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-0.11.69.tar.gz (21.1 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-0.11.69-py3-none-any.whl (30.1 kB view details)

Uploaded Python 3

File details

Details for the file delos_llmax-0.11.69.tar.gz.

File metadata

  • Download URL: delos_llmax-0.11.69.tar.gz
  • Upload date:
  • Size: 21.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.7

File hashes

Hashes for delos_llmax-0.11.69.tar.gz
Algorithm Hash digest
SHA256 b7030f7e4693c477dca7f713325157adbc3c7a673dcac26f544ab787cd13b1b0
MD5 6e642cc71fe1880aa4a5609d622d37f8
BLAKE2b-256 f95a4d31c030fe6926073da89b2eda89a52e2eca9065eea2a24b755fa43ba9b5

See more details on using hashes here.

File details

Details for the file delos_llmax-0.11.69-py3-none-any.whl.

File metadata

  • Download URL: delos_llmax-0.11.69-py3-none-any.whl
  • Upload date:
  • Size: 30.1 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-0.11.69-py3-none-any.whl
Algorithm Hash digest
SHA256 de5e7fc002c5b37309d5feb1c9e917a917e61c149b72d2e159743ab67d8cb5af
MD5 2d25f5204a644989175fd9ba9170e378
BLAKE2b-256 480d62b00183edbb4e08071a49ae1523d27202e1629507ef062558c5f0e2cd93

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