🔀Output Parsers

An output parser acts as a translator between LLMs and your application. It takes the raw, unformatted text generated by an LLM and transforms it into a more usable format suited for your needs.

1)CSV Output Parser

Parse the output of an LLM call as a comma-separated list of values.

Key Features:

• CSV Formatting: Converts responses into comma-separated values • Structured Output: Ensures consistent tabular data format • Autofix Option: Automatically fixes minor formatting issues

Setup Requirements:

1. Add CSV Output Parser node to the canvas

2. Enable Autofix if required

3. Connect the parser to a chat model output

Use Cases:

• Exporting data in CSV format • Tabular data generation

2)Custom List Output Parser

Parse the output of an LLM call as a list of values.

Key Features:

• Custom Formatting: Define list length and separator • Flexible Output: Supports different list structures • Autofix Option: Handles formatting inconsistencies

Setup Requirements:

1. Add Custom List Output Parser node to the canvas

2. Set Length (number of items)

3. Define Separator (example: comma, newline)

4. Enable Autofix if required

5. Connect to model output

Use Cases:

• Generating lists from responses • Structured text formatting

3)Structured Output Parser

Parse the output of an LLM call into a given (JSON) structure.

Key Features:

• Structured Data: Converts output into defined schema • Autofix Support: Fixes minor formatting issues • Consistent Responses: Ensures predictable output

Setup Requirements:

1. Add Structured Output Parser node to the canvas

2. Enable Autofix if required

3. Connect parser with model output

Use Cases:

• Structured response generation • API-ready outputs

4)Advanced Structured Output Parser

Parse the output of an LLM call into a given structure by providing a Zod schema.

Key Features:

• JSON Schema Support: Uses schema for structured output • High Accuracy: Ensures correct format strictly • Autofix Option: Handles minor formatting errors

Setup Requirements:

1. Add Advanced Structured Output Parser node to the canvas

2. Enable Autofix if required

3. Provide Example JSON schema

4. Connect parser to model output

Use Cases:

• JSON-based applications • Data validation and structured responses