Pipeline

The pipeline.run() method executes a pipeline you’ve created in Reducto Studio. Pipelines bundle parse, extract, split, and edit operations into a single reusable workflow. You configure the pipeline in Studio, deploy it to get a pipeline_id, then call it from your code.

Basic Usage

from pathlib import Path
from reducto import Reducto

client = Reducto()

# Upload document
upload = client.upload(file=Path("invoice.pdf"))

# Run a deployed pipeline
result = client.pipeline.run(
    input=upload.file_id,
    pipeline_id="your_pipeline_id"  # From Studio deployment
)

# Access results based on pipeline type
if result.result.extract:
    print(result.result.extract)
elif result.result.parse:
    for chunk in result.result.parse.result.chunks:
        print(chunk.content)

Method Signature

def pipeline.run(
    input: str | list[str],
    pipeline_id: str,
    settings: dict | None = None
) -> PipelineResponse

Parameters

Parameter	Type	Required	Description
`input`	`str \| list[str]`	Yes	File ID (`reducto://...`), URL, or list of file IDs for multi-document pipelines
`pipeline_id`	`str`	Yes	The pipeline ID from Studio deployment
`settings`	`dict \| None`	No	Settings that override pipeline defaults

Creating Pipelines

Pipelines are created and configured in Reducto Studio:

Go to Studio and create a new pipeline
Add and configure steps (Parse, Extract, Split, Edit)
Test with sample documents
Click Deploy and select Pipeline
Copy the generated pipeline_id

See Pipeline Basics for detailed instructions.

Response Structure

The response structure depends on what steps your pipeline includes:

result = client.pipeline.run(
    input=upload.file_id,
    pipeline_id="your_pipeline_id"
)

# Common fields
print(result.job_id)           # str: Job identifier
print(result.usage.num_pages)  # int: Pages processed
print(result.usage.credits)    # float: Credits used

# Results - populated based on pipeline type
print(result.result.parse)     # Parse results (if pipeline includes Parse)
print(result.result.extract)   # Extract results (if pipeline includes Extract)
print(result.result.split)     # Split results (if pipeline includes Split)
print(result.result.edit)      # Edit results (if pipeline includes Edit)

Parse Results

if result.result.parse:
    parse_result = result.result.parse
    for chunk in parse_result.result.chunks:
        print(chunk.content)

Extract Results

For Parse + Extract pipelines, extract is a single object:

if result.result.extract and not isinstance(result.result.extract, list):
    print(result.result.extract.result)

For Parse + Split + Extract pipelines, extract is a list (one per section):

if result.result.extract and isinstance(result.result.extract, list):
    for section in result.result.extract:
        print(f"{section.split_name}: {section.result}")

Multi-Document Pipelines

Pass multiple file IDs to process related documents together:

upload1 = client.upload(file=Path("doc1.pdf"))
upload2 = client.upload(file=Path("doc2.pdf"))

result = client.pipeline.run(
    input=[upload1.file_id, upload2.file_id],
    pipeline_id="your_pipeline_id"
)

Multi-document pipelines parse each document separately but run extraction across combined content.

Multi-document pipelines require a pipeline without Split. Split is not supported with multiple input documents.

Error Handling

from reducto import Reducto
import reducto

try:
    result = client.pipeline.run(
        input=upload.file_id,
        pipeline_id="your_pipeline_id"
    )
except reducto.APIConnectionError as e:
    print(f"Connection failed: {e}")
except reducto.APIStatusError as e:
    print(f"Pipeline failed: {e.status_code} - {e.response}")

Complete Example

from pathlib import Path
from reducto import Reducto

client = Reducto()

# Upload
upload = client.upload(file=Path("fidelity-example.pdf"))

# Run pipeline
result = client.pipeline.run(
    input=upload.file_id,
    pipeline_id="k9798h9mwt0wmq5qz5e45qxbfx7yj4bq"  # Example ID
)

print(f"Job ID: {result.job_id}")
print(f"Credits used: {result.usage.credits}")

# Handle different pipeline types
if result.result.extract:
    if isinstance(result.result.extract, list):
        # Split + Extract pipeline
        for section in result.result.extract:
            print(f"\n{section.split_name}:")
            print(section.result)
    else:
        # Parse + Extract pipeline
        print("Extracted data:", result.result.extract.result)
elif result.result.parse:
    # Parse-only pipeline
    for chunk in result.result.parse.result.chunks:
        print(chunk.content[:200])

Best Practices

Create Pipelines in Studio

Use Studio to configure and test pipelines before deploying to production.

Version Your Pipelines

Use version names when deploying to track configuration changes.

Next Steps

Learn how to create pipelines in Studio
Explore multi-document pipelines
Check out the async client for concurrent processing

Get Started

Core Methods

Async & Utilities

Basic Usage

Method Signature

Parameters

Creating Pipelines

Response Structure

Parse Results

Extract Results

Multi-Document Pipelines

Error Handling

Complete Example

Best Practices

Create Pipelines in Studio

Version Your Pipelines

Next Steps

​Basic Usage

​Method Signature

​Parameters

​Creating Pipelines

​Response Structure

​Parse Results

​Extract Results

​Multi-Document Pipelines

​Error Handling

​Complete Example

​Best Practices

Create Pipelines in Studio

Version Your Pipelines

​Next Steps

Basic Usage

Method Signature

Parameters

Creating Pipelines

Response Structure

Parse Results

Extract Results

Multi-Document Pipelines

Error Handling

Complete Example

Best Practices

Next Steps