Resize, sharpen, and compress an image to fit email platform size limits in a single pipeline.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Extract headline, author, publication date, summary, category, and named entities from a public news article.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Media monitoring teams use this recipe to turn public article pages into structured metadata for research and alerts. Extract headline, author, date, summary, category, and named entities.
Website Extraction (1 credit per website extraction)
You need an Iteration Layer API key. Get one at platform.iterationlayer.com during the 7-day trial.
For full integration guidance (SDKs, auth, MCP, error handling), see the Iteration Layer Integration Guide.
curl -X POST https://api.iterationlayer.com/website-extraction/v1/extract \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"file": {
"type": "url",
"url": "https://example.com/news/market-update"
},
"schema": {
"fields": [
{
"name": "headline",
"type": "TEXT",
"description": "Article headline"
},
{
"name": "author",
"type": "TEXT",
"description": "Article author"
},
{
"name": "published_date",
"type": "DATE",
"description": "Publication date"
},
{
"name": "summary",
"type": "TEXTAREA",
"description": "Short article summary"
},
{
"name": "entities",
"type": "ARRAY",
"description": "Named entities mentioned in the article",
"fields": [
{
"name": "entity",
"type": "TEXT",
"description": "One named entity"
}
]
}
]
}
}'
import { IterationLayer } from "iterationlayer";
const client = new IterationLayer({ apiKey: "YOUR_API_KEY" });
const result = await client.extractWebsite({
file: {
type: "url",
url: "https://example.com/news/market-update",
},
schema: {
"fields": [
{
"name": "headline",
"type": "TEXT",
"description": "Article headline"
},
{
"name": "author",
"type": "TEXT",
"description": "Article author"
},
{
"name": "published_date",
"type": "DATE",
"description": "Publication date"
},
{
"name": "summary",
"type": "TEXTAREA",
"description": "Short article summary"
},
{
"name": "entities",
"type": "ARRAY",
"description": "Named entities mentioned in the article",
"fields": [
{
"name": "entity",
"type": "TEXT",
"description": "One named entity"
}
]
}
]
},
});
from iterationlayer import IterationLayer
client = IterationLayer(api_key="YOUR_API_KEY")
result = client.extract_website(
file={
"type": "url",
"url": "https://example.com/news/market-update",
},
schema={
"fields": [
{
"name": "headline",
"type": "TEXT",
"description": "Article headline"
},
{
"name": "author",
"type": "TEXT",
"description": "Article author"
},
{
"name": "published_date",
"type": "DATE",
"description": "Publication date"
},
{
"name": "summary",
"type": "TEXTAREA",
"description": "Short article summary"
},
{
"name": "entities",
"type": "ARRAY",
"description": "Named entities mentioned in the article",
"fields": [
{
"name": "entity",
"type": "TEXT",
"description": "One named entity"
}
]
}
]
},
)
package main
import il "github.com/iterationlayer/sdk-go"
func main() {
client := il.NewClient("YOUR_API_KEY")
result, err := client.ExtractWebsite(il.ExtractWebsiteRequest{
File: il.UrlFileInput{
Type: "url",
Url: "https://example.com/news/market-update",
},
Schema: il.ExtractionSchema{
Fields: []any{
il.TextFieldConfig{
Name: "headline",
Type: "TEXT",
Description: "Article headline",
},
il.TextFieldConfig{
Name: "author",
Type: "TEXT",
Description: "Article author",
},
il.DateFieldConfig{
Name: "published_date",
Type: "DATE",
Description: "Publication date",
},
il.TextareaFieldConfig{
Name: "summary",
Type: "TEXTAREA",
Description: "Short article summary",
},
il.ArrayFieldConfig{
Name: "entities",
Type: "ARRAY",
Description: "Named entities mentioned in the article",
Fields: []any{
il.TextFieldConfig{
Name: "entity",
Type: "TEXT",
Description: "One named entity",
},
},
},
},
},
})
if err != nil {
panic(err)
}
_ = result
}
{
"name": "Extract News Article Metadata from Website",
"nodes": [
{
"parameters": {
