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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

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Use Cases

Output parsers are valuable in various scenarios, including:
  1. Data extraction: When you need to extract specific pieces of information from LLM responses.
  2. Standardization: To ensure consistent output formats across different LLM calls or models.
  3. Validation: To verify that LLM outputs meet certain structural or content requirements.
  4. Integration: To facilitate easier integration of LLM outputs with other systems or databases that expect structured data.
  5. 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.