sllim 0.2.2

Fixing the openai api

Simple Large Language Inference Model

sllim serves as a quality of life wrapper around the openai-python library. I found myself writing and rewriting the same helper functions with each new project I began, so now I am working to put these functions together into a easy to use library.

Nothing here is ground-breaking; everything here is opinionated.

Usage

Use the chat function to connect with the ChatCompletion.create models. By default, it uses the gpt-3.5-turbo model, but you can pass a model param to use gpt-4

from sllim import chat

chat(
    [
        {
            "role": "system",
            "content": "Example system message",
        },
        {
            "role": "user",
            "content": "Example user message",
        }
    ]
)

complete works just like Completion.create, and embed is Embedding.create.

Map Reduce

from sllim import map_reduce

template = [
 {
    "role": "system",
    "content": "You are an excellent copy writer and you will rewrite my work into {adjective} words."
 },
 {
    "role": "user",
    "content": "Below is my writing, please improve it.\n\n{writing}"
 },
]

writings = [...] # long list of copywriting
for adjective in ["clearer", "more expressive", "fewer"]:
    # Since `writings` is a list, this is what we will reduce over.
    # The other variables are treated as constants through the reduction.
    gen = map_reduce(template, adjective=adjective, writing=writings, model="gpt-4")
    for idx, result in enumerate(gen):
        # This is a multithreaded generator to optimize latency to networked services
        original = writings[idx]
        print("Was:", original, "\nNow:", result)

Benefits

• Local file caching. Each of the functions is locally cached in request-response key-pairs to prevent excessive network activity.

• Auto-retry. Timeouts for rate limits, retry for internal errors (>=500 status code).

• Parameter names are in the functions so that you don't have to go looking at the docs constantly.

• Map reduce prompts onto data

• TODO: Cost estimates before running long tasks

• TODO: Describe task -> run task

• TODO: Allow easy estimate

• TODO: Allow easy logging