

message: str | None

class gen_ai_hub.batch_service.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.

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

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

id: str | None

created_at: str | None

message: str | None

class gen_ai_hub.batch_service.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.

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

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

id: str | None

created_at: str | None

message: str | None

class gen_ai_hub.batch_service.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.

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

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

request_id: str

message: str

class gen_ai_hub.batch_service.BatchService(api_url: str | None = None, proxy_client: GenAIHubProxyClient | None = None, resource_group: str | None = None, timeout: int | float | Timeout | None = None)

Bases: object

Client for the LLM Batch Service API.

Supports synchronous and asynchronous variants of all five operations: create, list, get, cancel, and delete batch jobs.

The AI-Resource-Group header is injected automatically from proxy_client.request_header on every request.

Parameters:

• api_url (str, Optional) – Base URL of the SAP AI Core API (e.g. https://api.ai.prod.eu-central-1.aws.ml.hana.ondemand.com/v2). Defaults to the URL resolved from proxy_client.

• proxy_client (GenAIHubProxyClient) – A GenAIHubProxyClient instance. Defaults to the result of get_proxy_client(proxy_version="gen-ai-hub").

• resource_group (str, Optional) – Value for the AI-Resource-Group header. Falls back to the resource group on proxy_client when omitted.

• timeout (Union[int, float, httpx.Timeout], Optional) – Default HTTP request timeout passed to httpx.

__init__(api_url: str | None = None, proxy_client: GenAIHubProxyClient | None = None, resource_group: str | None = None, timeout: int | float | Timeout | None = None)

async acancel(batch_id: str, timeout: int | float | Timeout | None = None) → BatchCancelResponse

Async variant of cancel().

Parameters:

• batch_id (str) – UUID of the batch job.

• timeout (Union[int, float, httpx.Timeout], Optional) – Per-request timeout override.

Returns:

BatchCancelResponse confirming the cancellation request.

async aclose_http_connection() → None

Close the underlying asynchronous httpx client.

async acreate(*, type: str = 'llm-native', input_uri: str, output_uri: str, provider: str, model: str, timeout: int | float | Timeout | None = None) → BatchCreateResponse

Async variant of create().

Parameters:

• type (str) – Batch processing type (only "llm-native" is supported).

• input_uri (str) – URI of the input .jsonl file in the object store.

• output_uri (str) – URI of the output directory in the object store.

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

• model (str) – Model name (e.g. "gpt-4.1-mini").

• timeout (Union[int, float, httpx.Timeout], Optional) – Per-request timeout override.

Returns:

BatchCreateResponse with the job ID and initial status.

async adelete(batch_id: str, timeout: int | float | Timeout | None = None) → BatchDeleteResponse

Async variant of delete().

Parameters:

• batch_id (str) – UUID of the batch job.

• timeout (Union[int, float, httpx.Timeout], Optional) – Per-request timeout override.

Returns:

BatchDeleteResponse confirming the deletion.

async aget(batch_id: str, timeout: int | float | Timeout | None = None) → BatchDetailResponse

Async variant of get().

Parameters:

• batch_id (str) – UUID of the batch job.

• timeout (Union[int, float, httpx.Timeout], Optional) – Per-request timeout override.

Returns:

BatchDetailResponse with full job details.

async aget_status(batch_id: str, timeout: int | float | Timeout | None = None) → BatchStatusResponse

Async variant of get_status().

Parameters:

• batch_id (str) – UUID of the batch job.

• timeout (Union[int, float, httpx.Timeout], Optional) – Per-request timeout override.

Returns:

BatchStatusResponse with current and target status.

async alist(timeout: int | float | Timeout | None = None) → BatchListResponse

Async variant of list().

Parameters:

timeout (Union[int, float, httpx.Timeout], Optional) – Per-request timeout override.

Returns:

BatchListResponse containing the batch summaries.

cancel(batch_id: str, timeout: int | float | Timeout | None = None) → BatchCancelResponse

Schedule a batch job for cancellation.

Parameters:

• batch_id (str) – UUID of the batch job.

• timeout (Union[int, float, httpx.Timeout], Optional) – Per-request timeout override.

Returns:

BatchCancelResponse confirming the cancellation request.

close_http_connection() → None

Close the underlying synchronous httpx client.

create(*, type: str, input_uri: str, output_uri: str, provider: str, model: str, timeout: int | float | Timeout | None = None) → BatchCreateResponse

Create a new batch processing job.

Parameters:

• type (str) – Batch processing type (only "llm-native" is supported).

• input_uri (str) – URI of the input .jsonl file in the object store.

• output_uri (str) – URI of the output directory in the object store.

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

• model (str) – Model name (e.g. "gpt-4.1-mini").

• timeout (Union[int, float, httpx.Timeout], Optional) – Per-request timeout override.

Returns:

BatchCreateResponse with the job ID and initial status.

delete(batch_id: str, timeout: int | float | Timeout | None = None) → BatchDeleteResponse

Delete a batch job (only allowed for terminal states: COMPLETED, FAILED, CANCELLED).

Parameters:

• batch_id (str) – UUID of the batch job.

• timeout (Union[int, float, httpx.Timeout], Optional) – Per-request timeout override.

Returns:

BatchDeleteResponse confirming the deletion.

get(batch_id: str, timeout: int | float | Timeout | None = None) → BatchDetailResponse

Retrieve details of a specific batch job.

Parameters:

• batch_id (str) – UUID of the batch job.

