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# Philosophy
The philosophy behind this library is to provide a **lightweight** and **flexible** approach to leveraging language models (LLMs) to do **structured output without imposing unnecessary dependencies or abstractions.**
The instructor library embodies a philosophy of simplicity and flexibility in leveraging language models (LLMs). It offers a streamlined approach for structured output, avoiding unnecessary dependencies or complex abstractions.
The `instructor` library serves as a bridge from text-based language model interaction to Object-Oriented Programming, seamlessly integrating LLMs into the programming paradigms we're familiar with. By treating LLMs as callable functions that return typed objects, `instructor` demystifies their complexity, making them more accessible for everyday projects. This approach maintains the flexibility and power of Python, letting you write custom code without unnecessary constraints.
## The Bridge to Object-Oriented Programming
`instructor` acts as a bridge converting text-based LLM interactions into a familiar object-oriented format. Its integration with Pydantic provides type hints, runtime validation, and robust IDE support; love and supported by many in the Python ecosystem. By treating LLMs as callable functions returning typed objects, instructor makes language models backwards compatible with code, making them practical for everyday use while being complex enough for advanced applications.
## The zen of `instructor`
Maintain the flexibility and power of Python, without unnecessary constraints.
Begin with a function and a return type hint – simplicity is key. With my experience maintaining a large enterprize framework at my previous job over many years I've learned that the goal of a making a useful framework is minimizing regret, both for the author and hopefully for the user.
1. Define a Schema `#!python class StructuredData(BaseModel):`
2. Encapsulate all your LLM logic into a function `#!python def extract(a) -> StructuredData:`
3. Define typed computations against your data with `#!python def compute(data: StructuredData):`
2. Define validators and methods on your schema.
3. Encapsulate all your LLM logic into a function `#!python def extract(a) -> StructuredData:`
4. Define typed computations against your data with `#!python def compute(data: StructuredData):` or call methods on your schema `#!python data.compute()`
Please note that the library is designed to be adaptable and open-ended, allowing you to customize and extend its functionality based on your specific requirements.
It should be that simple.
If you have any further questions or ideas hit me up on [twitter](https://twitter.com/jxnlco)
## My Goals
The goal for the library, documentation, and blog, is to help you be a better python programmer and as a result a better AI engineer.
- The library is a result of my desire for simplicity.
- The library should help maintain simplicity in your codebase.
- I won't try to write prompts for you,
- I don't try to create indirections or abstractions that make it hard to debug in the future
Please note that the library is designed to be adaptable and open-ended, allowing you to customize and extend its functionality based on your specific requirements. If you have any further questions or ideas hit me up on [twitter](https://twitter.com/jxnlco)