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-0.11.17.tar.gz (17.3 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-0.11.17-py3-none-any.whl (26.3 kB view details)

Uploaded Python 3

File details

Details for the file delos_llmax-0.11.17.tar.gz.

File metadata

  • Download URL: delos_llmax-0.11.17.tar.gz
  • Upload date:
  • Size: 17.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.7

File hashes

Hashes for delos_llmax-0.11.17.tar.gz
Algorithm Hash digest
SHA256 6c9bd305bd2a833cdb5104c8d480c12ccd0608ae3892dbe525b16bd2bf758030
MD5 0e5f742c3ee6815d00c8c249d642c79e
BLAKE2b-256 3091f65d100f9e9d3d6e8abcbd8c252b9aea4eab1b931fe91fbd5ae990771863

See more details on using hashes here.

File details

Details for the file delos_llmax-0.11.17-py3-none-any.whl.

File metadata

  • Download URL: delos_llmax-0.11.17-py3-none-any.whl
  • Upload date:
  • Size: 26.3 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-0.11.17-py3-none-any.whl
Algorithm Hash digest
SHA256 1eac75146c028e024c0acfd653bb5b52a0c5340b014d026d779e15717ea1bd37
MD5 8503f1bf6fb02cb4c6f7d1e5b0c15c55
BLAKE2b-256 fadcb2b6ade2e0ba28d2883b2a2e5e49f3b3a75f160d75ce8058859c04cd9c78

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