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Overview

Once you’ve configured extraction settings, split topics, or a schema in the Playground, you can save them as presets. Presets let you:
  • Reuse the same configuration across multiple documents
  • Share standardized settings with your team
  • Reference configurations by ID in API calls, keeping your code clean and your settings centralized
Each pipeline step has its own preset library:
StepConfig ID fieldWhat it stores
Extractextraction_config_idPage range, figures, chunking, effort, HTML settings
Splitsplit_config_idTopic names and descriptions
Schemaschema_config_idJSON Schema, schema prompt, effort mode
Pipelinepipeline_idAll step types + config ID references

Saving a Preset in the Playground

1
Configure your step
2
Set up your extraction settings, split topics, or schema as usual.
3
Click “Save”
4
Click the Save button in the step’s configuration panel.
5
Name your preset
6
Give it a descriptive name (e.g., “Invoice Extract — Standard”, “Annual Report Split Topics”) and optional description.
7
Confirm
8
The preset is saved to your organization’s library. The config ID is now available for API use.

Loading a Preset in the Playground

When starting a new extraction, click the Load Preset button (or select from the preset picker) to apply a saved configuration. All settings from the preset are loaded into the UI so you can use them as-is or tweak before running.

Saving a Full Pipeline Preset

You can also save an entire pipeline — including all step configurations — as a single preset. When loading a pipeline preset, all steps (extract, split, schema) are restored together. Pipeline presets store:
  • Step types — which steps are included (extract, split, schema)
  • Config ID references — pointers to each step’s saved config
  • For split-mode schemas, per-topic schema config IDs

Using Config IDs in the API

The real power of presets is using them in API calls. Instead of inlining your full configuration in every request, pass the config ID and the backend resolves it.

Extract with a Saved Config

from pulse_python_sdk import Pulse

client = Pulse(api_key="YOUR_API_KEY")

# Use a saved extraction config
result = client.extract(
    file=open("document.pdf", "rb"),
    extraction_config_id="abc123-your-saved-config-id",
    storage={"enabled": True}
)

print(f"Extraction ID: {result.extraction_id}")

Extract → Split → Schema with Saved Configs

from pulse_python_sdk import Pulse

client = Pulse(api_key="YOUR_API_KEY")

# Step 1: Extract using saved extraction config
result = client.extract(
    file=open("annual_report.pdf", "rb"),
    extraction_config_id="ext-config-abc123",
    async_=True,
    storage={"enabled": True}
)
extraction_id = result.extraction_id

# Step 2: Split using saved split config
split_result = client.split.document(
    extraction_id=extraction_id,
    split_config_id="split-config-def456"
)
split_id = split_result.split_id

# Step 3: Schema using saved schema config (single mode)
schema_result = client.schema.extract_schema(
    extraction_id=extraction_id,
    schema_config_id="schema-config-ghi789"
)

print(schema_result.schema_output)

Inline vs. Config ID

You can mix and match — use a config ID for some steps and inline config for others:
# Inline extract settings + saved split config + inline schema
result = client.extract(
    file=open("doc.pdf", "rb"),
    extract_figure=True,     # inline setting
    async_=True,
    storage={"enabled": True}
)

split_result = client.split.document(
    extraction_id=result.extraction_id,
    split_config_id="split-config-def456"   # saved config
)

schema_result = client.schema.extract_schema(
    split_id=split_result.split_id,
    split_schema_config={                    # inline config
        "Financials": {
            "schema": {"type": "object", "properties": {"revenue": {"type": "number"}}},
            "schema_prompt": "Extract financial data"
        }
    }
)
When using a config ID, do not also pass inline config for the same step — the config ID takes precedence and the inline values are ignored.

Benefits of Config IDs

BenefitDescription
ConsistencyEveryone on your team uses the same validated settings
VersioningUpdate a preset once and all future runs pick up the change
Cleaner codeAPI calls are shorter — no need to inline large schemas
PortabilityBuild and test in the Playground, then use the same config ID in production