Request
curl -X POST https://api.iterationlayer.com/document-extraction/v1/extract \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"files": [
{
"type": "url",
"name": "paper.pdf",
"url": "https://example.com/papers/research-paper.pdf"
}
],
"schema": {
"fields": [
{
"name": "title",
"type": "TEXT",
"description": "Title of the academic paper"
},
{
"name": "authors",
"type": "ARRAY",
"description": "List of paper authors",
"fields": [
{
"name": "name",
"type": "TEXT",
"description": "Full name of the author"
}
]
},
{
"name": "abstract",
"type": "TEXTAREA",
"description": "Paper abstract"
},
{
"name": "published_date",
"type": "DATE",
"description": "Publication date of the paper"
},
{
"name": "keywords",
"type": "ARRAY",
"description": "Subject keywords or tags",
"fields": [
{
"name": "keyword",
"type": "TEXT",
"description": "A keyword or topic tag"
}
]
}
]
}
}'Response
{
"success": true,
"data": {
"title": {
"value": "Attention Is All You Need",
"confidence": 0.99,
"citations": ["Attention Is All You Need"]
},
"authors": {
"value": [
{
"name": {
"value": "Ashish Vaswani",
"confidence": 0.98,
"citations": ["Ashish Vaswani"]
}
},
{
"name": {
"value": "Noam Shazeer",
"confidence": 0.97,
"citations": ["Noam Shazeer"]
}
}
],
"confidence": 0.97,
"citations": []
},
"abstract": {
"value": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms.",
"confidence": 0.95,
"citations": [
"The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
]
},
"published_date": {
"value": "2017-06-12",
"confidence": 0.94,
"citations": ["12 Jun 2017"]
},
"keywords": {
"value": [
{
"keyword": {
"value": "transformer",
"confidence": 0.96,
"citations": ["Transformer"]
}
},
{
"keyword": {
"value": "attention mechanism",
"confidence": 0.95,
"citations": ["attention mechanisms"]
}
}
],
"confidence": 0.95,
"citations": []
}
}
}Request
import { IterationLayer } from "iterationlayer";
const client = new IterationLayer({ apiKey: "YOUR_API_KEY" });
const result = await client.extractDocument({
files: [
{
type: "url",
name: "paper.pdf",
url: "https://example.com/papers/research-paper.pdf",
},
],
schema: {
fields: [
{
name: "title",
type: "TEXT",
description: "Title of the academic paper",
},
{
name: "authors",
type: "ARRAY",
description: "List of paper authors",
fields: [
{
name: "name",
type: "TEXT",
description: "Full name of the author",
},
],
},
{
name: "abstract",
type: "TEXTAREA",
description: "Paper abstract",
},
{
name: "published_date",
type: "DATE",
description: "Publication date of the paper",
},
{
name: "keywords",
type: "ARRAY",
description: "Subject keywords or tags",
fields: [
{
name: "keyword",
type: "TEXT",
description: "A keyword or topic tag",
},
],
},
],
},
});Response
{
"success": true,
"data": {
"title": {
"value": "Attention Is All You Need",
"confidence": 0.99,
"citations": ["Attention Is All You Need"]
},
"authors": {
"value": [
{
"name": {
"value": "Ashish Vaswani",
"confidence": 0.98,
"citations": ["Ashish Vaswani"]
}
},
{
"name": {
"value": "Noam Shazeer",
"confidence": 0.97,
"citations": ["Noam Shazeer"]
}
}
],
"confidence": 0.97,
"citations": []
},
"abstract": {
"value": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms.",
"confidence": 0.95,
"citations": [
"The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
]
},
"published_date": {
"value": "2017-06-12",
"confidence": 0.94,
"citations": ["12 Jun 2017"]
},
"keywords": {
"value": [
{
"keyword": {
"value": "transformer",
"confidence": 0.96,
"citations": ["Transformer"]
}
},
{
"keyword": {
