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Jason Liu
36 lines
2.6 KiB
Markdown
36 lines
2.6 KiB
Markdown
# Philosophy
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The instructor values [simplicity](https://eugeneyan.com/writing/simplicity/) and flexibility in leveraging language models (LLMs). It offers a streamlined approach for structured output, avoiding unnecessary dependencies or complex abstractions. Let [Pydantic](https://docs.pydantic.dev/latest/) do the heavy lifting.
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> “Simplicity is a great virtue but it requires hard work to achieve it and education to appreciate it. And to make matters worse: complexity sells better.” — Edsger Dijkstra
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## The Bridge to Object-Oriented Programming
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`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](https://www.youtube.com/watch?v=yj-wSRJwrrc), making them practical for everyday use while being complex enough for advanced applications.
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## The zen of `instructor`
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Maintain the flexibility and power of Python, without unnecessary constraints.
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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.
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1. Define a Schema `#!python class StructuredData(BaseModel):`
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2. Define validators and methods on your schema.
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3. Encapsulate all your LLM logic into a function `#!python def extract(a) -> StructuredData:`
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4. Define typed computations against your data with `#!python def compute(data: StructuredData):` or call methods on your schema `#!python data.compute()`
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It should be that simple.
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## My Goals
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The goal for the library, [documentation](https://jxnl.github.io/instructor/), and [blog](https://jxnl.github.io/instructor/blog/), is to help you be a better python programmer and as a result a better AI engineer.
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- The library is a result of my desire for simplicity.
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- The library should help maintain simplicity in your codebase.
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- I won't try to write prompts for you,
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- I don't try to create indirections or abstractions that make it hard to debug in the future
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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)
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Cheers!
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