Integrating Your Interface

Once your flow is ready for production, you can deploy it as an API. Just click on deploy to get a production-ready version of your model.

Get your RestAPI

To obtain your API just go to the deploy section of the Stack AI tool.

In this section, you will receive a code snippet to call your flow via a POST request in Python, JavaScript, and cURL.

import requests

API_URL = f"https://stack-inference.com/inference/v0/run/<YOUR_ORG_ID>/<YOUR_FLOW_ID>"
headers = {'Authorization':
 'Bearer YOUR_PUBLIC_KEY',
 'Content-Type': 'application/json'
}

def query(payload):
 response = requests.post(API_URL, headers=headers, json=payload)
 return response.json()

# you can add all your inputs here:
body = {'in-0': 'text for in-0', 'audio2text-0': 'base64 of audio to send',
'string-0': 'long string to send', 'url-0': 'url of website to load'}

output = query(body)

Some quick facts:

• This request receives all the inputs to the LLM as the body.

• This request returns the value of all the outputs to the LLM as a JSON.

• The API supports auto-scaling for a large volume of requests.

• Stack protects this API with the Token of your organization

Exposed Inputs

As part of your deployment, you can specify the following values as inputs in the request body:

• Input nodes in-.

• User ID for LLM Memory user_id

  • This value will create a database entry with the conversation history between each user and the LLM.

• String nodes string-.

• URL nodes url-.

• Inline document nodes indoc-.

• Image to Text img2txt-.

• Audio to Text Voice audio2text-.

If the values for these inputs are not specified, the flow will use the values from the flow.