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.
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.
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)-
Local file caching. Each of the functions is locally cached in request-response key-pairs to prevent excessive network activity.
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Auto-retry. Timeouts for rate limits, retry for internal errors (>=500 status code).
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Parameter names are in the functions so that you don't have to go looking at the docs constantly.
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Map reduce prompts onto data
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TODO: Cost estimates before running long tasks
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TODO: Describe task -> run task
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TODO: Allow easy estimate
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TODO: Allow easy logging