Cognigy

Cognigy.AI Docs

COGNIGY.AI is the Conversational AI Platform focused on the needs of large enterprises to develop, deploy and run Conversational AI’s on any conversational channel.

Given the arising need of voice interfaces as the most natural way of communicating with brands, Cognigy was founded in 2016 by Sascha Poggemann and Phil Heltewig. Our mission: to enable all devices and applications to intelligently communicate with their users via naturally spoken or written dialogue.

Get Started

Within our COGNIGY.AI platform you're able to connect your Cognigy resources to your Rest client by using our Rest Endpoint integration.

Generic Endpoint Settings

Find out about the generic endpoint settings available with this endpoint on the following pages:

Connect your Application

After creating a REST Endpoint you are able to send POST requests to the Endpoint URL. The body of the requests should have the following format:

{
  "userId":"userId",
  "sessionId": "someUniqueId",
  "text":"message text",
  "data": {
    "key": "value"
  }
}

Parameters
userId - a user ID of the end user in form of a string
sessionId - a unique ID that is used to track the current conversation in form of a string
text - message text that should get processed by the assigned flow in form of a string
data - message data that should get processed by the assigned flow in form of an object

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Sending text and data

By default, you can use the REST Endpoint to send either text or data to your Flow. You can choose to send both, but at least one is required. If invalid text and invalid data is specified, then the REST Endpoint throws an error.

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Session ID

The sessionId is a unique identifier that is used to keep the state of a conversation. This means that you should generate a new unique ID whenever a new conversation starts, and not on every message. For testing purposes, you can use whatever string value you like as the sessionId, and change it whenever you want a new conversation to start.

The response contains the output text, output data and the outputStack, which is an array of all Flow outputs. Since the Rest Endpoint will concatenate all Flow Outputs (e.g. all Say Nodes) into one text / data output, you can use the outputStack for debugging purposes.

{
    "text": "output2",
    "data": {
        "output": 2
    },
    "outputStack": [
        {
            "text": "output 1",
            "data": {
                "output": 1
            }
        },
        {
            "text": "output 2",
            "data": {
                "output": 2
            }
        }
    ]
}

Updated about a month ago


REST


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