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wiki:software:beuthbot:rasa [23.01.2020 00:46]
Abirathan Yogarajah 		wiki:software:beuthbot:rasa [24.01.2020 15:18] (aktuell)
Abirathan Yogarajah
Zeile 48: Zeile 48:
             "value": "2020-01-20T00:00:00.000+01:00",             "value": "2020-01-20T00:00:00.000+01:00",
             "confidence": 1.0,             "confidence": 1.0,
-            "additional_info": { +            ...
-                "values": [ +
-                    { +
-                        "value": "2020-01-20T00:00:00.000+01:00", +
-                        "grain": "day", +
-                        "type": "value" +
-                    } +
-                ], +
-                "value": "2020-01-20T00:00:00.000+01:00", +
-                "grain": "day", +
-                "type": "value" +
-            }, +
-            "entity": "time", +
-            "extractor": "DucklingHTTPExtractor"+
         }         }
     ]     ]
-    . +    ...
-    . +
-    .+
 } }
 </code> </code>
Zeile 114: Zeile 99:
 rasa shell nlu –m models/name-of-the-model.tar.gz rasa shell nlu –m models/name-of-the-model.tar.gz
 </code> </code>
 +
 +Running Duckling:
 +
 +After using the command "rasa train nlu" a model is generated. When communicating with Rasa via the shell ("rasa shell nlu ...") the component "Duckling" is not addressed. With Duckling you can identify and resolve dates. To use Duckling you can add the trained model in the path "docker\rasa-app-data\models". Then you can run Rasa and Duckling as docker-containers and query them using the Rest API. Running Rasa and Duckling as docker-containers are explained in a later section.
  
 === How to generate training datasets === === How to generate training datasets ===
  
 In this project we write training data in the form of JSON, because JSON offers the possibility to extract entities from a text message. For this purpose the data was generated with the tool   [[https://github.com/YuukanOO/tracy|Tracy]]. In the image below, Tracy is shown with "Öffnungszeiten". Entities are added as "slots", such as "Ort". Training data follows in the lower part of the picture. As training data, you can specify messages, which the user can send to the "chatbot". Currently the three user intentions "Mensa", "Wetter" and "Öffnungszeiten" are supported. In this project we write training data in the form of JSON, because JSON offers the possibility to extract entities from a text message. For this purpose the data was generated with the tool   [[https://github.com/YuukanOO/tracy|Tracy]]. In the image below, Tracy is shown with "Öffnungszeiten". Entities are added as "slots", such as "Ort". Training data follows in the lower part of the picture. As training data, you can specify messages, which the user can send to the "chatbot". Currently the three user intentions "Mensa", "Wetter" and "Öffnungszeiten" are supported.
 +
 +{{:wiki:software:beuthbot:index.png_.png?400|}}
  
 === Add new Model for Rasa-Container (Docker) === === Add new Model for Rasa-Container (Docker) ===
Zeile 132: Zeile 123:
 === Installing === === Installing ===
  
-After the repository has been cloned and the prerequisites have been fulfilled, you can run the Docker-Compose-file.+After the repository has been cloned and the prerequisites have been fulfilled, you can run the Docker-Compose-file. The docker commands must be executed in the 'docker'-directory.
  
 :!: FIXME FIXME FIXME :!: FIXME FIXME FIXME
  
 <code> <code>
-# build and start Rasa-NLU-Container && serve at localhost:5005+# build and start Containers && serve at localhost:5005 (rasa) and at localhost:8000 (duckling)
 docker-compose up docker-compose up
  
-# stop and remove rasa-container, volumes, images and networks+# stop and remove rasa-containers, volumes, images and networks
 docker-compose down docker-compose down
  
Zeile 217: Zeile 208:
 - https://www.artificial-solutions.com/wp-content/uploads/chatbots-ebook-deutsche.pdf (Retrieved 12.12.2019)\\ - https://www.artificial-solutions.com/wp-content/uploads/chatbots-ebook-deutsche.pdf (Retrieved 12.12.2019)\\
 - https://docs.docker.com/ (Retrieved 12.12.2019) - https://docs.docker.com/ (Retrieved 12.12.2019)
 +<WRAP pagebreak></WRAP>
wiki/software/beuthbot/rasa.1579736810.txt.gz · Zuletzt geändert: 23.01.2020 00:46 von Abirathan Yogarajah

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