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Asset listing

This guide will walk you through the process of searching for various aiXplain assets, including agents, models, pipelines, metrics, data, benchmark, corpora and files.

To explore all available assets, visit Discover in aiXplain Studio. There, you can browse, filter, sort, and compare assets side by side to find the best models, and agent tools for your needs.

Models

aiXplain's collection of models are searchable using queries, filters and directly accessible using thier IDs (unique identifiers).

Models are searchable on the SDK using the following search parameters:

  • function: Defines the AI function the model performs (e.g., translation, summarization). Can be an enum or None.
  • source_languages (Input language): Specifies the default input language(s) supported by the model. Accepts a list or None.
  • target_languages (Output language): Specifies the default output language(s) produced by the model. Accepts a list or None.
  • ownership (Owner): Indicates the Team/User that owns the model asset. Accepts a tuple containing ownership types.
  • is_finetunable (FineTune compatible): Boolean indicating whether the model supports fine-tuning.
  • suppliers: Identifies the provider hosting the model. Accepts a list or None.
  • model_ids: A list of specific model IDs to filter results. Can be None.

Let's use query, function, source_languages, target_languages and suppliers to search for translation models from English to Canadian French.

from aixplain.factories import ModelFactory
from aixplain.enums import Function, Language, Supplier

model_list = ModelFactory.list(
function=Function.TRANSLATION,
source_languages=Language.English,
target_languages=Language.French,
suppliers=[Supplier.AWS, Supplier.GOOGLE, Supplier.MICROSOFT],
)["results"]

for model in model_list:
print(model.__dict__)
Show output
tip

Use the _member_names_ attribute to see the list of available function types, languages and suppliers.

Function._member_names_
Show output
Language._member_names_
Show output
Supplier._member_names_
Show output

Direct Access

Once you know a model's ID, you can access the model directly (without searching for it).

EXAMPLE OpenAI's GPT-4 model has ID 6414bd3cd09663e9225130e8.

Instantiate a model object

from aixplain.factories import ModelFactory
model = ModelFactory.get('6414bd3cd09663e9225130e8')
model.__dict__
Show output

Once you have identified a suitable model, you can use it for inference, benchmarking, finetuning, or integrating it into custom AI pipelines.

Next, create or integrate these assets into AI agents to enhance your applications.