Data Extraction

Extract structured data from unstructured text using AI. The minded-extraction tool uses LLM capabilities to parse content and return data in a predefined format.

Overview

• Structured Extraction with Zod Schema: Define exact data structure

• Prompt-based Extraction: Extract information using custom prompts

• Validation and Retries: Automatic validation with configurable retry logic

• Structured Output Support: Uses LLM native structured output when available

Using in Flows

- id: extractCustomerInfo
  type: tool
  toolName: minded-extraction
  prompt: Extract customer name, email, and phone number from the message

Tool Parameters

Parameter	Type	Description	Required
content	string	Text to extract from	Yes
schema	object	Zod-compatible schema	No
systemPrompt	string	Custom instructions	No
examples	array	Input/output examples	No
strictMode	boolean	Enable validation (default: true)	No
maxRetries	number	Retry attempts on failure (default: 3)	No
defaultValue	any	Fallback value	No

Overriding Parameters in Flows

- name: 'Extract Customer Info'
  type: 'tool'
  toolName: 'minded-extraction'
  parameters:
    content: '{state.memory.rawText}'
    schema:
      name:
        type: 'string'
        description: 'Customer full name'
      email:
        type: 'string'
        description: 'Email address'
        required: false
      phone:
        type: 'string'
    systemPrompt: 'Extract contact information from the text'
    strictMode: true
    maxRetries: 3

Available schema field properties:

• type: 'string', 'number', 'boolean', 'array', or 'object'

• description: Optional field description

• required: Optional boolean (defaults to true)

Programmatic Usage

import { extract, createExtractor } from '@minded-ai/mindedjs';
import { z } from 'zod';

// Direct extraction
const result = await extract(
  content,
  {
    schema: z.object({
      name: z.string(),
      age: z.number(),
    }),
    systemPrompt: 'Extract person details',
  },
  agent.llm,
);

// Create reusable extractor
const extractor = createExtractor(schema, { systemPrompt: 'Extract data' });
const result = await extractor(content, agent.llm);

How It Works

1. With Structured Output Support: Uses LLM's withStructuredOutput for direct schema-compliant extraction

2. Fallback Mode: Generates prompt with schema description, parses JSON, validates against Zod schema, retries with error feedback

3. Non-strict Mode: Skips validation for flexible extraction