Create an agent that reacts to user inputs

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 a Regular Dictionary inside Parser

See also Semantic Parser

Create regular dictionaries in order to store custom words and phrases. A dictionary is there to map user input to expressions, which are later used in Behavior Rules. A POST to /regulardictionarystore/regulardictionaries with a JSON in the body like this:

{
  "words": [
    {
      "word": "hello",
      "expressions": "greeting(hello)",
      "frequency": 0
    },
    {
      "word": "hi",
      "expressions": "greeting(hi)",
      "frequency": 0
    },
    {
      "word": "bye",
      "expressions": "goodbye(bye)",
      "frequency": 0
    },
    {
      "word": "thanks",
      "expressions": "thanks(thanks)",
      "frequency": 0
    }
  ],
  "phrases": [
    {
      "phrase": "good afternoon",
      "expressions": "greeting(good_afternoon)"
    },
    {
      "phrase": "how are you",
      "expressions": "how_are_you"
    }
  ]
}

Example using CURL:

curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{ \
"language" : "en", \
"words" : [ \
{ \
"word" : "hello", \
"expressions" : "greeting(hello)", \
"frequency" : 0 \
}, \
{ \
"word" : "hi", \
"expressions" : "greeting(hi)", \
"frequency" : 0 \
}, \
{ \
"word" : "bye", \
"expressions" : "goodbye(bye)", \
"frequency" : 0 \
}, \
{ \
"word" : "thanks", \
"expressions" : "thanks(thanks)", \
"frequency" : 0 \
} \
], \
"phrases" : [ \
{ \
"phrase" : "good afternoon", \
"expressions" : "greeting(good_afternoon)" \
}, \
{ \
"phrase" : "how are you", \
"expressions" : "how_are_you" \
} \
] \
}' 'http://localhost:7070/regulardictionarystore/regulardictionaries'

Dictionary parameters

Name	Description	Required
words	Array of Word
phrases	Array of Phrase
Word.word	String, single word, no spaces.	True
Word.expressions	String, "greeting(hello)": "greeting" is the category of this expression and "hello" is an entity.
Word.frequency	int, Used for a randomizer
Phrase.phrase	String, Spaces allowed	True
Phrase.expressions	String, "greeting(hello)": "greeting" is the category of this expression and "hello" is an entity.

The returned URI is a reference for this specific resource. This resource will be referenced in the agent definition.

2. Creating Behavior Rules

See also Behavior Rules

Next, create a behaviorRule resource to configure the decision making a. Make a POST to /behaviorstore/behaviorsets with a JSON in the body like this:

{
  "behaviorGroups": [
    {
      "name": "Smalltalk",
      "behaviorRules": [
        {
          "name": "Welcome",
          "actions": [
            "welcome"
          ],
          "conditions": [
            {
              "type": "occurrence",
              "configs": {
                "maxTimesOccurred": "0",
                "behaviorRuleName": "Welcome"
              }
            }
          ]
        },
        {
          "name": "Greeting",
          "actions": [
            "greet"
          ],
          "conditions": [
            {
              "type": "inputmatcher",
              "configs": {
                "expressions": "greeting(*)",
                "occurrence": "currentStep"
              }
            }
          ]
        },
        {
          "name": "Goodbye",
          "actions": [
            "say_goodbye",
            "CONVERSATION_END"
          ],
          "conditions": [
            {
              "type": "inputmatcher",
              "configs": {
                "expressions": "goodbye(*)"
              }
            }
          ]
        },
        {
          "name": "Thank",
          "actions": [
            "thank"
          ],
          "conditions": [
            {
              "type": "inputmatcher",
              "configs": {
                "expressions": "thank(*)"
              }
            }
          ]
        },
        {
          "name": "how are you",
          "actions": [
            "how_are_you"
          ],
          "conditions": [
            {
              "type": "inputmatcher",
              "configs": {
                "expressions": "how_are_you"
              }
            }
          ]
        }
      ]
    }
  ]
}

