AI · 04 — Extract structured data
Free-text email in → clean fields out. The ◈-wired Extract node pulls orderId / customer / urgent from the message so downstream nodes get real columns, not prose. Swap the Inject text for any email.
What’s inside
4 nodes — every type links to its full reference page.
| Step | Node | Type |
|---|---|---|
| Inject | input.inject | |
| Extract fields | AI Extract | ai.extract |
| Ollama (local) | AI Model | ai.model |
| Fields | Console | utility.console |
Import it
- Studio → Import — paste the JSON below (or the URL
/docs/templates/ai-04-extract-structured-data.json). - Or ask the AI Copilot with the JSON pasted after the phrase:
import this workflow json into a new workflow named "AI · 04 — Extract structured data" - Any credentials the nodes need are
${credential.…}references — add them once in Admin → Credentials and the template picks them up. Nothing secret ships in a template.
The workflow document (flowdrome.workflow.v1, 4 node types)
{
"schemaVersion": "flowdrome.workflow.v1",
"id": "",
"name": "AI · 04 — Extract structured data",
"version": "0",
"status": "draft",
"nodes": [
{
"id": "seed",
"type": "input.inject",
"label": "Email",
"category": "trigger",
"config": {
"contentType": "application/json",
"payload": {
"text": "Hi, this is Ada Lovelace. Order 8412 arrived damaged — please rush a replacement, we need it for Friday's launch!"
}
},
"position": {
"x": 0,
"y": 0
}
},
{
"id": "pull",
"type": "ai.extract",
"label": "Extract fields",
"category": "ai",
"config": {
"provider": "ollama",
"baseUrl": "",
"apiKey": "",
"model": "qwen2.5:1.5b",
"systemPrompt": "",
"inputField": "text",
"attributes": [
{
"name": "orderId",
"description": "the order number",
"type": "string",
"required": "yes"
},
{
"name": "customer",
"description": "the customer's name",
"type": "string",
"required": "no"
},
{
"name": "urgent",
"description": "whether they need it urgently",
"type": "boolean",
"required": "no"
}
],
"timeoutMs": 120000
},
"position": {
"x": 530,
"y": 0
},
"attachments": {
"model": "llm"
}
},
{
"id": "llm",
"type": "ai.model",
"label": "Ollama (local)",
"category": "ai",
"config": {
"provider": "ollama",
"baseUrl": "",
"apiKey": "",
"model": "qwen2.5:1.5b",
"models": [],
"temperature": 0.2,
"maxTokens": 512
},
"position": {
"x": 530,
"y": 222
}
},
{
"id": "out",
"type": "utility.console",
"label": "Fields",
"category": "utility",
"config": {
"message": "order={{ $json.orderId }} customer={{ $json.customer }} urgent={{ $json.urgent }}"
},
"position": {
"x": 1060,
"y": 0
}
}
],
"edges": [
{
"id": "e0",
"sourceNodeId": "seed",
"sourcePort": "output",
"targetNodeId": "pull",
"targetPort": "input"
},
{
"id": "e1",
"sourceNodeId": "pull",
"sourcePort": "output",
"targetNodeId": "out",
"targetPort": "input"
}
],
"triggers": [
{
"id": "t0",
"nodeId": "seed"
}
],
"meta": {
"snippets": [],
"source": "template-gallery",
"description": "Free-text email in → clean fields out. The ◈-wired Extract node pulls orderId / customer / urgent from the message so downstream nodes get real columns, not prose. Swap the Inject text for any email."
}
} Generated from the verified demo corpus — this exact document seeds and runs on a fresh Flowdrome. Raw JSON: ai-04-extract-structured-data.json.