• timeout (Union[int, float, httpx.Timeout], Optional) – Per-request timeout override.

Returns:

BatchDetailResponse with full job details.

get_status(batch_id: str, timeout: int | float | Timeout | None = None) → BatchStatusResponse

Retrieve the current status of a batch job.

Parameters:

• batch_id (str) – UUID of the batch job.

• timeout (Union[int, float, httpx.Timeout], Optional) – Per-request timeout override.

Returns:

BatchStatusResponse with current and target status.

list(timeout: int | float | Timeout | None = None) → BatchListResponse

List all batch jobs for the current resource group.

Parameters:

timeout (Union[int, float, httpx.Timeout], Optional) – Per-request timeout override.

Returns:

BatchListResponse containing the batch summaries.

exception gen_ai_hub.batch_service.BatchServiceError(request_id: str, message: str, status_code: int, headers: Headers)

Bases: Exception

Raised when the batch service returns an error response.

Captures the request_id from the error payload for tracing.

__init__(request_id: str, message: str, status_code: int, headers: Headers)

Subpackages

• gen_ai_hub.batch_service.models package
  • ABCBaseModel
    • ABCBaseModel.model_config
    • ABCBaseModel.model_dump()
  • ResponseBaseModel
    • ResponseBaseModel.model_config
  • BatchCreateRequest
    • BatchCreateRequest.model_config
    • BatchCreateRequest.type
    • BatchCreateRequest.input
    • BatchCreateRequest.output
    • BatchCreateRequest.spec
  • BatchInput
    • BatchInput.model_config
    • BatchInput.uri
  • BatchOutput
    • BatchOutput.model_config
    • BatchOutput.uri
  • BatchSpec
    • BatchSpec.model_config
    • BatchSpec.provider
    • BatchSpec.model
  • BatchStatus
    • BatchStatus.PENDING
    • BatchStatus.RUNNING
    • BatchStatus.COMPLETED
    • BatchStatus.FAILED
    • BatchStatus.CANCELLED
    • BatchStatus.CANCELLING
  • BatchCreateResponse
    • BatchCreateResponse.model_config
    • BatchCreateResponse.id
    • BatchCreateResponse.created_at
    • BatchCreateResponse.status
    • BatchCreateResponse.message
  • BatchSummary
    • BatchSummary.model_config
    • BatchSummary.id
    • BatchSummary.type
    • BatchSummary.provider
    • BatchSummary.created_at
    • BatchSummary.status
  • BatchListResponse
    • BatchListResponse.model_config
    • BatchListResponse.count
    • BatchListResponse.resources
  • BatchStatusDetail
    • BatchStatusDetail.model_config
    • BatchStatusDetail.current_status
    • BatchStatusDetail.target_status
    • BatchStatusDetail.updated_at
    • BatchStatusDetail.message
  • BatchInputDetail
    • BatchInputDetail.model_config
    • BatchInputDetail.uri
  • BatchOutputDetail
    • BatchOutputDetail.model_config
    • BatchOutputDetail.uri
  • BatchDetailResponse
    • BatchDetailResponse.model_config
    • BatchDetailResponse.id
    • BatchDetailResponse.type
    • BatchDetailResponse.provider
    • BatchDetailResponse.created_at
    • BatchDetailResponse.input
    • BatchDetailResponse.output
    • BatchDetailResponse.spec
    • BatchDetailResponse.status
  • BatchStatusResponse
    • BatchStatusResponse.model_config
    • BatchStatusResponse.current_status
    • BatchStatusResponse.target_status
    • BatchStatusResponse.updated_at
    • BatchStatusResponse.message
  • BatchCancelResponse
    • BatchCancelResponse.model_config
    • BatchCancelResponse.id
    • BatchCancelResponse.created_at
    • BatchCancelResponse.message
  • BatchDeleteResponse
    • BatchDeleteResponse.model_config
    • BatchDeleteResponse.id
    • BatchDeleteResponse.created_at
    • BatchDeleteResponse.message
  • ErrorResponse
    • ErrorResponse.model_config
    • ErrorResponse.request_id
    • ErrorResponse.message
  • Submodules
    • gen_ai_hub.batch_service.models.base module
      • ABCBaseModel
      • ResponseBaseModel
    • gen_ai_hub.batch_service.models.request module
      • BatchInput
      • BatchOutput
      • BatchSpec
      • BatchCreateRequest
    • gen_ai_hub.batch_service.models.response module
      • BatchStatus
      • BatchCreateResponse
      • BatchSummary
      • BatchListResponse
      • BatchStatusDetail
      • BatchInputDetail
      • BatchOutputDetail
      • BatchDetailResponse
      • BatchStatusResponse
      • BatchCancelResponse
      • BatchDeleteResponse
      • ErrorResponse

Submodules

• gen_ai_hub.batch_service.exceptions module
  • BatchServiceError
    • BatchServiceError.__init__()
• gen_ai_hub.batch_service.service module
  • BatchService
    • BatchService.__init__()
    • BatchService.create()
    • BatchService.list()
    • BatchService.get()
    • BatchService.get_status()
    • BatchService.cancel()
    • BatchService.delete()
    • BatchService.acreate()
    • BatchService.alist()
    • BatchService.aget()
    • BatchService.aget_status()
    • BatchService.acancel()
    • BatchService.adelete()
    • BatchService.close_http_connection()
    • BatchService.aclose_http_connection()