Pipelines

Overview

Pipelines chain processors (convert, extract, segment, custom) into reusable, versioned configurations. See Pipeline Overview for concepts.

Basic Usage

from datalab_sdk import DatalabClient, PipelineProcessor

client = DatalabClient()

# Create a pipeline
pipeline = client.create_pipeline(steps=[
    PipelineProcessor(type="convert", settings={"mode": "balanced"}),
    PipelineProcessor(type="extract", settings={
        "page_schema": {
            "type": "object",
            "properties": {
                "title": {"type": "string"},
                "date": {"type": "string"}
            }
        }
    })
])

# Save and publish
pipeline = client.save_pipeline(pipeline.pipeline_id, name="My Pipeline")
version = client.create_pipeline_version(pipeline.pipeline_id)

# Run
execution = client.run_pipeline(pipeline.pipeline_id, file_path="doc.pdf")
execution = client.get_pipeline_execution(execution.execution_id, max_polls=300)

# Get results
result = client.get_step_result(execution.execution_id, step_index=1)

Models

PipelineProcessor

Defines a single processor in a pipeline.

from datalab_sdk import PipelineProcessor

step = PipelineProcessor(
    type="extract",                      # Step type
    settings={"page_schema": {...}},     # Step-specific config
    custom_processor_id="cp_abc123",     # For custom steps
    eval_rubric_id=42,                   # Optional eval rubric
)

Field	Type	Required	Description
`type`	str	Yes	`"convert"`, `"extract"`, `"segment"`, or `"custom"`
`settings`	dict	Yes	Step-specific configuration
`custom_processor_id`	str	No	Custom processor ID for `"custom"` steps
`eval_rubric_id`	int	No	Evaluation rubric to apply

PipelineConfig

Returned by pipeline CRUD methods.

Field	Type	Description
`pipeline_id`	str	Unique ID (`pl_XXXXX`)
`steps`	list	Ordered list of step definitions
`name`	str	Pipeline name (set via `save_pipeline`)
`is_saved`	bool	Whether pipeline has been saved
`archived`	bool	Whether pipeline is archived
`active_version`	int	Current published version (`0` = no published version)
`created`	datetime	Creation timestamp
`updated`	datetime	Last update timestamp

PipelineVersion

Immutable snapshot of pipeline steps at a point in time.

Field	Type	Description
`version`	int	Version number
`steps`	list	Steps at this version
`description`	str	Version description
`created`	datetime	When version was published

PipelineExecution

Result from running a pipeline.

Field	Type	Description
`execution_id`	str	Unique ID (`pex_XXXXX`)
`pipeline_id`	str	Pipeline that was executed
`pipeline_version`	int	Version used (`0` = draft)
`status`	str	`pending`, `running`, `completed`, `completed_with_errors`, `failed`
`steps`	list	List of `PipelineExecutionStepResult`
`started_at`	datetime	Execution start time
`completed_at`	datetime	Execution end time
`created`	datetime	When execution was created
`config_snapshot`	dict	Frozen step configuration used
`input_config`	dict	Input file details
`rate_breakdown`	dict	Billing breakdown

PipelineExecutionStepResult

Status of a single step within an execution.

Field	Type	Description
`step_index`	int	Position in pipeline
`step_type`	str	Step type
`status`	str	`pending`, `dispatched`, `running`, `completed`, `failed`, `skipped`
`result_url`	str	URL to fetch step result
`checkpoint_id`	str	Checkpoint passed to downstream steps
`started_at`	datetime	Step start time
`finished_at`	datetime	Step end time
`error_message`	str	Error details if failed

Pipeline Management

Create

pipeline = client.create_pipeline(steps=[
    PipelineProcessor(type="convert", settings={"mode": "balanced"}),
    PipelineProcessor(type="extract", settings={"page_schema": {...}})
])

Save

pipeline = client.save_pipeline(pipeline.pipeline_id, name="Invoice Parser")

Update

Creates a draft if a published version exists:

pipeline = client.update_pipeline(pipeline.pipeline_id, steps=[
    PipelineProcessor(type="convert", settings={"mode": "accurate"}),
    PipelineProcessor(type="extract", settings={"page_schema": {...}})
])

List

result = client.list_pipelines(
    saved_only=True,           # Only saved pipelines (default)
    include_archived=False,    # Include archived (default: False)
    limit=50,
    offset=0
)

for p in result["pipelines"]:
    print(f"{p.pipeline_id}: {p.name}")

Get

pipeline = client.get_pipeline("pl_abc123")

Archive / Unarchive

client.archive_pipeline("pl_abc123")
client.unarchive_pipeline("pl_abc123")

