Information Extractor
Extract structured data from unstructured text using an LLM.
Written By pvdyck
Last updated 18 minutes ago
Information Extractor
The Information Extractor uses an LLM to pull structured fields from unstructured text such as emails, documents, support tickets, or web content.
How It Works
You define a JSON schema describing the fields you want to extract. The node sends the input text and schema to the LLM, which returns a structured JSON object matching your schema.
Parameters
Schema Example
{ "type": "object", "properties": { "name": { "type": "string", "description": "Person's full name" }, "email": { "type": "string", "description": "Email address" }, "orderNumber": { "type": "string", "description": "Order or reference number" } }, "required": ["name"]}Sub-Node Connections
Example
Input: "Hi, I'm Jane Doe (jane@example.com). My order #98765 hasn't arrived."
Output:
{ "name": "Jane Doe", "email": "jane@example.com", "orderNumber": "98765" }Tips
- Add
descriptionfields to your schema properties β they help the LLM understand what to extract. - Use
requiredto ensure critical fields are always returned. - For complex extractions, add a System Prompt with examples of expected output.
- Works well chained after an HTTP Request node to extract data from fetched web pages.