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jackmpcollinson Show HN: Magentic – Use LLMs as simple Python functions

1) The OpenAI API will be queried each time a "prompt-function" is called in python code. If you provide the `functions` argument in order to use function-calling then magentic will not execute the function the LLM has chosen, instead it returns a `FunctionCall` instance which you can validate before calling.

2) I haven't measured additional latency but it should be negligible in comparison to the speed of generation of the LLM. And since it makes it easy to use streaming and async functions you might be able to achieve much faster generation speeds overall - see the Async section in the README. Token usage should also be a negligible change from calling the OpenAI API directly - the only "prompting" magentic does currently is in naming the functions sent to OpenAI, all other input tokens are written by the user. A user switching from explicitly defining the output schema in the prompt to using function-calling via magentic might actually save a few tokens.

3) Functionality is not deterministic, even with `temperature=0`, but since we're working with python functions one option is to just add the `@cache` decorator. This would save you tokens and time when calling the same prompt-function with the same inputs.

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1) https://github.com/jackmpcollins/magentic#usage 2) https://github.com/jackmpcollins/magentic#asyncio 3) https://docs.python.org/3/library/functools.html#functools.c...

jackmpcollinson Show HN: Magentic – Use LLMs as simple Python functions

I found this was the most compact way to represent what I wanted to define, and makes it easy to keep the type hints for parameters. If you look inside `@prompt` it's creating a `PromptFunction` instance which I think would be a similar API to what you would end up with without using decorators https://github.com/jackmpcollins/magentic/blob/afdb22513385b...

jackmpcollinson Show HN: Magentic – Use LLMs as simple Python functions

Oh, and some companies offer APIs that match the OpenAI API and there are some open-source projects that do this for llama running locally. Since those would be compatible with the openai python package they will work with magentic too - though some of these do not support function calling.

See for example Anyscale Endpoints https://app.endpoints.anyscale.com/landing and https://github.com/AmineDiro/cria

jackmpcollinson Show HN: Magentic – Use LLMs as simple Python functions

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 make it visible to others.

jackmpcollinson Show HN: Magentic – Use LLMs as simple Python functions

Yes, similar ideas. Marvin [asks the LLM to mimic the python function](https://github.com/PrefectHQ/marvin/blob/f37ad5b15e2e77dd998...), whereas in magentic the function signature just represents the inputs/outputs to the prompt-template/LLM, so the LLM “doesn’t know” that it is pretending to be a python function - you specify all the prompts.