Create a bot 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

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

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 bot'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 Package

Now we will align the just created LifecycleTasks in the Package. 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>"
      }
    }
  ]
}

Package parameters

Extension Types

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

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_PACKAGE_ID>?version=<PACKAGE_VERSION>

Example

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

See also the API documentation at http://localhost:7070/view#!/configurations/createPackage

5. Creating a Bot

Make a POST to /botstore/bots with a JSON like this:

{
"packages": [
"eddi://ai.labs.package/packagestore/packages/<UNIQUE_PACKAGE_ID>?version=<PACKAGE_VERSION>"
],
"channels": []
}

Bot parameters

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

eddi://ai.labs.bot/botstore/bots/<UNIQUE_BOT_ID>?version=<BOT_VERSION>

Example:

eddi://ai.labs.bot/botstore/bots/5a2ae68a17312624f8b8a446?version=1

See also the API documentation at http://localhost:7070/view#!/configurations/createBot

6. Launching the Bot

Finally, we are ready to let the bot 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/unrestricted/<UNIQUE_BOT_ID>

Otherwise via REST:

  1. Deploy the Bot:

    Make a POST to /administration/unrestricted/deploy/<UNIQUE_BOT_ID>?version=<BOT_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/unrestricted/deploymentstatus/<UNIQUE_BOT_ID>?version=<BOT_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 Bot is deployed and has READY status, make a POST to /bots/unrestricted/<UNIQUE_BOT_ID>

    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 Bot 1. Make a POST to /bots/unrestricted/<UNIQUE_BOT_ID>/<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 bot will be returned as JSON

  3. If you are interested in fetching the conversationmemory at any given time, make a GET to /bots/unrestricted/<UNIQUE_BOT_ID>/<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 Chatbot 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 bot

  6. Deploy the bot

  7. Create conversation

  8. Say Hello to the bot

Using collections in postman

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