Extract → Split → Schema

Overview

The Extract → Split → Schema pipeline is the most powerful processing mode in Pulse. After extracting the document, it splits the pages into topic-based sections and then applies a different schema to each section. This is ideal for long, multi-section documents where different parts contain different kinds of data.

When to Use

Annual reports — Financials, Leadership, and Outlook each have different data to extract
Multi-section contracts — different clause types (indemnification, IP rights, payment terms) need different schemas
Research papers — Abstract, Methodology, Results, and Conclusion each have distinct structure
Insurance documents — policy details, claims history, and coverage schedules are all different
Regulatory filings — mixed sections like company overview, financial statements, risk factors

If your entire document uses one schema, use Extract → Schema instead — it’s simpler and faster.

How to Use in the Playground

Configure extraction settings

Set page range, figure extraction, chunking, and other options on the Configuration tab — same as Extract Only.

Define split topics

Switch to the Split step. Add topics with names and descriptions — Pulse uses these to assign pages to topics based on document content.

TopicDescriptionFinancialsRevenue, expenses, and profit dataLeadershipExecutive team and board of directorsOutlookFuture plans, projections, and guidance

Use the AI Helper to draft split topics from a prompt, the attached document, or existing split inputs.

The split step assigns whole pages to topics. A page belongs to the topic that best matches its content. Pages can only belong to one topic.

Define per-topic schemas

For each topic, define a JSON Schema tailored to the data you expect in that section. Each topic gets its own schema and optional prompt.

Example — Financials schema:

{
  "type": "object",
  "properties": {
    "total_revenue": { "type": "number", "description": "Total revenue for the fiscal year" },
    "net_income": { "type": "number", "description": "Net income after taxes" },
    "revenue_growth_pct": { "type": "number", "description": "Year-over-year revenue growth %" }
  },
  "required": ["total_revenue"]
}

Example — Leadership schema:

{
  "type": "object",
  "properties": {
    "ceo": { "type": "string", "description": "Name of the CEO" },
    "board_members": {
      "type": "array",
      "items": { "type": "string" },
      "description": "List of board member names"
    }
  }
}

For each topic, use descriptions and prompts that match only that section. The Schema AI Helper can draft the first version, but you should still review field names and required fields before saving the preset.

Upload and extract

Click Extract All. The pipeline chains all three steps automatically:

Extract — converts the document to markdown

Split — assigns pages to topics based on content

Schema — runs each topic’s schema against its assigned pages

Review results

Results appear organized by topic. Switch between topics to see:

Page assignments — which pages belong to each topic

Structured output — the JSON extracted for each topic

Citations — where in the document each value was found

What You Get Back

Everything from Extract, plus:

Field	Description
`split_output.splits`	Page assignments — `{ "Financials": [1,2,3], "Leadership": [4,5] }`
`split_id`	Saved split result ID
`results`	Per-topic structured output with `values` and `citations` for each topic
`schema_id`	Saved schema result ID

API Usage

Python
TypeScript
curl

from pulse import Pulse

client = Pulse(api_key="YOUR_API_KEY")

# Step 1: Extract
extract_result = client.extract(
    file=open("annual_report.pdf", "rb"),
    async_=True,
    storage={"enabled": True}
)
extraction_id = extract_result.extraction_id

# Step 2: Split
split_result = client.split(
    extraction_id=extraction_id,
    split_config={
        "split_input": [
            {"name": "Financials", "description": "Revenue, expenses, and profit data"},
            {"name": "Leadership", "description": "Executive team and board members"},
            {"name": "Outlook", "description": "Future plans and projections"}
        ]
    }
)
split_id = split_result.split_id
print(f"Split: {split_result.split_output}")

# Step 3: Schema per topic
schema_result = client.schema(
    split_id=split_id,
    split_schema_config={
        "Financials": {
            "schema": {
                "type": "object",
                "properties": {
                    "total_revenue": {"type": "number"},
                    "net_income": {"type": "number"}
                }
            },
            "schema_prompt": "Extract financial metrics"
        },
        "Leadership": {
            "schema": {
                "type": "object",
                "properties": {
                    "ceo": {"type": "string"},
                    "board_members": {"type": "array", "items": {"type": "string"}}
                }
            },
            "schema_prompt": "Extract leadership information"
        },
        "Outlook": {
            "schema": {
                "type": "object",
                "properties": {
                    "guidance": {"type": "string"},
                    "growth_target": {"type": "number"}
                }
            },
            "schema_prompt": "Extract forward-looking statements"
        }
    }
)

for topic, data in schema_result.results.items():
    print(f"{topic}: {data}")

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

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

// Step 1: Extract (async)
const extractResult = await client.extract({
    file: fs.createReadStream("annual_report.pdf"),
    async: true,
    storage: { enabled: true }
});
const extractionId = extractResult.extraction_id;

// Step 2: Split
const splitResult = await client.split({
    extraction_id: extractionId,
    split_config: {
        split_input: [
            { name: "Financials", description: "Revenue, expenses, and profit data" },
            { name: "Leadership", description: "Executive team and board members" },
            { name: "Outlook", description: "Future plans and projections" }
        ]
    }
});
const splitId = splitResult.split_id;

// Step 3: Schema per topic
const schemaResult = await client.schema({
    split_id: splitId,
    split_schema_config: {
        Financials: {
            schema: { type: "object", properties: { total_revenue: { type: "number" } } },
            schema_prompt: "Extract financial metrics"
        },
        Leadership: {
            schema: { type: "object", properties: { ceo: { type: "string" } } },
            schema_prompt: "Extract leadership information"
        },
        Outlook: {
            schema: { type: "object", properties: { guidance: { type: "string" } } },
            schema_prompt: "Extract forward-looking statements"
        }
    }
});

console.log(schemaResult.results);

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

# Step 2: Split
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": {
      "split_input": [
        {"name": "Financials", "description": "Revenue, expenses, and profit data"},
        {"name": "Leadership", "description": "Executive team and board members"},
        {"name": "Outlook", "description": "Future plans and projections"}
      ]
    }
  }'
# Save split_id from response

# Step 3: Schema per topic
curl -X POST https://api.runpulse.com/schema \
  -H "x-api-key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "split_id": "SPLIT_ID",
    "split_schema_config": {
      "Financials": {
        "schema": {"type": "object", "properties": {"total_revenue": {"type": "number"}}},
        "schema_prompt": "Extract financial metrics"
      },
      "Leadership": {
        "schema": {"type": "object", "properties": {"ceo": {"type": "string"}}},
        "schema_prompt": "Extract leadership information"
      }
    }
  }'

Skipping Schema (Extract → Split Only)

You don’t have to add schema after splitting. If you just want to know which pages belong to which topic — without structured extraction — you can stop after the split step. This is useful for document triage or routing.

# Extract → Split only (no schema)
split_result = client.split(
    extraction_id=extraction_id,
    split_config={
        "split_input": [
            {"name": "Relevant", "description": "Pages related to our query"},
            {"name": "Not Relevant", "description": "Background or boilerplate pages"}
        ]
    }
)

print(split_result.split_output)
# {"splits": {"Relevant": [1,3,5], "Not Relevant": [2,4,6]}}

Split API Reference

Full API documentation for the /split endpoint

Schema API Reference

Full API documentation for the /schema endpoint (single and split mode)

​Overview

​When to Use

​How to Use in the Playground

​What You Get Back

​API Usage

​Skipping Schema (Extract → Split Only)

​Related

Split API Reference

Schema API Reference

Overview

When to Use

How to Use in the Playground

What You Get Back

API Usage

Skipping Schema (Extract → Split Only)

Related