Show HN: Fructose, LLM calls as strongly typed functions

• fructose

  3 675 9.1 Python
  

This approach may be too high-level "magic" to the point of being difficult to work with and iterate upon.

Looking at the prompt templates (https://github.com/bananaml/fructose/tree/main/src/fructose/... ), they use LangChain-esque "just try to make the output to be valid JSON" when APIs such as the GPT-4 turbo which this model uses by defauly now support function calling/structured data natively, and libraries such as outlines (https://github.com/outlines-dev/outlines) which is more complex but can better ensure a dictionary output for local LLMs

• outlines

  30 5,649 9.7 Python

  Structured Text Generation

  

This approach may be too high-level "magic" to the point of being difficult to work with and iterate upon.

Looking at the prompt templates (https://github.com/bananaml/fructose/tree/main/src/fructose/... ), they use LangChain-esque "just try to make the output to be valid JSON" when APIs such as the GPT-4 turbo which this model uses by defauly now support function calling/structured data natively, and libraries such as outlines (https://github.com/outlines-dev/outlines) which is more complex but can better ensure a dictionary output for local LLMs

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• guidance

  23 17,246 9.8 Jupyter Notebook

  A guidance language for controlling large language models.

  

Why do you have Guidance in caps?

https://github.com/guidance-ai/guidance

or ...

https://huggingface.co/docs/text-generation-inference/concep...

or ... ?

A quick glance through these, they don't seem yet to call json_object on OpenAI with the word JSON in the prompt, which works wonders with the 0125 models.

• instructor

  14 4,980 9.8 Python

  structured outputs for llms

Good stuff. How does this compare to Instructor? I’ve been using this extensively

https://jxnl.github.io/instructor/

• lmql

  30 3,320 9.5 Python

  A language for constraint-guided and efficient LLM programming.

  

• synquid

  3 113 0.0 Haskell

https://www.youtube.com/watch?v=HnOix9TFy1A

Links to more projects and papers by Prof. Polikarpova: https://cseweb.ucsd.edu/~npolikarpova/

I think this is one of the main ones she discusses in the talk: https://github.com/nadia-polikarpova/synquid

• dspy

  20 10,471 9.9 Python

  DSPy: The framework for programming—not prompting—foundation models

  

Have you done any comparison with DSPy ? (https://github.com/stanfordnlp/dspy)

Feels very similiar to DSPy except you dont have optimizations yet. But I like your API and the programming model your are enforcing through this.

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Suggest a related project