Behavior Rules parameters

Name	Description
BehaviorRule.name	String, e.g. "Smalltalk"
BehaviourGroup.behaviorRules	Array of BehaviorRule
BehaviorRule.name	String, e.g. "Greeting"
BehaviorRule.actions	Array of String, e.g. "greet" or "CONVERSATION_END"
BehaviorRule.conditions	Array of RuleChild
RuleChild.type	String, allowed values: —>"inputmatcher" (has params: "expressions" (Array of String( and "occurrence") —>"negation" (BehaviorExtension object, has params: "conditions" and "occurrence")
RuleChild.values	HashMap, allowed values: —>"expressions": String, mandatory. Expression e.g. "greeting(*)" or "how_are_you" —>"occurrence": String, optional. Allowed values "currentStep"
Negation.conditons	Array of NegationChild
NegationChild.type	String e.g. "occurrence"
NegationChild.values	HashMap, allowed values: —>"maxTimesOccurred": String, e.g. 1 —>"minTimesOccurred": String, e.g. 1 —>"behaviorRuleName": String

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

eddi://ai.labs.behavior/behaviorstore/behaviorsets/<UNIQUE_BEHAVIOR_ID>?version=<BEHAVIOR_VERSION>

Example:

eddi://ai.labs.behavior/behaviorstore/behaviorsets/5a26d8fd17312628b46119fb?version=1

3. 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": "welcome",
      "timesOccurred": 0,
      "outputs": [
        {
          "valueAlternatives": [
            {
              "type": "text",
              "text": "Welcome!"
            }
          ]
        },
        {
          "valueAlternatives": [
            {
              "type": "text",
              "text": "My name is E.D.D.I"
            }
          ]
        }
      ],
      "quickReplies": [
        {
          "value": "Hi EDDI",
          "expressions": "greeting(hi)"
        },
        {
          "value": "Bye EDDI",
          "expressions": "goodbye(bye)"
        }
      ]
    },
    {
      "action": "greet",
      "timesOccurred": 0,
      "outputs": [
        {
          "valueAlternatives": [
            {
              "type": "text",
              "text": "Hi there! Nice to meet up! :-)"
            },
            {
              "type": "text",
              "text": "Hey you!"
            }
          ]
        }
      ]
    },
    {
      "action": "greet",
      "timesOccurred": 1,
      "outputs": [
        {
          "valueAlternatives": [
            {
              "type": "text",
              "text": "Did we already say hi ?! Well, twice is better than not at all! ;-)"
            }
          ]
        }
      ]
    },
    {
      "action": "say_goodbye",
      "timesOccurred": 0,
      "outputs": [
        {
          "valueAlternatives": [
            {
              "type": "text",
              "text": "See you soon!"
            }
          ]
        }
      ]
    },
    {
      "action": "thank",
      "timesOccurred": 0,
      "outputs": [
        {
          "valueAlternatives": [
            {
              "type": "text",
              "text": "Your Welcome!"
            }
          ]
        }
      ]
    },
    {
      "action": "how_are_you",
      "timesOccurred": 0,
      "outputs": [
        {
          "valueAlternatives": [
            {
              "type": "text",
              "text": "Pretty good.. having lovely conversations all day long.. :-D"
            }
          ]
        }
      ]
    }
  ]
}

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.parser",
      "extensions": {
        "dictionaries": [
          {
            "type": "eddi://ai.labs.parser.dictionaries.integer"
          },
          {
            "type": "eddi://ai.labs.parser.dictionaries.decimal"
          },
          {
            "type": "eddi://ai.labs.parser.dictionaries.punctuation"
          },
          {
            "type": "eddi://ai.labs.parser.dictionaries.email"
          },
          {
            "type": "eddi://ai.labs.parser.dictionaries.time"
          },
          {
            "type": "eddi://ai.labs.parser.dictionaries.ordinalNumber"
          },
          {
            "type": "eddi://ai.labs.parser.dictionaries.regular",
            "config": {
              "uri": "eddi://ai.labs.regulardictionary/regulardictionarystore/regulardictionaries/<UNIQUE_DICTIONARY_ID>?version=<DICTIONARY_VERSION>"
            }
          }
        ],
        "corrections": [
          {
            "type": "eddi://ai.labs.parser.corrections.stemming",
            "config": {
              "language": "english",
              "lookupIfKnown": "false"
            }
          },
          {
            "type": "eddi://ai.labs.parser.corrections.levenshtein",
            "config": {
              "distance": "2"
            }
          },
          {
            "type": "eddi://ai.labs.parser.corrections.mergedTerms"
          }
        ]
      },
      "config": {}
    },
    {
      "type": "eddi://ai.labs.behavior",
      "config": {
        "uri": "eddi://ai.labs.behavior/behaviorstore/behaviorsets/<UNIQUE_BEHAVIOR_ID>?version=<BEHAVIOR_VERSION>"
      }
    },
    {
      "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

