Love the examples too. Low-effort humor is the best:
> create_superhero("Garden Man")
> # Superhero(name='Garden Man', age=30, power='Control over plants', enemies=['Pollution Man', 'Concrete Woman'])
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Love the examples too. Low-effort humor is the best:
> create_superhero("Garden Man")
> # Superhero(name='Garden Man', age=30, power='Control over plants', enemies=['Pollution Man', 'Concrete Woman'])
This is also similar in spirit to LMQL https://github.com/eth-sri/lmql
Curious as to why you chose to do it as a decorator instead of just a function call?
Is it really LLMs (plural) when you only have OpenAPI integration?
You could have more flexibility by abstracting out the underlying LLM APIs, but then you also have a bigger deal with supported features of different APIs, the same conceptual feature supported with very different parameter structures, etc., etc.
Is it really LLMs (plural) when you only have OpenAPI integration?
Is it really LLMs (plural) when you only have OpenAPI integration?
Right now it just works with OpenAI chat models (gpt-3.5-turbo, gpt-4) but if there's interest I plan to extend it to have several backends. These would probably each be an existing library that implements generating structured output like https://github.com/outlines-dev/outlines or https://github.com/guidance-ai/guidance . If you have ideas how this should be done let me know - on a github issue would be great to ma…
See for example Anyscale Endpoints https://app.endpoints.anyscale.com/landing and https://github.com/AmineDiro/cria
Is it really LLMs (plural) when you only have OpenAPI integration?
Right now it just works with OpenAI chat models (gpt-3.5-turbo, gpt-4) but if there's interest I plan to extend it to have several backends. These would probably each be an existing library that implements generating structured output like https://github.com/outlines-dev/outlines or https://github.com/guidance-ai/guidance . If you have ideas how this should be done let me know - on a github issue would be great to ma…