Python bindings for the Muse API: production-ready intelligence primitives powered by state-of-the-art language models. By LightOn.
Create. Process. Understand. Learn.
Uplift your product with the natural language generation & understanding capabilities of Muse. State-of-the-art large language models in French, English, Italian, and Spanish—with more to come—are just an API call away. Our models can help you build conversational AI, copywriting tools, text classifiers, semantic search, and more.
🛣️ Accessing the Muse API public beta
The Muse API is currently in public beta. Learn more about Muse and sign up at muse.lighton.ai.
You can install this package from PyPi with:
pip install lightonmuseTo install from source:
git clone https://github.com/lightonai/lightonmuse.git
cd lightonmuse
pip install ./Once the package is installed, make sure to define an environment variable
MUSE_API_KEY to your API key, e.g. by adding the following line to your .bashrc
export MUSE_API_KEY="<your api key>"
Guides and documentation can be found at the API docs website.
Using lightonmuse is pretty simple, the interface matches the endpoints offered by the Muse API
from lightonmuse import Create
creator = Create("lyra-en")
print(creator("Wow, the Muse API is really amazing"))from lightonmuse import Select
selector = Select("orion-fr-v2")
print(selector("Quel nom est correct?", candidates=["pain au chocolat", "chocolatine"]))from lightonmuse import CalibratedSelect
selector = CalibratedSelect("orion-fr-v2")
selector.fit(
content_free_inputs='Voici une critique : "" \n',
candidates=["positive", "négative"],
conjunction="Cette critique est"
)
critique = 'Voici une critique : "Ce film est super pour s\'endormir"'
print(selector(critique, candidates=["positive", "négative"], conjunction="Cette critique est"))from lightonmuse import Analyse
analyser = Analyse("orion-fr-v2")
print(analyser("Avec \"Analyse\" on peut toujours trouver les parties plus surprenantes d'une phrase."))from lightonmuse import Embed
embedder = Embed("lyra-en")
print(embedder("This sentence will be transformed in a nice matrix of numbers."))from lightonmuse import Compare
comparer = Compare("lyra-en")
print(comparer("This is the reference.", candidates=["This is close to the reference", "While this is most definitely not"]))from lightonmuse import Tokenize
tokenizer = Tokenize("lyra-en")
print(tokenizer("Let's discover how many tokens is this text"))Access the public beta of LightOn MUSE and try our intelligence primitives at muse.lighton.ai
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