Principle:Langchain ai Langchain Structured Output Extraction

Knowledge Sources	LangChain OpenAI Structured Outputs
Domains	NLP, Data_Extraction, Structured_Output
Last Updated	2026-02-11 00:00 GMT

Overview

A technique that constrains LLM output to match a predefined schema, ensuring responses are always parseable into structured data types.

Description

Structured output extraction guarantees that the model's response conforms to a specified schema (Pydantic model or JSON Schema). This eliminates the need for fragile regex parsing and provides type-safe outputs. Three methods are available:

• Function calling: Uses the tool-calling mechanism with a single "extraction" tool
• JSON mode: Forces the model to output valid JSON (but without schema enforcement)
• JSON schema: Provider-native schema enforcement (OpenAI Structured Outputs)

Usage

Use structured output when you need the model to return data in a specific format (e.g., extracting entities, classification results, or configuration objects).

Theoretical Basis

Structured output uses constrained decoding or schema-guided generation:

# Abstract algorithm (not real code)
schema = {"name": str, "age": int, "skills": list[str]}
model_with_schema = model.with_structured_output(schema)
result = model_with_schema.invoke("Extract info about John, 30, knows Python and SQL")
# result = {"name": "John", "age": 30, "skills": ["Python", "SQL"]}

Related Pages

Implemented By

• Implementation:Langchain_ai_Langchain_BaseChatOpenAI_With_Structured_Output