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-0.11.8.tar.gz (14.4 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-0.11.8-py3-none-any.whl (19.9 kB view details)

Uploaded Python 3

File details

Details for the file delos_llmax-0.11.8.tar.gz.

File metadata

  • Download URL: delos_llmax-0.11.8.tar.gz
  • Upload date:
  • Size: 14.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.7

File hashes

Hashes for delos_llmax-0.11.8.tar.gz
Algorithm Hash digest
SHA256 322fdae91627dd48611ca698304c0965f1a0621bc4d54d742c3620673ef65426
MD5 c7474288c27996d1b7dcd97b18faa1ad
BLAKE2b-256 b6eaf60ae9b900404607bc063b202d64c2eaac6d0c3435745592dd2f6239b130

See more details on using hashes here.

File details

Details for the file delos_llmax-0.11.8-py3-none-any.whl.

File metadata

  • Download URL: delos_llmax-0.11.8-py3-none-any.whl
  • Upload date:
  • Size: 19.9 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-0.11.8-py3-none-any.whl
Algorithm Hash digest
SHA256 47ce2af22928e59c618f6c81c7077432cb9ecf97b2c9b3419c19d0f38782131a
MD5 e660ac2f900daec8a48b7be0a418788c
BLAKE2b-256 28b702f325bb8425e14fe2084ae22aec5ccdc1763625f82c671d1e644ef0bbb2

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