{"id":6326,"date":"2020-01-13T13:19:18","date_gmt":"2020-01-13T13:19:18","guid":{"rendered":"https:\/\/www.smatbot.com\/blog\/?p=6326"},"modified":"2020-01-13T13:19:18","modified_gmt":"2020-01-13T13:19:18","slug":"how-to-use-rasax-to-create-an-ai-chatbot","status":"publish","type":"post","link":"https:\/\/www.smatbot.com\/blog\/how-to-use-rasax-to-create-an-ai-chatbot\/","title":{"rendered":"How To Use RasaX To Create An AI Chatbot."},"content":{"rendered":"<h2><b>What is Rasa:<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">In simple terms, <\/span><a href=\"https:\/\/rasa.com\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">Rasa<\/span><\/a><span style=\"font-weight: 400;\"> is an open-source platform used to build conversational assistants that can be deployed anywhere. Rasa provides Rasa X tool that can be used to build complete chatbots using a graphical interface(GUI).<\/span><!--more--><\/p>\n<p><span style=\"font-weight: 400;\">You can check the installation guides for <\/span><a href=\"https:\/\/rasa.com\/docs\/rasa\/user-guide\/installation\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">Rasa<\/span><\/a><span style=\"font-weight: 400;\"> and <\/span><a href=\"https:\/\/rasa.com\/docs\/rasa-x\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">RasaX<\/span><\/a><span style=\"font-weight: 400;\"> for running them in your environment.<\/span><\/p>\n<h3><b>Rasa X:\u00a0<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Rasa X<\/span><span style=\"font-weight: 400;\"> helps us to learn from real conversations with the help of a graphical user interface. It is a tool designed to make it easier to Build, Improve and deploy assistants by learning from real conversations.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Rasa X is packed with new UI features which allow you to:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\"><b>View and annotate conversations:<\/b><span style=\"font-weight: 400;\"> You can review and fix conversations that didn\u2019t work as expected with your users.<\/span><\/li>\n<li style=\"font-weight: 400;\"><b>Get feedback: <\/b><span style=\"font-weight: 400;\">You can share your assistant with real-world users and get immediate feedback and training data<\/span><\/li>\n<\/ul>\n<h2><b>Getting Started:<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Before we start with building our chatbot lets take a look at some fundamentals<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\"><b>Intent<\/b><span style=\"font-weight: 400;\">: Consider it as the aim or target of user input. Suppose If a user says, \u201c<\/span><i><span style=\"font-weight: 400;\">Which month is tomorrow<\/span><\/i><span style=\"font-weight: 400;\">?\u201d, the intent would be finding the month.<\/span><\/li>\n<li style=\"font-weight: 400;\"><b>Entity<\/b><span style=\"font-weight: 400;\">: It is the information or an item from the user input that can be extracted for further operations. If we consider the previous example, we can understand the target is to find the month of a particular day, but we do not know of which day yet. If we can extract \u201c<\/span><i><span style=\"font-weight: 400;\">Tomorrow\u201d<\/span><\/i><span style=\"font-weight: 400;\"> as an entity here, we can then find the month of <\/span><i><span style=\"font-weight: 400;\">\u201cTomorrow\u201d<\/span><\/i><span style=\"font-weight: 400;\">.<\/span><\/li>\n<li style=\"font-weight: 400;\"><b>Actions<\/b><span style=\"font-weight: 400;\">: As the name suggests, it is an action performed by the bot be it replying to something, parsing a database, triggering an API etc.(in our case here responses)<\/span><\/li>\n<li style=\"font-weight: 400;\"><b>Stories<\/b><span style=\"font-weight: 400;\">: These are the interactions between a user and the bot. Stories can otherwise be defined as intents captured and actions performed. Using these Stories a developer can code what to based on user inputs. Like when a question is asked by a user and you could not extract all intents &amp; entities accordingly.<\/span><\/li>\n<\/ul>\n<h2><b>Creating a Rasa agent:<\/b><\/h2>\n<h3><b>Creating intents:\u00a0<\/b><\/h3>\n<ol>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">After you log in to rasa x in the left side panel, below training, select NLU training and click the \u2018+\u2019 icon in Annotate new data.<\/span><span style=\"font-weight: 400;\"><img fetchpriority=\"high\" decoding=\"async\" class=\"wp-image-6329 aligncenter\" src=\"https:\/\/www.smatbot.com\/blog\/wp-content\/uploads\/2020\/01\/Screenshot-2020-01-13-at-4.08.24-PM.png\" alt=\"\" width=\"638\" height=\"297\" \/><\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Now add any possible user query and click enter.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Now in the text box beside it, give the new intent name and click on \u2018create {intent name}\u2019 in the dropdown and mark it as correct.