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How to deploy

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.

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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/run_deployed_flow?flow_id={'YOUR FLOW_ID'}&org={'YOUR_ORG_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

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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.