limin 0.1.0

A Python library for interacting with OpenAI-compatible LLM APIs.

limin

A Python library for interacting with OpenAI-compatible LLM APIs.

Installation

Usage

General Usage Notes

Note that the entire library is asynchronous. If you want to use it in a script, you can use asyncio.run to run the main function.

Additionally, you will need to set the OPENAI_API_KEY environment variable to your API key (or pass the api_key parameter to the function you want to use).

For example, you can retrieve a text completion for a single user prompt by calling the generate_text_completion function:

from limin import generate_text_completion

completion = await generate_text_completion("What is the capital of France?")
print(completion.message)

If you want to use this in a script, you can do the following:

from limin import generate_text_completion


async def main():
    completion = await generate_text_completion("What is the capital of France?")
    print(completion.message)


if __name__ == "__main__":
    import asyncio
    import dotenv

    dotenv.load_dotenv()

    asyncio.run(main())

This will print something like:

The capital of France is Paris.

Generating a Single Text Completion

You can generate a single text completion for a user prompt by calling the generate_text_completion function:

from limin import generate_text_completion

completion = await generate_text_completion("What is the capital of France?")
print(completion.message)

You can generate a single text completion for a conversation by calling the generate_text_completion_for_conversation function:

from limin import generate_text_completion_for_conversation

completion = await generate_text_completion_for_conversation([
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is the capital of France?"},
    {"role": "assistant", "content": "The capital of France is Paris."},
    {"role": "user", "content": "What is the capital of Germany?"},
])
print(completion.message)

Generating Multiple Text Completions

You can generate multiple text completions for a list of user prompts by calling the generate_text_completions function:

from limin import generate_text_completions

completions = await generate_text_completions([
    "What is the capital of France?",
    "What is the capital of Germany?",
])

for completion in completions:
    print(completion.message)

It's important to note that the generate_text_completions function will parallelize the generation of the text completions. The number of parallel completions is controlled by the n_parallel parameter (which defaults to 5).

For example, if you want to generate 4 text completions with 2 parallel completions, you can do the following:

completions = await generate_text_completions([
    "What is the capital of France?",
    "What is the capital of Germany?",
    "What is the capital of Italy?",
    "What is the capital of Spain?",
], n_parallel=2)

for completion in completions:
    print(completion.message)

You can also generate multiple text completions for a list of conversations by calling the generate_text_completions_for_conversations function:

from limin import generate_text_completions_for_conversations

first_conversation = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is the capital of France?"},
]

second_conversation = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is the capital of Germany?"},
]

completions = await generate_text_completions_for_conversations([
    first_conversation,
    second_conversation,
], n_parallel=2)

for completion in completions:
    print(completion.message)

Note that both the generate_text_completions and generate_text_completions_for_conversations functions will show a progress bar if the show_progress parameter is set to True (which it is by default). You can suppress this by setting the show_progress parameter to False.

The TextCompletion Class

The generation functions return either a TextCompletion object or a list of TextCompletion objects. This has the following attributes:

• conversation: The conversation that was used to generate the completion.
• model: The model that was used to generate the completion.
• message: The message that was generated.
• start_time: The start time of the generation.
• end_time: The end time of the generation.
• duration: The duration of the generation took (in seconds).

The start_time, end_time, and duration attributes allow you to benchmark the performance of the generation.