Skip to main content

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.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

delos_llmax-0.11.31.tar.gz (18.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

delos_llmax-0.11.31-py3-none-any.whl (27.5 kB view details)

Uploaded Python 3

File details

Details for the file delos_llmax-0.11.31.tar.gz.

File metadata

  • Download URL: delos_llmax-0.11.31.tar.gz
  • Upload date:
  • Size: 18.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.7

File hashes

Hashes for delos_llmax-0.11.31.tar.gz
Algorithm Hash digest
SHA256 88127883de31edc2f90446dfd46214418143e69ceefde690e2959674c8c96df1
MD5 0af37f494230f5245d5b662bae83ac3e
BLAKE2b-256 0d4de5b5f1ec13933d74825db67c4996ab0e658eae704af7fd86f048f05bc0c5

See more details on using hashes here.

File details

Details for the file delos_llmax-0.11.31-py3-none-any.whl.

File metadata

  • Download URL: delos_llmax-0.11.31-py3-none-any.whl
  • Upload date:
  • Size: 27.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.7

File hashes

Hashes for delos_llmax-0.11.31-py3-none-any.whl
Algorithm Hash digest
SHA256 095471e27fd1e1ba3da85fa5a4d82d3a06f074bcd5a3e76afe18da17b4903216
MD5 272ffcab44eb946094e5001169750094
BLAKE2b-256 961807a6b65dbc105b38a6ad81a94244b7870abf50f74fbc872501a0c9751eb3

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page