Models

Models are the foundation of intelligence in the aiXplain ecosystem. They encapsulate capabilities such as language understanding, translation, summarization, speech processing, and more. Models can be directly used or integrated as tools into agents.

Key Concepts:

• Models are callable assets that accept text, audio, or other modalities.

• They support multiple functions: generation, translation, classification, etc.

• Models can be run synchronously, asynchronously, or in streaming mode.

• Streaming responses yield results incrementally while status=IN_PROGRESS.

Listing Models

To explore all accessible models for your team:

from aixplain.factories import ModelFactory
models = ModelFactory.list(
    function=Function.TRANSLATION,
    source_languages=[Language.ENGLISH],
    target_languages=[Language.FRENCH]
)
for model in model_list:
  print(model.id, model.name, model.supplier)

Alternatively, browse models and other assets in the marketplace.

Parameters

Parameter	Type	Description
function	Function	Filter by model function (e.g., TEXT_GENERATION, TRANSLATION, SUMMARIZATION).
query	str	Optional keyword to search by name or description.
suppliers	Supplier or List[Supplier]	Filter by model supplier (e.g., OPENAI, HUGGINGFACE).
source_languages	Language or List[Language]	Accepted source/input language(s).
target_languages	Language or List[Language]	Output language(s), applicable to translation models.
is_finetunable	bool	Whether the model supports fine-tuning.
ownership	Tuple[OwnershipType, List[OwnershipType]]	Ownership filter: OWNER, SUBSCRIBED, PUBLIC.
sort_by	SortBy	Attribute to sort by (e.g., NAME, CREATED_AT).
sort_order	SortOrder	ASCENDING or DESCENDING.
page_number	int	Page number for paginated results.
page_size	int	Maximum number of results to return (default: 20).
model_ids	List[str]	Specific model IDs to retrieve. Cannot be used with other filters.
api_key	str	Optional API key override for authorization.

Running a Model

You can run a model by calling run() with the desired input:

model = ModelFactory.get("65c51c556eb563350f6e1bb1")
response = model.run("Latest news about AI agents")
print(response.data.output)

Streaming Output for LLMs

To receive tokens incrementally for LLMs:

response = model.run("Explain LLMs", stream=True)
for chunk in response:
    print(chunk)

Each chunk is a ModelResponse object with:

• status: IN_PROGRESS or COMPLETED

• data: Partial output (if available)

• details: Full content generated so far

You can also use a loop with next(response) until status is COMPLETE.

response = model.run("Explain how to change a light bulb", max_tokens=512, stream=True)

while response.status == ResponseStatus.IN_PROGRESS:
    content = next(response)
    print(content)