Legacy Helper

Legacy helper components provide backward compatibility with older utility functions and deprecated helper tools while offering migration paths to modern alternatives.

Create List

This component dynamically creates a record with a specified number of fields.

Usage

Create List features:

• Dynamic list creation

• Configurable field count

• Text key specification

• Legacy compatibility

• Simple list operations

Inputs

Name	Display Name	Info
n_fields	Number of Fields	Number of fields to be added to the record.
text_key	Text Key	Key used as text.

Outputs

Name	Display Name	Info
list	List	The dynamically created list with the specified number of fields.

Output Parser

This component transforms the output of a language model into a specified format. It supports CSV format parsing, which converts LLM responses into comma-separated lists using Langchain's CommaSeparatedListOutputParser.

Note: This component only provides formatting instructions and parsing functionality. It does not include a prompt. You'll need to connect it to a separate Prompt component to create the actual prompt template for the LLM to use.

Both the Output Parser and Structured Output components format LLM responses, but they have different use cases. The Output Parser is simpler and focused on converting responses into comma-separated lists. Use this when you just need a list of items, for example ["item1", "item2", "item3"]. The Structured Output is more complex and flexible, and allows you to define custom schemas with multiple fields of different types. Use this when you need to extract structured data with specific fields and types.

Usage

To use this component:

1. Create a Prompt component and connect the Output Parser's format_instructions output to it. This ensures the LLM knows how to format its response.

2. Write your actual prompt text in the Prompt component, including the {format_instructions} variable. For example, in your Prompt component, the template might look like:

{format_instructions}
Please list three fruits.

1. Connect the output_parser output to your LLM model.

2. The output parser converts this into a Python list: ["apple", "banana", "orange"].

Inputs

Name	Display Name	Info
parser_type	Parser	Select the parser type. Currently supports "CSV".

Outputs

Name	Display Name	Info
format_instructions	Format Instructions	Pass to a prompt template to include formatting instructions for LLM responses.
output_parser	Output Parser	The constructed output parser that can be used to parse LLM responses.

Deprecation Notice

⚠️ Important: These components are part of the legacy helper collection and are no longer in active development. While they remain backward compatible and functional, we recommend migrating to modern alternatives:

Recommended Modern Alternatives

For List Creation

• Data Management: Advanced data structure creation

• Data Transformation: Modern data transformation utilities

• Custom Scripts: Use programming tools for complex list operations

For Output Parsing

• Structured Output: Advanced structured data extraction

• Text Processing: Modern text parsing capabilities

• Data Filtering: Advanced filtering and transformation

Migration Benefits

• Enhanced Functionality: Modern components offer more features

• Better Performance: Optimized for current infrastructure

• Active Support: Ongoing development and support

• Integration: Better integration with modern workflows

• Flexibility: More flexible and configurable options

Migration Guide

Step 1: Assess Current Usage

1. Inventory existing Create List usage

2. Document Output Parser implementations

3. Identify dependencies and connections

4. Plan migration timeline

Step 2: Choose Alternatives

1. Map legacy functionality to modern components

2. Review feature gaps and enhancements

3. Plan workflow updates

4. Test compatibility

Step 3: Gradual Migration

1. Implement parallel modern components

2. Test functionality and performance

3. Gradually migrate workflows

4. Monitor for issues

Step 4: Complete Transition

1. Remove legacy component dependencies

2. Update documentation

3. Train team on new components

4. Archive legacy configurations

Legacy Support Information

Current Support Level

• Maintenance Mode: Bug fixes for critical issues only

• Security Updates: Security patches as needed

• No New Features: No new functionality added

• Limited Documentation: Documentation updates on hold

Transition Timeline

• Deprecation Notice: Components marked as deprecated

• Migration Period: Time to migrate to alternatives

• End of Support: Date when support ends

• Removal: Date when components are removed

Best Practices for Legacy Usage

• Avoid New Implementations: Don't use in new workflows

• Plan Migration: Actively plan migration to modern alternatives

• Monitor Performance: Watch for performance issues

• Backup Configurations: Maintain backup configurations

• Test Thoroughly: Test any changes carefully

Technical Considerations

Compatibility Notes

• Version Compatibility: May not work with newest features

• Integration Limits: Limited integration with modern components

• Performance: May not be optimized for current infrastructure

• Security: May not have latest security features

Maintenance Requirements

• Regular Monitoring: Monitor for issues and errors

• Version Pinning: Pin to stable versions

• Backup Plans: Have backup and rollback plans

• Documentation: Maintain usage documentation

Usage Notes

• Legacy Status: Components are deprecated

• Migration Priority: High priority for migration planning

• Support Limitations: Limited support available

• Risk Management: Assess risks of continued usage

• Modern Alternatives: Evaluate and adopt modern alternatives

• Training: Train team on modern component usage