Background
The raw output of a Large Language Model (LLM) is usually free-form text, which makes programmatic processing difficult.The Instructor library is specifically designed to address this problem by simplifying the subsequent data processing process through structured output.
Core Solutions
- Defining Structures with Pydantic Models: First create a class that inherits from BaseModel, explicitly defining the fields and types of output you expect
- Integrated LLM client: add structured processing capabilities by wrapping the standard client via constructor.from_openai()
- Specify the response_model parameter: pass in your defined model class in the API call and let LLM return the data in that format
- automated verification: Instructor automatically verifies that the returned data conforms to the model definition, ensuring that the type is correct
workaround
- For complex nested structures, you can use Pydantic's nested modeling capabilities
- If some fields may be null, you can use the Optional type labeling
- For special data formats, you can utilize Pydantic's custom validators
Summary points
Using the Instructor library + Pydantic model approach not only solves the problem of confusing output, but also catches formatting errors at an early stage of the data, dramatically reducing the difficulty of subsequent processing.
This answer comes from the articleInstructor: a Python library to simplify structured output workflows for large language modelsThe
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