Extension	Config
eddi://ai.labs.parser	Dictionaries and/or corrections Object "extensions" can contain "dictionaries" (Array of Dictionary) and/or "corrections" (Array of Correction) Object "Dictionary" has params "type" and "config" (optional) Dictionary.type can reference Regular-Dictionaries "eddi://ai.labs.parser.dictionaries.regular" (needs param "config.uri") or be one of the EDDI out of the box types: —>"eddi://ai.labs.parser.dictionaries.integer" —>"eddi://ai.labs.parser.dictionaries.decimal" —>"eddi://ai.labs.parser.dictionaries.punctuation" —>"eddi://ai.labs.parser.dictionaries.email" —>"eddi://ai.labs.parser.dictionaries.time" —>"eddi://ai.labs.parser.dictionaries.ordinalNumber" Object "Correction" has params "type" and "config" (optional) Correction.type can reference one of the EDDI out of the box types: —>"eddi://ai.labs.parser.corrections.stemming": Object "config" has params "language" (String e.g. "english") and "lookupIfKnown" (Boolean) —>"eddi://ai.labs.parser.corrections.levenshtein": Object "config" has param "distance" (Integer, e.g. 2) —>"eddi://ai.labs.parser.corrections.mergedTerms"
eddi://ai.labs.behavior	Object Config contains param uri with Link to a behavior set, e.g. eddi://ai.labs.behavior/behaviorstore/behaviorsets/5a26d8fd17312628b46119fb?version=1
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

New

Now you can use the new feature of defining properties in the package definition : This can be used by introducing an extension with type eddi://ai.labs.property which has the config model as follows:

{
  "type": "eddi://ai.labs.property",
  "config": {
    "setOnActions": [
      {
        "actions": "string",
        "setProperties": [
          {
            "name": "string",
            "fromObjectPath": "string",
            "scope": "string"
          }
        ]
      }
    ]
  }
}

Description of eddi://ai.labs.property model

Name	Description
setOnActions.actions	(string) defines which for which actions (triggered by BehaviorRules) these Properties should be set
setOnActions.setProperties	(Array <Property>: ) must respect the Propertymodel: name, fromObjectPath and scope.
setOnActions.setProperties.name	(string) name of the Property.
setOnActions.setProperties.fromObjectPath	(string) path to the json object.
setOnActions.setProperties.scope	(string) Possible values step, conversation and longTerm .

Example of eddi://ai.labs.property

{
  "packageExtensions": [
   ...
    {
      "type": "eddi://ai.labs.property",
      "config": {
        "setOnActions": [
          {
            "actions": "currentWeather",
            "setProperties": [
              {
                "name": "city",
                "fromObjectPath": "memory.current.input",
                "scope": "longTerm"
              }
            ]
          }
        ]
      }
    },
   ...
    {
      "type": "eddi://ai.labs.property",
      "config": {
        "setOnActions": [
          {
            "actions": "currentWeather",
            "setProperties": [
              {
                "name": "currentWeather",
                "fromObjectPath": "memory.current.httpCalls.currentWeather",
                "scope": "conversation"
              }
            ]
          }
        ]
      }
    },
   ...
  ]
}

You should again get a return code of 201 with an URI in the location header referencing the newly created package format

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>"
],
"channels": []
}

Agent parameters

Name	Description
packages	Array of String, references to Workflows
channels	Array of Channel,
Channel.type	String, e.g. "eddi://ai.labs.channel.facebook"
Channel.config	Config Object. For "Facebook" this object has the params "appSecret" (String), "verificationToken" (String), "pageAccessToken" (String)

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 dictionary (greetings)

2. Create behaviourSet

3. Create outputSet

4. Creating package

5. Creating agent

6. Deploy the agent

7. Create conversation

8. Say Hello to the agent

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

External Links

Using collections in postman