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-0.11.51.tar.gz (19.5 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-0.11.51-py3-none-any.whl (28.1 kB view details)

Uploaded Python 3

File details

Details for the file delos_llmax-0.11.51.tar.gz.

File metadata

  • Download URL: delos_llmax-0.11.51.tar.gz
  • Upload date:
  • Size: 19.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.7

File hashes

Hashes for delos_llmax-0.11.51.tar.gz
Algorithm Hash digest
SHA256 86bf1259ae8f17c8b355faa1606420a3fe02b3e8227bc69642ce789efb1b616c
MD5 7a0ddd597d13e44a96db051c5e78ca22
BLAKE2b-256 e07826c0ea310e1688f2205ef807ff66ed59588b54f8631ef099870bc115b21e

See more details on using hashes here.

File details

Details for the file delos_llmax-0.11.51-py3-none-any.whl.

File metadata

  • Download URL: delos_llmax-0.11.51-py3-none-any.whl
  • Upload date:
  • Size: 28.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.7

File hashes

Hashes for delos_llmax-0.11.51-py3-none-any.whl
Algorithm Hash digest
SHA256 e65d15e423139b61e47a77ce33c779cd61970912061c371d7f5b5b0646caf40c
MD5 b1aa0c7bfa77f05c48217d9e4c73723d
BLAKE2b-256 e926bfd09f04b2300b0b74770e4a66ffb27683df69e5d236d17a02821382050c

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