"value": "attention mechanism",
"confidence": 0.95,
"citations": ["attention mechanisms"]
}
}
],
"confidence": 0.95,
"citations": []
}
}
}Request
from iterationlayer import IterationLayer
client = IterationLayer(api_key="YOUR_API_KEY")
result = client.extract_document(
files=[
{
"type": "url",
"name": "paper.pdf",
"url": "https://example.com/papers/research-paper.pdf",
}
],
schema={
"fields": [
{
"name": "title",
"type": "TEXT",
"description": "Title of the academic paper",
},
{
"name": "authors",
"type": "ARRAY",
"description": "List of paper authors",
"fields": [
{
"name": "name",
"type": "TEXT",
"description": "Full name of the author",
},
],
},
{
"name": "abstract",
"type": "TEXTAREA",
"description": "Paper abstract",
},
{
"name": "published_date",
"type": "DATE",
"description": "Publication date of the paper",
},
{
"name": "keywords",
"type": "ARRAY",
"description": "Subject keywords or tags",
"fields": [
{
"name": "keyword",
"type": "TEXT",
"description": "A keyword or topic tag",
},
],
},
]
},
)Response
{
"success": true,
"data": {
"title": {
"value": "Attention Is All You Need",
"confidence": 0.99,
"citations": ["Attention Is All You Need"]
},
"authors": {
"value": [
{
"name": {
"value": "Ashish Vaswani",
"confidence": 0.98,
"citations": ["Ashish Vaswani"]
}
},
{
"name": {
"value": "Noam Shazeer",
"confidence": 0.97,
"citations": ["Noam Shazeer"]
}
}
],
"confidence": 0.97,
"citations": []
},
"abstract": {
"value": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms.",
"confidence": 0.95,
"citations": [
"The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
]
},
"published_date": {
"value": "2017-06-12",
"confidence": 0.94,
"citations": ["12 Jun 2017"]
},
"keywords": {
"value": [
{
"keyword": {
"value": "transformer",
"confidence": 0.96,
"citations": ["Transformer"]
}
},
{
"keyword": {
"value": "attention mechanism",
"confidence": 0.95,
"citations": ["attention mechanisms"]
}
}
],
"confidence": 0.95,
"citations": []
}
}
}Request
package main
import il "github.com/iterationlayer/sdk-go"
func main() {
client := il.NewClient("YOUR_API_KEY")
result, err := client.ExtractDocument(il.ExtractDocumentRequest{
Files: []il.FileInput{
il.FileInput{
Type: "url",
Name: "paper.pdf",
Url: "https://example.com/papers/research-paper.pdf",
},
},
Schema: il.ExtractionSchema{
Fields: []any{
il.TextFieldConfig{
Name: "title",
Type: "TEXT",
Description: "Title of the academic paper",
},
il.ArrayFieldConfig{
Name: "authors",
Type: "ARRAY",
Description: "List of paper authors",
Fields: []any{
il.TextFieldConfig{
Name: "name",
Type: "TEXT",
Description: "Full name of the author",
},
},
},
il.TextareaFieldConfig{
Name: "abstract",
Type: "TEXTAREA",
Description: "Paper abstract",
},
il.DateFieldConfig{
Name: "published_date",
Type: "DATE",
Description: "Publication date of the paper",
},
il.ArrayFieldConfig{
Name: "keywords",
Type: "ARRAY",
Description: "Subject keywords or tags",
Fields: []any{
il.TextFieldConfig{
Name: "keyword",
Type: "TEXT",
Description: "A keyword or topic tag",
},
},
},
},
},
})
if err != nil {
panic(err)
}
_ = result
}Response
{
"success": true,
"data": {
"title": {
"value": "Attention Is All You Need",
"confidence": 0.99,
"citations": ["Attention Is All You Need"]
},
"authors": {
"value": [
{
"name": {
"value": "Ashish Vaswani",
"confidence": 0.98,
"citations": ["Ashish Vaswani"]
}
},
{
"name": {
"value": "Noam Shazeer",
"confidence": 0.97,
"citations": ["Noam Shazeer"]
}
}
],
"confidence": 0.97,
"citations": []
},
"abstract": {
