Interface to handle multiple LLMs and AI tools.
Project description
llmax
Python package to manage most external and internal LLM APIs fluently.
Installation
To install, run the following command:
python3 -m pip install delos-llmax
How to use
You first have to define a list of Deployment
as such, where you need to specify the endpoints, key and deployment_name. Then create the client:
from llmax.clients import MultiAIClient
from llmax.models import Deployment, Model
deployments: dict[Model, Deployment] = {
"gpt-4o": Deployment(
model="gpt-4o",
provider="azure",
deployment_name="gpt-4o-2024-05-13",
api_key=os.getenv("LLMAX_AZURE_OPENAI_SWEDENCENTRAL_KEY", ""),
endpoint=os.getenv("LLMAX_AZURE_OPENAI_SWEDENCENTRAL_ENDPOINT", ""),
),
"whisper-1": Deployment(
model="whisper-1",
provider="azure",
deployment_name="whisper-1",
api_key=os.getenv("LLMAX_AZURE_OPENAI_SWEDENCENTRAL_KEY", ""),
endpoint=os.getenv("LLMAX_AZURE_OPENAI_SWEDENCENTRAL_ENDPOINT", ""),
api_version="2024-02-01",
),
}
client = MultiAIClient(
deployments=deployments,
)
Then you should define your input (that can be a text, image or audio, following the openai documentation for instance).
messages = [
{"role": "user", "content": "Raconte moi une blague."},
]
And finally get the response:
response = client.invoke_to_str(messages, model)
print(response)
Specificities
When creating the client, you can also specify two functions, increment_usage and get_usage. The first one is Callable[[float, Model], bool] while the second is Callable[[], float]. increment_usage is a function that is called after a call of the llm. The float is the price and Model, the model used. It can therefore be used to update your database. get_usage returns whether a condition is met. For instance, it can be a function that calls your database and returns whether the user is still active.
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