Step Preset Library

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:

Step	Config ID field	What it stores
Extract	`extraction_config_id`	Page range, figures, chunking, effort, HTML settings
Split	`split_config_id`	Topic names and descriptions
Schema	`schema_config_id`	JSON Schema, schema prompt, effort mode
Pipeline	`pipeline_id`	All step types + config ID references

Saving a Preset in the Playground

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. Each step type has its own config library, so Extract, Schema, and Split presets stay organized by what they control.

Extract
Schema
Split

Extraction Config Library with folders and saved extraction configs

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

Python
TypeScript
curl

from pulse 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}")

import { PulseClient } from "pulse-ts-sdk";
import fs from "fs";

const client = new PulseClient({
    apiKey: "YOUR_API_KEY"
});

// Use a saved extraction config
const result = await client.extract({
    file: fs.createReadStream("document.pdf"),
    extractionConfigId: "abc123-your-saved-config-id",
    storage: { enabled: true }
});

console.log("Extraction ID:", result.extraction_id);

curl -X POST https://api.runpulse.com/extract \
  -H "x-api-key: YOUR_API_KEY" \
  -F "file=@document.pdf" \
  -F "extraction_config_id=abc123-your-saved-config-id" \
  -F 'storage={"enabled": true}'

Extract → Split → Schema with Saved Configs

Python
TypeScript
curl

from pulse 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(
    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(
    extraction_id=extraction_id,
    schema_config_id="schema-config-ghi789"
)

print(schema_result.schema_output)

import { PulseClient } from "pulse-ts-sdk";
import fs from "fs";

const client = new PulseClient({
    apiKey: "YOUR_API_KEY"
});

// Step 1: Extract using saved extraction config (async)
const result = await client.extract({
    file: fs.createReadStream("annual_report.pdf"),
    extractionConfigId: "ext-config-abc123",
    async: true,
    storage: { enabled: true }
});
const extractionId = result.extraction_id;

// Step 2: Split using saved split config
const splitResult = await client.split({
    extraction_id: extractionId,
    split_config_id: "split-config-def456"
});

// Step 3: Schema using saved schema config (single mode)
const schemaResult = await client.schema({
    extraction_id: extractionId,
    schema_config_id: "schema-config-ghi789"
});

console.log(schemaResult.schema_output);

# Step 1: Extract using saved config
curl -X POST https://api.runpulse.com/extract \
  -H "x-api-key: YOUR_API_KEY" \
  -F "file=@annual_report.pdf" \
  -F "extraction_config_id=ext-config-abc123" \
  -F "async=true" \
  -F 'storage={"enabled": true}'
# Save extraction_id

# Step 2: Split using saved config
curl -X POST https://api.runpulse.com/split \
  -H "x-api-key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "extraction_id": "EXTRACTION_ID",
    "split_config_id": "split-config-def456"
  }'
# Save split_id

# Step 3: Schema using saved config
curl -X POST https://api.runpulse.com/schema \
  -H "x-api-key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "extraction_id": "EXTRACTION_ID",
    "schema_config_id": "schema-config-ghi789"
  }'

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"),
    async_=True,
    storage={"enabled": True}
)

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

schema_result = client.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

Benefit	Description
Consistency	Everyone on your team uses the same validated settings
Versioning	Update a preset once and all future runs pick up the change
Cleaner code	API calls are shorter — no need to inline large schemas
Portability	Build and test in the Playground, then use the same config ID in production

Pipeline Types

Overview of the three pipeline configurations

Moving to Production

Take your Playground pipeline into production code

Platform Reference

Pipelines

Forms

Production Workflow

Library & Exports

Step Preset Library

Overview

Saving a Preset in the Playground

Loading a Preset in the Playground

Saving a Full Pipeline Preset

Using Config IDs in the API

Extract with a Saved Config

Extract → Split → Schema with Saved Configs

Inline vs. Config ID

Benefits of Config IDs

Pipeline Types

Moving to Production

​Overview

​Saving a Preset in the Playground

​Loading a Preset in the Playground

​Saving a Full Pipeline Preset

​Using Config IDs in the API

​Extract with a Saved Config

​Extract → Split → Schema with Saved Configs

​Inline vs. Config ID

​Benefits of Config IDs

​Related

Pipeline Types

Moving to Production

Overview

Saving a Preset in the Playground

Loading a Preset in the Playground

Saving a Full Pipeline Preset

Using Config IDs in the API

Extract with a Saved Config

Extract → Split → Schema with Saved Configs

Inline vs. Config ID

Benefits of Config IDs

Related