Create a "Hello World" agent

Prerequisites: Up and Running instance of EDDI (see: Getting started)

Let's get started

Follow these steps to create the configuration files you will need:

1. Creating Output

See also Output Configuration.

You have guessed it correctly, another POST to /outputstore/outputsets creates the agent's Output with a JSON in the body like this:

{
  "outputSet": [
    {
      "action": "CONVERSATION_START",
      "timesOccurred": 0,
      "outputs": [
        {
          "valueAlternatives": [
            {
              "type": "text",
              "text": "Hello World!"
            }
          ]
        }
      ]
    }
  ]
}

You should again get a return code of 201 with a URI in the location header referencing the newly created output :

eddi://ai.labs.output/outputstore/outputsets/<UNIQUE_OUTPUTSET_ID>?version=<OUTPUTSET_VERSION>

Example :

eddi://ai.labs.output/outputstore/outputsets/5a26d97417312628b46119fc?version=1

4. Creating the Workflow

Now we will align the just created LifecycleTasks in the Workflow. Make a POST to /packagestore/packages with a JSON in the body like this:

{
  "packageExtensions": [
    {
      "type": "eddi://ai.labs.output",
      "config": {
        "uri": "eddi://ai.labs.output/outputstore/outputsets/<UNIQUE_OUTPUTSET_ID>?version=<OUTPUTSET_VERSION>"
      }
    }
  ]
}

Workflow parameters

Name	Description	Required
packageextensions	Array of WorkflowExtension
WorkflowExtension.type	possible values, see table below "Extension Types"
WorkflowExtension.extensions	Array of Object	False
WorkflowExtension.config	Config object, but can be empty.	True

Extension Types in this examples

Extension	Config
eddi://ai.labs.output	Object Config contains param uri with Link to output set, e.g. eddi://ai.labs.output/outputstore/outputsets/5a26d97417312628b46119fc?version=1

eddi://ai.labs.package/packagestore/packages/<UNIQUE_WORKFLOW_ID>?version=<WORKFLOW_VERSION>

Example

eddi://ai.labs.package/packagestore/packages/5a2ae60f17312624f8b8a445?version=1

See also the API documentation at http://localhost:7070/q/swagger-ui

5. Creating an Agent

Make a POST to /agentstore/agents with a JSON like this:

{
     "packages": [
          "eddi://ai.labs.package/packagestore/packages/<UNIQUE_WORKFLOW_ID>?version=<WORKFLOW_VERSION>"
     ]
}

Agent parameters

Name	Description
packages	Array of String, references to Workflows

b. You should again get a return code of 201 with a URI in the location header referencing the newly created agent :

eddi://ai.labs.agent/agentstore/agents/<UNIQUE_AGENT_ID>?version=<AGENT_VERSION>

Example:

eddi://ai.labs.agent/agentstore/agents/5a2ae68a17312624f8b8a446?version=1

See also the API documentation at http://localhost:7070/q/swagger-ui

6. Launching the Agent

Finally, we are ready to let the agent fly. From here on, you have the possibility to let an UI do it for you or you do it step by step.

The UI that automates these steps can be reached here: /chat/production/<UNIQUE_AGENT_ID>

Otherwise via REST:

1. Deploy the Agent:

   Make a POST to /administration/production/deploy/<UNIQUE_AGENT_ID>?version=<AGENT_VERSION>

   You will receive a 202 http code.

2. Since deployment could take a while it has been made asynchronous.

3. Make a GET to /administration/production/deploymentstatus/<UNIQUE_AGENT_ID>?version=<AGENT_VERSION> to find out the status of deployment.

NOT_FOUND, IN_PROGRESS, ERROR and READY is what you can expect to be returned in the body.

1. As soon as the Agent is deployed and has READY status, make a POST to /agents/<UNIQUE_AGENT_ID>/start

   1. You will receive a 201 with the URI for the newly created Conversation, like this:

      1. e.g.

         eddi://ai.labs.conversation/conversationstore/conversations/<UNIQUE_CONVERSATION_ID>

2. Now it's time to start talking to our Agent 1. Make a POST to /agents/<UNIQUE_AGENT_ID>/start/<UNIQUE_CONVERSATION_ID>

Option 1: is to hand over the input text as contentType text/plain. Include the User Input in the body as text/plain (e.g. Hello)

Option 2: is to hand over the input as contentType application/json, which also allows you to handover context information that you can use with the eddi configurations 1. Include the User Input in the body as application/json (e.g. Hello)

{
  "input": "some user input"
}

1. You have two query params you can use to config the returned output 1. returnDetailed - default is false - will return all sub results of the entire conversation steps, otherwise only public ones such as input, action, output & quickreplies 2. returnCurrentStepOnly - default is true - will return only the latest conversation step that has just been processed, otherwise returns all conversation steps since the beginning of this conversation

2. The output from the agent will be returned as JSON

3. If you are interested in fetching the conversationmemory at any given time, make a GET to /agents/<UNIQUE_AGENT_ID>/start/<UNIQUE_CONVERSATION_ID>?returnDetailed=true (the query param is optional, default is false)

If you made it till here, CONGRATULATIONS, you have created your first Agent with EDDI !

By the way you can use the attached postman collection below to do all of the steps mentioned above by clicking send on each request in postman.

1. Create outputSet

2. Creating package

3. Creating agent

4. Deploy the agent

5. Create conversation

6. Say Hello to the agent

Download the Postman collection to run through all the steps above.

External Links

Using collections in postman