How to call an asset

This guide will walk you through the process of calling your deployed models, pipelines, and agents using the aiXplain SDK.

Models

Models

The aiXplain SDK allows you to run models synchronously (Python) or asynchronously (Python and Swift). You can also process Data assets (Python).

Let's use Groq's Llama 70B as an example.

Python

from aixplain.factories import ModelFactory
from aixplain.enums import Supplier

model_list = ModelFactory.list(suppliers=Supplier.GROQ)["results"]
for model in model_list:
    print(model.__dict__)

Show output

model = ModelFactory.get("6626a3a8c8f1d089790cf5a2")

Swift

let provider = ModelProvider()
Task {
    let query = ModelQuery(query: "groq")
    let result = try? await provider.list(query)

    result?.forEach {
        print($0.id, $0.name)
    }
}

Show output

let model = ModelProvider().get("6626a3a8c8f1d089790cf5a2")

note

Model (and pipeline) inputs can be URLs, file paths, or direct text/labels (if applicable).
The examples below use only direct text.

Synchronous

Python

model.run("Tell me a joke about dogs.")

Show output

Use a dictionary to specify additional parameters or if the model takes multiple inputs.

model.run(
    {
        "text": "Tell me a joke about dogs.",
        "max_tokens": 10,
        "temperature": 0.5,
    }
)

Show output

Asynchronous

Python

start_response = model.run_async("Tell me a joke about dogs.")
start_response

Show output

Use the poll method to monitor inference progress.

while True:
  status = model.poll(start_response['url'])
  print(status)
  if status['status'] != 'IN_PROGRESS':
      break
  time.sleep(1)  # wait for 1 second before checking again

Show output

Swift

let result = model.run("Tell me a joke about dogs.")
print(result)

Show output

Use a dictionary to specify additional parameters or if the model takes multiple inputs.

let result = try await model.run({
    "text": "Tell me a joke about dogs.",
    "max_tokens": 10,
    "temperature": 0.5
})
print(result)

Show output

Process Data Assets

You can also perform inference on Data assets (Corpora or Datasets). You will need to onboard a data asset to use it.

note

Inference on Data assets is only available in Python.

Each data asset has an ID, and each column in that data asset has an ID, too. Specify both to perform inference:

Python

Run

result = model.run(
  data="64acbad666608858f693a3a0",
  data_asset="64acbad666608858f693a39f"
)

Run Async

start_response = model.run_async(
  data="64acbad666608858f693a3a0",
  data_asset="64acbad666608858f693a39f"
)

Pipelines

Pipelines

The aiXplain SDK allows you to run pipelines synchronously (Python) or asynchronously (Python, Swift). You can also choose between two running modes (Python) and process Data assets (Python).

note

Pipeline (and model) inputs can be URLs, file paths, or direct text/labels (if applicable).
The examples below use only direct text.

Synchronous

Python

result = pipeline.run("This is a sample text")

For multi-input pipelines, you can specify as input a dictionary where the keys are the label names of the input nodes (as seen in design) and values are their corresponding content:

result = pipeline.run({
    "Input 1": "This is a sample text to input node 1.",
    "Input 2": "This is a sample text to input node 2."
})
# or
result = pipeline.run(data = {
    "Input 1": "This is a sample text to input node 1.",
    "Input 2": "This is a sample text to input node 2."
})

Asynchronous

Python

start_response = pipeline.run_async({
  "Input 1": "This is a sample text to input node 1.",
  "Input 2": "This is a sample text to input node 2."
})

Use the poll method to monitor inference progress.

while True:
    status = pipeline.poll(start_response['url'])
    print(status)
    if status['status'] != 'IN_PROGRESS':
        break
        time.sleep(1) # wait for 1 second before checking again

Swift

let result = try await pipeline.run({
    "Input 1": "This is a sample text to input node 1.",
    "Input 2": "This is a sample text to input node 2."
})

Running Modes

You can run pipeline in 2 modes

• Service mode: For short/quick (seconds to few minutes) pipelines where pods are always on. The pipeline executes instantly without any warmup time in this case.
• Batch mode: For longer running pipelines (minutes to hours) where the pipeline gets dedicated resources. This adds to a warmup time before the pipeline execution.

You may control the mode by passing the argument as follows.

Python

result = pipeline.run(data={
    "Input 1": "This is a sample text to input node 1.",
    "Input 2": "This is a sample text to input node 2."
},
**{"batchmode": False})

Process Data Assets

You can also perform inference on aiXplain Data assets (Corpora or Datasets). You will need to onboard a data asset to use it.

note

Inference on Data assets is only available in Python.

Each data asset has an ID, and each column in that data asset has an ID, too. Specify both to perform inference:

Python

Run

result = pipeline.run(
  data="64acbad666608858f693a3a0",
  data_asset="64acbad666608858f693a39f"
)

Run Async

start_response = pipeline.run_async(
    data="64acbad666608858f693a3a0",
    data_asset="64acbad666608858f693a39f"
)

Agents

Agents

The aiXplain SDK allows you to run agents and gives you the option to use short-term memory (history).

Invoking Agents

Use the run method to call an agent.

Python

agent_response = agent.run(
"Analyse the following text: The quick brown fox jumped over the lazy dog. Answer in text and audio."
)

display(agent_response)

Show output

tip

You can use the regular expression module re to retrieve the URL by searching for the "https://" pattern in the output.

import re

def extract_url(text):
    url_pattern = r'https://[^\s]+'
    match = re.search(url_pattern, text)
    if match:
        return match.group(0)
    return None

tip

You can make a HTTP requst to retrieve your output, then use the IPython Audio and display to display it.

from IPython.display import Audio, display
import requests

def download_and_display_audio(url):
    response = requests.get(url, stream=True)
    if response.status_code == 200:
        file_name = "downloaded_audio.mp3"
        with open(file_name, 'wb') as f:
            f.write(response.content)

        display(Audio(file_name))
    else:
        print(f"Failed to download file: {response.status_code}")

output_text = agent_response['data']['output']
print("output_text:", output_text)

url = extract_url(output_text)
print("url:", url)

download_and_display_audio(url)

Show output

Swift

Call and run your agents using the code below.

let agent = try await AgentProvider().get("<AGENT ID>")

let response = try await agent.run(input: "Analyse the following text: The quick brown fox jumped over the lazy dog. Answer in text and audio.")

print(respose)

let output_text = response.data.output
print("output_text:", output_text)

Invoking Agents with Short-Term Memory

Every agent response includes a session_id value, which corresponds to a history.

To continue using a history, specify the session_id from the first query as an input parameter for all subsequent queries.

Python

session_id = agent_response["data"]["session_id"]
print(f"Session id: {session_id}")

Show output

agent_response = agent.run(
    "What makes this text interesting?",
    session_id=session_id,
)
display(agent_response)

Show output

Swift

let session_id = agent_response.data.sessionID

agent_response = agent.run(
    "What makes this text interesting?",
    sessionID=session_id,
)

print(agent_response)

With these steps, you can easily call models, pipelines, and integrate them into your agents to perform AI tasks.