Overview #
The Extract Information node is a specialized AI node designed to extract specific information from unstructured text using advanced AI models. It allows users to define custom extraction fields and receive structured data output.
Key Features #
- Custom field definition
- Support for both single values and lists
- Intelligent pattern recognition
- Flexible data type handling
- Format preservation
- Context-aware extraction
Node Configuration #
Basic Setup #
- Node Addition:
Workflow Builder → AI Actions → Extract Information
- Required Fields:
- Node Name (optional, for identification)
- Input Content (text to analyze)
- Extraction Fields (what to extract)
Field Configuration #
Field Properties #
{
"name": "field_name",
"description": "What to extract",
"type": "string|number|boolean|array",
"isList": true|false
}
Supported Data Types #
string
: Text contentnumber
: Numerical valuesboolean
: True/false valuesarray
: Lists of items
Usage Examples #
1. Basic Contact Information #
Field Configuration:
- Name: "email"
Description: "Extract email addresses"
Type: string
- Name: "phone"
Description: "Extract phone numbers"
Type: string
- Name: "addresses"
Description: "Extract physical addresses"
Type: array
IsList: true
2. Product Information #
Field Configuration:
- Name: "product_name"
Description: "Extract product name"
Type: string
- Name: "price"
Description: "Extract price in numbers"
Type: number
- Name: "features"
Description: "Extract product features"
Type: array
IsList: true
3. Document Analysis #
Field Configuration:
- Name: "dates"
Description: "Extract all dates mentioned"
Type: array
IsList: true
- Name: "names"
Description: "Extract person names"
Type: array
IsList: true
- Name: "key_points"
Description: "Extract main points"
Type: array
IsList: true
Working Process #
1. Input Processing #
graph TD
A[Input Text] --> B[Text Preprocessing]
B --> C[Context Analysis]
C --> D[Field Mapping]
D --> E[Extraction Process]
2. Extraction Flow #
- Text Analysis
- Content parsing
- Structure identification
- Pattern recognition
- Field Matching
- Pattern matching
- Context evaluation
- Type validation
- Data Extraction
- Value extraction
- Type conversion
- Format validation
- Output Formatting
- Data structuring
- Type enforcement
- List processing
Output Format #
Standard Output Structure #
{
"field_name1": "extracted_value",
"field_name2": 123,
"field_name3": ["item1", "item2", "item3"],
"field_name4": true
}
Sample Response #
{
"email": "john.doe@example.com",
"phone": "+1-555-123-4567",
"addresses": [
"123 Main St, City, State 12345",
"456 Side Ave, Town, State 67890"
]
}
Best Practices #
Field Definition #
- Clear Descriptions
- Be specific about what to extract
- Include format requirements
- Specify any constraints
- Appropriate Types
- Use correct data types
- Consider list vs single value
- Match expected format
- Naming Conventions
- Use descriptive names
- Maintain consistency
- Avoid special characters
Input Preparation #
- Text Formatting
- Clean input text
- Remove irrelevant content
- Maintain structure
- Content Organization
- Group related information
- Maintain context
- Preserve relationships
Error Handling #
Common Issues and Solutions #
Issue | Cause | Solution |
---|---|---|
No Data Extracted | Unclear description | Improve field description |
Wrong Data Type | Type mismatch | Verify field type configuration |
Missing Values | Content not found | Check input text coverage |
Invalid Format | Format mismatch | Specify format requirements |
Error Messages #
Error Types:
- FIELD_NOT_FOUND: Required field not found in text
- TYPE_MISMATCH: Extracted data doesn't match specified type
- FORMAT_ERROR: Data format validation failed
- EXTRACTION_FAILED: General extraction failure
Performance Optimization #
Best Practices #
- Input Optimization
- Limit text length
- Remove unnecessary content
- Maintain relevant context
- Field Configuration
- Limit number of fields
- Use specific descriptions
- Optimize field types
- Processing Efficiency
- Group similar extractions
- Use appropriate models
- Cache common patterns
Integration Examples #
1. Form Processing #
Workflow:
Form Submission → Extract Information → Database Storage
Fields:
- Personal Information
- Contact Details
- Requirements
2. Document Analysis #
Workflow:
Document Upload → Text Extraction → Extract Information → Report Generation
Fields:
- Key Terms
- Important Dates
- Action Items
3. Email Processing #
Workflow:
Email Receipt → Extract Information → CRM Update
Fields:
- Customer Details
- Order Information
- Support Requirements
Troubleshooting Guide #
Diagnostic Steps #
- Verify input text quality
- Check field configurations
- Validate data types
- Review extraction patterns
- Check model responses
Common Solutions #
- No Data Extracted
- Improve field descriptions
- Check input text
- Verify field names
- Wrong Data
- Review field types
- Check format specifications
- Validate input content
- Performance Issues
- Optimize input length
- Reduce field count
- Improve descriptions
Additional Resources #
Documentation #
- Field configuration guide
- Data type reference
- Pattern matching guide
- Best practices guide
Support #
- Community forums
- Technical support
- Usage examples
- FAQ section
Remember to regularly test your extraction configurations and validate the output to ensure accurate and reliable data extraction.