Versioning

Publish a Version

version = client.create_pipeline_version(
    "pl_abc123",
    description="Added line items extraction"
)
print(f"Published v{version.version}")

List Versions

result = client.list_pipeline_versions("pl_abc123")
for v in result["versions"]:
    print(f"v{v.version}: {v.description}")

Discard Draft

# Revert to active published version
pipeline = client.discard_pipeline_draft("pl_abc123")

# Revert to a specific version
pipeline = client.discard_pipeline_draft("pl_abc123", version=1)

Get Rate

rate = client.get_pipeline_rate("pl_abc123")
print(f"{rate['rate_per_1000_pages_cents']} cents per 1000 pages")

Execution

Run

execution = client.run_pipeline(
    "pl_abc123",
    file_path="document.pdf",     # or file_url="https://..."
    page_range="0-10",
    output_format="json",
    skip_cache=False,
    run_evals=False,
    webhook_url="https://example.com/hook",
    version=2,                    # omit for active version
    max_polls=1,                  # polls after submission
    poll_interval=1,
)

Parameter	Type	Default	Description
`pipeline_id`	str	Required	Pipeline to run
`file_path`	str	-	Local file path
`file_url`	str	-	URL to document
`page_range`	str	-	Pages to process (`"0-5,10"`)
`output_format`	str	-	Override output format
`skip_cache`	bool	`False`	Skip cached results
`run_evals`	bool	`False`	Run eval rubrics on steps
`webhook_url`	str	-	Webhook URL for completion
`version`	int	-	Version to run (omit=active, 0=draft, N=specific)
`max_polls`	int	`1`	Polling attempts
`poll_interval`	int	`1`	Seconds between polls

Poll Execution

execution = client.get_pipeline_execution(
    "pex_abc123",
    max_polls=300,
    poll_interval=2
)

List Executions

result = client.list_pipeline_executions("pl_abc123", limit=20)
for ex in result["executions"]:
    print(f"{ex.execution_id}: {ex.status}")

Get Step Result

result = client.get_step_result("pex_abc123", step_index=1)

Async Usage

All pipeline methods are available on AsyncDatalabClient:

import asyncio
from datalab_sdk import AsyncDatalabClient, PipelineProcessor

async def run():
    async with AsyncDatalabClient() as client:
        pipeline = await client.create_pipeline(steps=[
            PipelineProcessor(type="convert", settings={"mode": "balanced"}),
            PipelineProcessor(type="extract", settings={"page_schema": {
                "type": "object",
                "properties": {"title": {"type": "string"}}
            }})
        ])

        pipeline = await client.save_pipeline(
            pipeline.pipeline_id, name="Async Pipeline"
        )

        execution = await client.run_pipeline(
            pipeline.pipeline_id, file_path="doc.pdf"
        )

        execution = await client.get_pipeline_execution(
            execution.execution_id, max_polls=300
        )

        result = await client.get_step_result(
            execution.execution_id, step_index=1
        )
        return result

result = asyncio.run(run())

Next Steps

Pipeline Overview

Concepts, processor types, and when to use pipelines.

Create a Pipeline

Step-by-step guide to building pipelines.

Pipeline Versioning

Manage drafts and publish versions.

Run a Pipeline

Execution, overrides, and result retrieval.

Welcome

On-prem

Platform

Overview

Basic Usage

Models

PipelineProcessor

PipelineConfig

PipelineVersion

PipelineExecution

PipelineExecutionStepResult

Pipeline Management

Create

Save

Update

List

Get

Archive / Unarchive

Versioning

Publish a Version

List Versions

Discard Draft

Get Rate

Execution

Run

Poll Execution

List Executions

Get Step Result

Async Usage

Next Steps

Pipeline Overview

Create a Pipeline

Pipeline Versioning

Run a Pipeline

​Overview

​Basic Usage

​Models

​PipelineProcessor

​PipelineConfig

​PipelineVersion

​PipelineExecution

​PipelineExecutionStepResult

​Pipeline Management

​Create

​Save

​Update

​List

​Get

​Archive / Unarchive

​Versioning

​Publish a Version

​List Versions

​Discard Draft

​Get Rate

​Execution

​Run

​Poll Execution

​List Executions

​Get Step Result

​Async Usage

​Next Steps

Pipeline Overview

Create a Pipeline

Pipeline Versioning

Run a Pipeline

Overview

Basic Usage

Models

PipelineProcessor

PipelineConfig

PipelineVersion

PipelineExecution

PipelineExecutionStepResult

Pipeline Management

Create

Save

Update

List

Get

Archive / Unarchive

Versioning

Publish a Version

List Versions

Discard Draft

Get Rate

Execution

Run

Poll Execution

List Executions

Get Step Result

Async Usage

Next Steps