"value": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms.",
"confidence": 0.95,
"citations": [
"The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
]
},
"published_date": {
"value": "2017-06-12",
"confidence": 0.94,
"citations": ["12 Jun 2017"]
},
"keywords": {
"value": [
{
"keyword": {
"value": "transformer",
"confidence": 0.96,
"citations": ["Transformer"]
}
},
{
"keyword": {
"value": "attention mechanism",
"confidence": 0.95,
"citations": ["attention mechanisms"]
}
}
],
"confidence": 0.95,
"citations": []
}
}
}Template
{
"name": "Extract Academic Paper Metadata",
"nodes": [
{
"parameters": {
"content": "## Extract Academic Paper Metadata\n\nResearch teams and academic platforms use this recipe to extract metadata from a paper. Upload a PDF paper and receive structured JSON with title, authors, abstract, publication date, and keywords \u2014 ready for indexing, citation analysis, or a literature review tool.\n\n**Note:** This workflow uses the Iteration Layer community node (`n8n-nodes-iterationlayer`). Install it via Settings > Community Nodes on self-hosted n8n, or add it directly on n8n Cloud with Verified Community Nodes enabled.",
"height": 280,
"width": 500,
"color": 2
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
200,
40
],
"id": "2e5849ad-7140-4898-9adc-b818ae2d3de8",
"name": "Overview"
},
{
"parameters": {
"content": "### Step 1: Extract Data\nResource: **Document Extraction**\n\nConfigure the Document Extraction parameters below, then connect your credentials.",
"height": 160,
"width": 300,
"color": 6
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
475,
100
],
"id": "0895d89d-fcc2-409b-a989-35d4344b4b0d",
"name": "Step 1 Note"
},
{
"parameters": {},
"type": "n8n-nodes-base.manualTrigger",
"typeVersion": 1,
"position": [
250,
300
],
"id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
"name": "Manual Trigger"
},
{
"parameters": {
"resource": "documentExtraction",
"schemaInputMode": "rawJson",
"schemaJson": "{\"fields\":[{\"name\":\"title\",\"type\":\"TEXT\",\"description\":\"Title of the academic paper\"},{\"name\":\"authors\",\"type\":\"ARRAY\",\"description\":\"List of paper authors\",\"fields\":[{\"name\":\"name\",\"type\":\"TEXT\",\"description\":\"Full name of the author\"}]},{\"name\":\"abstract\",\"type\":\"TEXTAREA\",\"description\":\"Paper abstract\"},{\"name\":\"published_date\",\"type\":\"DATE\",\"description\":\"Publication date of the paper\"},{\"name\":\"keywords\",\"type\":\"ARRAY\",\"description\":\"Subject keywords or tags\",\"fields\":[{\"name\":\"keyword\",\"type\":\"TEXT\",\"description\":\"A keyword or topic tag\"}]}]}",
"files": {
"fileValues": [
{
"fileInputMode": "url",
"fileName": "paper.pdf",
"fileUrl": "https://example.com/papers/research-paper.pdf"
}
]
}
},
"type": "n8n-nodes-iterationlayer.iterationLayer",
"typeVersion": 1,
"position": [
500,
300
],
"id": "b2c3d4e5-f6a7-8901-bcde-f12345678901",
"name": "Extract Data",
"credentials": {
"iterationLayerApi": {
"id": "1",
"name": "Iteration Layer API"
}
}
}
],
"connections": {
"Manual Trigger": {
"main": [
[
{
"node": "Extract Data",
"type": "main",
"index": 0
}
]
]
}
},
"settings": {
"executionOrder": "v1"
}
}Prompt
Extract metadata from the academic paper at [file URL]. Use the extract_document tool with these fields:
- title (TEXT): Title of the academic paper
- authors (ARRAY): Each with name (TEXT)
- abstract (TEXTAREA): Paper abstract
- published_date (DATE): Publication date of the paper
- keywords (ARRAY): Each with keyword (TEXT)