This repository demonstrates how to use LangChain's Output Parser classes to transform raw LLM responses into usable formats.
It explores four parser types:
- String Output Parser
- JSON Output Parser
- Structured Output Parser
- Pydantic Output Parser
LangChain Output Parsers help convert the often messy, unstructured text from LLMs into predictable formats.
By using an appropriate parser, you can:
- Enforce consistent output formats.
- Reduce parsing errors.
- Directly integrate results into your application logic.
Description:
Extracts the LLM’s response as plain text, without additional parsing.
Advantages:
- Simple and lightweight.
- Zero overhead — returns exactly what the LLM generates.
- No schema definition required.
Use Cases:
- When raw text output is acceptable.
- Creative or open-ended tasks (e.g., storytelling, brainstorming).
- Quick prototypes.
Description:
Parses the LLM output into a JSON object.
Useful when you want structured key-value pairs without strict typing.
Advantages:
- Easy to integrate with APIs and databases.
- Works well for dynamic and flexible schemas.
- Language-agnostic JSON format.
Use Cases:
- Applications exchanging data between different services.
- Situations where schema changes frequently.
- Quick structured data extraction.
Description:
Enforces a schema (via LangChain's schema tools) so that LLM output matches predefined fields and types.
Advantages:
- Predictable and consistent output format.
- Works well with nested and complex structures.
- Stronger validation than raw JSON parsing.
Use Cases:
- When output must match a known template.
- Complex workflows requiring consistent structure.
- Integration with downstream automation systems.
Description:
Parses the LLM output directly into a Pydantic model, with built-in validation.
Advantages:
- Strong runtime validation.
- Automatic type conversion.
- Detailed error messages on validation failure.
Use Cases:
- Production-grade APIs.
- Applications where data integrity is critical.
- Complex nested data structures.