Output Parsers
Output Parsers Overview
Output Parsers
Output Parsers Overview
Output Parsers are components that help structure and format the output from language models into specific data formats. They ensure that model responses conform to expected schemas, making them more reliable and easier to work with in applications.
Available Components
Advanced Structured Output Parser
Uses Zod schema to parse LLM output into predefined structures
CSV Output Parser
Parses LLM output into comma-separated list of values
Structured Output Parser
Parses LLM output into predefined JSON structures
Use Cases
Output parsers are valuable in various scenarios, including:
- Data extraction: When you need to extract specific pieces of information from LLM responses.
- Standardization: To ensure consistent output formats across different LLM calls or models.
- Validation: To verify that LLM outputs meet certain structural or content requirements.
- Integration: To facilitate easier integration of LLM outputs with other systems or databases that expect structured data.
- Workflow automation: To streamline the process of using LLM outputs in larger workflows or pipelines.
By using these output parsers, you can significantly improve the reliability and usability of LLM outputs in your Ardor projects, making it easier to build complex AI-powered applications and workflows.