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Jason
14 lines
1.2 KiB
Markdown
14 lines
1.2 KiB
Markdown
# Philosophy
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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.**
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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.
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1. Define a Schema `#!python class StructuredData(BaseModel):`
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2. Encapsulate all your LLM logic into a function `#!python def extract(a) -> StructuredData:`
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3. Define typed computations against your data with `#!python def compute(data: StructuredData):`
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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.
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If you have any further questions or ideas hit me up on [twitter](https://twitter.com/jxnlco)
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