"content": "## Extract News Article Metadata from Website\n\nMedia monitoring teams use this recipe to turn public article pages into structured metadata for research and alerts. Extract headline, author, date, summary, category, and named entities.\n\n**Note:** This workflow uses the Iteration Layer community node (`n8n-nodes-iterationlayer`). Install it via Settings > Community Nodes before importing. Self-hosted n8n only.",
"height": 280,
"width": 500,
"color": 2
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
200,
40
],
"id": "5766ba18-e695-5f62-aaae-932013b93399",
"name": "Overview"
},
{
"parameters": {
"content": "### Step 1: Extract Website Data\nResource: **Website Extraction**\n\nConfigure the public website URL and schema below, then connect your credentials.",
"height": 160,
"width": 320,
"color": 6
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
475,
100
],
"id": "eda4ec81-04b9-5775-abfb-6c49de96d208",
"name": "Step 1 Note"
},
{
"parameters": {},
"type": "n8n-nodes-base.manualTrigger",
"typeVersion": 1,
"position": [
250,
300
],
"id": "5be9ccc8-fefc-5654-af78-bff133a24904",
"name": "Manual Trigger"
},
{
"parameters": {
"resource": "websiteExtraction",
"fileUrl": "https://example.com/news/market-update",
"schemaInputMode": "rawJson",
"schemaJson": "{\"fields\":[{\"name\":\"headline\",\"type\":\"TEXT\",\"description\":\"Article headline\"},{\"name\":\"author\",\"type\":\"TEXT\",\"description\":\"Article author\"},{\"name\":\"published_date\",\"type\":\"DATE\",\"description\":\"Publication date\"},{\"name\":\"summary\",\"type\":\"TEXTAREA\",\"description\":\"Short article summary\"},{\"name\":\"entities\",\"type\":\"ARRAY\",\"description\":\"Named entities mentioned in the article\",\"fields\":[{\"name\":\"entity\",\"type\":\"TEXT\",\"description\":\"One named entity\"}]}]}"
},
"type": "n8n-nodes-iterationlayer.iterationLayer",
"typeVersion": 1,
"position": [
520,
300
],
"id": "8e5eb38c-9e78-59a2-ac0d-8b7231907bf8",
"name": "Extract Website Data",
"credentials": {
"iterationLayerApi": {
"id": "1",
"name": "Iteration Layer API"
}
}
}
],
"connections": {
"Manual Trigger": {
"main": [
[
{
"node": "Extract Website Data",
"type": "main",
"index": 0
}
]
]
}
},
"settings": {
"executionOrder": "v1"
}
}
Extract structured data from the public website page at [website URL]. Use the extract_website tool with this schema:
- headline (TEXT): Article headline
- author (TEXT): Article author
- published_date (DATE): Publication date
- summary (TEXTAREA): Short article summary
- entities (ARRAY): Named entities mentioned in the article. Each item has entity (TEXT)
{
"success": true,
"data": {
"headline": {
"type": "TEXT",
"value": "European SaaS Spending Rises in Q1",
"confidence": 0.98,
"citations": [
"European SaaS Spending Rises in Q1"
],
"source": "website.html"
},
"author": {
"type": "TEXT",
"value": "Mara Klein",
"confidence": 0.92,
"citations": [
"By Mara Klein"
],
"source": "website.html"
},
"published_date": {
"type": "DATE",
"value": "2026-04-12",
"confidence": 0.9,
"citations": [
"April 12, 2026"
],
"source": "website.html"
}
}
}
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Resize, sharpen, and compress an image to fit email platform size limits in a single pipeline.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Compress an image to fit within a specific file size in bytes using quality-first compression.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Convert a contract PDF to clean markdown for clause extraction or LLM analysis.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Convert external documents — specs, contracts, reports — to markdown for knowledge base ingestion.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Convert a document to clean markdown suitable for chunking and embedding in a RAG pipeline.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Convert an image between PNG, JPEG, and WebP formats with quality control for web optimization.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.