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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.

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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)

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Method Signature

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

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Parameters

ParameterTypeRequiredDescription
inputstr | list[str]YesFile ID (reducto://...), URL, or list of file IDs for multi-document pipelines
pipeline_idstrYesThe pipeline ID from Studio deployment
settingsdict | NoneNoSettings that override pipeline defaults

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Creating Pipelines

Pipelines are created and configured in Reducto Studio:
  1. Go to Studio and create a new pipeline
  2. Add and configure steps (Parse, Extract, Split, Edit)
  3. Test with sample documents
  4. Click Deploy and select Pipeline
  5. Copy the generated pipeline_id
See Pipeline Basics for detailed instructions.

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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)

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Parse Results

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

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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}")

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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.

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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}")

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Complete Example

from pathlib import Path
from reducto import Reducto

client = Reducto()

# Upload
upload = client.upload(file=Path("financial-statement.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])

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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.

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Next Steps