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Chatbots Lab Con Whatsapp Chatbots Lab con WhatsApp Setiembre 2019 Copyright © GeneXus S.A. 1988-2019. All rights reserved. This document may not be reproduced by any means without the express permission of GeneXus S.A.The information contained herein is intended for ​ ​ personal use only. Registered Trademarks: GeneXus is a trademark or registered trademark of GeneXus S.A. All other ​ ​ ​ trademarks mentioned herein are the property of their respective owners. 2 OBJETIVO Bienvenidos al lab de chatbots! Aquí veremos la potencia del generador de chatbots introducido en la versión GeneXus 16. Crearemos un chatbot que atiende consultas en un Encuentro de tecnología y negocios. Los chatbots generados con GeneXus ejecutan tanto en Web como Mobile. En este lab estaremos accediendo a nuestro chatbot a través de WhatsApp. ALGÚN AJUSTE PREVIO Requerimientos previos del lab: Versión de GeneXus: Utilizaremos la versión GeneXus Beta o GeneXus 16 upgrade 6 (o superior). WhatsApp: Para la integración con WhatsApp lo haremos a través de Twilio. Debes seguir los pasos detallados a continuación para configurar la cuenta: https://wiki.genexus.com/commwiki/servlet/wiki?44141,GX29+Chatbots+Laborat ory+Initial+Setup, Proveedor NLP: Puedes usar Watson o DialogFlow. En cada uno de los casos, sigue las instrucciones dadas: ● Watson https://wiki.genexus.com/commwiki/servlet/wiki?39748,Configure+IBM+Wa tson+services+for+the+Chatbot+generator, 3 ● DialogFlow https://wiki.genexus.com/commwiki/servlet/wiki?39749,Configure+Google+ Dialogflow+for+the+Chatbot+generator, Base de conocimientos: Debes hacer un checkout de la KB: http://samples.genexusserver.com/beta/kbdashboard.aspx?SampleChatbotsWha tsApp CONOCIENDO LOS CHATBOTS EN GENEXUS Para comenzar, ejecutemos los siguientes pasos para dialogar con el chatbot. El chatbot que tienes en la KB se llama RUDILabGX29 y aún no ha sido sincronizado con el Provider. En esta sección veremos específicamente el caso de Watson puedes usar DialogFlow si así lo prefieres. En el objeto RUDILabGX29, en el nodo padre “Conversational Flows Instance”, configuremos la propiedad API Key. La propiedad Authentication Type debe ser ​ ​ igual a “IAM Authentication”, y la Region “Dallas”. El workspace Id se asigna de manera automática. 4 Luego de salvar los cambios (lo mismo ocurre haciendo botón derecho sobre la instancia - objeto RUDILabGX29- seleccionado la opción Synchronize Chatbot), se genera un archivo de metadata que se impacta en el NLP provider, para generar el (1) diálogo con sus intenciones y entidades .​ ​ Dada una intención del usuario se pueden distinguir diferentes objetos o tópicos, dentro de esa intención, que llamamos formalmente Entidades. Las entidades se almacenan en el NLP provider, con sus posibles valores y sinónimos. Para crear un chatbot, se necesita modelar su comportamiento (definir intenciones, entidades) y la respuesta que se muestra al usuario para cada una de esos intenciones. 5 NOTA: EL GENERADOR DE CHATBOTS FUNCIONA COMO UN PATTERN. A PARTIR DEL MODELO, EL PATTERN ​ GENERA AUTOMÁTICAMENTE TODOS LOS OBJETOS NECESARIOS PARA RESOLVER LA CONVERSACIÓN ENTRE EL USUARIO Y EL NLP PROVIDER. Ya entendimos los principales conceptos, y qué es el Chatbot Generator, ahora ¡empecemos a construir! EL CHATBOT EN ACCIÓN A través de nuestro chatbot, los usuarios podrán: ● Saber dónde se encuentran las salas de conferencias ● Saber dónde se encuentran algunos servicios ● Obtener datos de los oradores Hagamos botón derecho sobre el objeto RUDILabGX29, y luego Generate ChatBot. Nota: La opción Generate Chatbot hace que se calculen los objetos que se van a ​ generar a partir de nuestra instancia (RUDILabGX29) y se importen en la KB. Puede aparecer el siguiente mensaje, que indica que se actualizarán los recursos del generador de chatbots. Hagamos un Rebuild all. ​ ​ Se va a solicitar el create de la BD. Observar que hay dos tablas, llamadas GXChatMessage y GXChatUser, que son usadas por el chatbot. INICIALIZACIÓN 6 Las entidades han sido creadas en el NLP provider, pero sus valores deben ser inicializados. Vamos a trabajar con las siguientes entidades: ● Floors ● Rooms ● Speakers Los valores serán cargados en el NLP provider a través de una API provista por el módulo chatbots. Abre el objeto BatchEntities para conocer el método usado para subir valores y ​ ​ entidades a Watson (Chatbot.SendEntityValues). Ejecuta entonces, el proc BatchEntities (botón derecho - Run desde la solapa). Este proceso entrena el sistema, por lo cual si bien el request al NLP provider retorna de inmediato un HTTP Status 200, puede tardar unos minutos el proceso de entrenamiento. Verás lo siguiente en el output de GeneXus: Floors: [{"Id":"","Type":2,"Description":"200 "}] Rooms: [{"Id":"","Type":2,"Description":"200 "}] Speakers: [{"Id":"","Type":2,"Description":"200 "}] El chatbot ya está listo! Veremos a continuación como ejecutarlo en WhatsApp. EJECUTANDO EL CHATBOT EN WHATSAPP Debes tener a mano el Auth Token dado al crear la cuenta en Twilio. Para obtenerlo, puedes entrar a la consola de Twilio (https://www.twilio.com/console), y copiarlo de allí: ​ ​ Auth Token: 7 Nota: Solo los usuarios que se han unido al sandbox, pueden recibir mensajes. Como tú ya has hecho un join al sandbox, podrás recibir mensajes. Si quieres que alguien más lo haga, debe unirse también. 1. Para que WhatsApp atienda las solicitudes, tenemos definido en la Kb un webhook (es un objeto con call Protocol = HTTP), llamado TwilioWppWebhook. Editar el objeto TwilioWppWebhook y asignar el valor de Auth Token a la ​ ​ variable &Value. Luego debes hacer “Build with this only” de TwilioWppWebhook. 8 2. Debemos configurar en Twilio, nuestro webhook. Debes ir a esta URL (https://www.twilio.com/console/sms/whatsapp/sandbox) y configurar en ​ ​ “When a message comes in”, la URL de tu aplicación completa, incluyendo ​ el webhook (ej: http://40.122.78.153/<MiBASEURL>/TwilioWppWebhook.aspx). Nota: Debes usar la IP pública de la máquina. La puedes obtener a través de esta URL MiBASEURL la obtienes de la propiedad Web Root (Preferences > Generators - Default (C# Web)) 9 Entonces, en la consola de Twilio, te quedará así: Abajo en la página tienes el botón de save. 10 Verificar que la URL del webhook haya quedado configurada correctamente. Ahora todo está listo para probar el chatbot con WhatsApp. Veamos ahora cómo se resuelven las intenciones, sin antes dejar de saludar al chatbot! Abre WhatsApp, y saluda: 11 Nota: Este comportamiento se resuelve con el Flow “Opening” de nuestro chatbot. ● Dónde se encuentran las salas de conferencias 12 Puedes ver el Flow “ServiceLocationRoom” que es el que resuelve esta intención. 13 ● Dónde se encuentra el baño 14 En el Flow “ServiceLocationBathroom” verás la resolución de este intent. ● Obtener datos de los oradores El Flow que resuelve este intent es “DetailFromSpeaker”. ENTENDIENDO COMO FUNCIONA EL CHATBOT 15 Veamos por ejemplo el Flow ServiceLocationBathroom. ​ Observa que tiene un User Input “SearchFloor” en donde al usuario se le solicita indicar el piso donde se encuentra. Dicho User Input tiene Match with Entity = TRUE, y la entidad es Floor. Es decir, que si el usuario ingresa un piso que no está ​ ​ dentro de los valores de la entidad, el Provider indica del error, y se le da el feedback al usuario con el mensaje indicado en la propiedad On Error Messages. ¿Qué significa que se mapea con una entidad en Watson? Que se controlará que ​ el usuario no ingrese un dato que no se encuentre dentro de los valores (y sus sinónimos) de esta entidad. Es como un chequeo de integridad, contra el Provider. ¿CÓMO SE RESUELVE LA RESPUESTA? CUANDO EL USUARIO TERMINA DE INGRESAR LOS DATOS SOLICITADOS, SE EJECUTA EL OBJETO DADO EN LA PROPIEDAD CONVERSATIONAL OBJECT. En este caso, la propiedad Conversational Object indica un procedimiento que busca los servicios más cercanos al piso donde se encuentra el usuario. La firma de ese proc es: 16 parm(in:&SearchFloor, out:&BathRoomMapImage); Se configuran varias respuestas, condicionadas al piso donde se encuentra el usuario, y se muestra al usuario final, la imagen del mapa devuelta por el objeto conversacional. DOCUMENTACIÓN Chatbots in GeneXus Chatbot Generator Chatbots Architecture How to build a Chatbot using GeneXus Chatbots Channels API Chatbot Generator Try Live 17 REFERENCIAS (1) P​ or ejemplo, dada la intención de “Obtener los datos de un orador”, una ​ ​ entidad de esa intención sería los “Oradores”, y el valor de dicha entidad por ​ ​ ​ ejemplo, puede ser “Gaston Milano”. Entonces, le llamamos entidad a una agrupación de varios valores. Alguno o varios de ellos serán instanciados en la consulta. 18 .
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