<img decoding=\"async\" class=\"wp-image-6335 aligncenter\" src=\"https:\/\/www.smatbot.com\/blog\/wp-content\/uploads\/2020\/01\/Screenshot-2020-01-13-at-4.10.57-PM.png\" alt=\"\" width=\"638\" height=\"312\" \/><\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">We suggest to follow this pattern while choosing intent name convention, eg: \u2018SmatBot.pricing\u2019 where SmatBot is a product\/service name and pricing is the intent.<\/span><\/li>\n<\/ol>\n<h3><b>Adding responses:\u00a0<\/b><\/h3>\n<ol>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Once you create a new intent head over to responses and click the \u2018+\u2019 button.<img decoding=\"async\" class=\"wp-image-6332 aligncenter\" src=\"https:\/\/www.smatbot.com\/blog\/wp-content\/uploads\/2020\/01\/Screenshot-2020-01-13-at-4.09.59-PM.png\" alt=\"\" width=\"640\" height=\"314\" \/><\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">In the first text field give the utterance name, eg: utter_SmatBot.pricing and corresponding response in the second text field.\u00a0<\/span><\/li>\n<\/ol>\n<h3><b>Creating a story:\u00a0<\/b><\/h3>\n<ol>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Now let\u2019s map intent with a response. Head over to stories and click on the \u2018+\u2019 button.<img loading=\"lazy\" decoding=\"async\" class=\"wp-image-6330 aligncenter\" src=\"https:\/\/www.smatbot.com\/blog\/wp-content\/uploads\/2020\/01\/Screenshot-2020-01-13-at-4.08.43-PM.png\" alt=\"\" width=\"638\" height=\"314\" \/><\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Use below syntax for creating a story and click on save.<\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">\u00a0# {story name}<\/span><\/p>\n<p><span style=\"font-weight: 400;\">* \u201cintent name\u201d<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0&#8211; \u201cutterance name\u201d<\/span><\/p>\n<h3><b>Adding entities:<\/b><\/h3>\n<ol>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">To add entities, you need to double click on the word in training data query you added.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Now, in the dialogue box, type in the entity name and click \u2018create{entity name}\u2019 in the dropdown.<\/span><\/li>\n<\/ol>\n<h4><b>Example:\u00a0<\/b><\/h4>\n<p><span style=\"font-weight: 400;\">Here, let\u2019s say for example you need to train your agent for answering pricing related questions.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Users can ask it with many different synonyms like cost, expense etc. First, create an entity named \u2018pricing\u2019 and You can add cost, expense in the training data and mark them as synonyms for your entity \u2018pricing\u2019.<img loading=\"lazy\" decoding=\"async\" class=\"wp-image-6331 aligncenter\" src=\"https:\/\/www.smatbot.com\/blog\/wp-content\/uploads\/2020\/01\/Screenshot-2020-01-13-at-4.09.04-PM.png\" alt=\"\" width=\"639\" height=\"317\" \/><\/span><\/p>\n<p><b>Note:<\/b><span style=\"font-weight: 400;\"> You should create at least 2 or more samples for intent mapping, 2 or more responses for an utterance, 2 or more samples for an entity mapping.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In the same way, you can create multiple intents, corresponding responses, stories and entities. This makes the bot more diverse with multiple FAQ\u2019s.<\/span><\/p>\n<h2><b>Training and Activating Model:\u00a0<\/b><\/h2>\n<ol>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Once you add all the intents, responses, stories and entities in your agent click on \u2018train\u2019.<img loading=\"lazy\" decoding=\"async\" class=\"wp-image-6333 aligncenter\" src=\"https:\/\/www.smatbot.com\/blog\/wp-content\/uploads\/2020\/01\/Screenshot-2020-01-13-at-4.10.21-PM.png\" alt=\"\" width=\"640\" height=\"298\" \/><\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">After the completion of training head over to the models and activate the latest trained model by clicking on the three dots shown in the below image.<img loading=\"lazy\" decoding=\"async\" class=\"wp-image-6336 aligncenter\" src=\"https:\/\/www.smatbot.com\/blog\/wp-content\/uploads\/2020\/01\/Screenshot-2020-01-13-at-4.11.19-PM.png\" alt=\"\" width=\"639\" height=\"313\" \/><\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">Note: This step is Mandatory after changes done to the agent. Else, your agent will be working on the previously activated model.<\/span><\/p>\n<h2><b>Deploying your rasa agent.<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Now there are multiple ways to deploy your rasa agent. A simple google search can help you find multiple ways to deploy it for your users. Also, You can check\u00a0<\/span><span style=\"font-weight: 400;\">out\u00a0<\/span><a href=\"https:\/\/www.smatbot.com\/help\/getting-started-with-agents\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">this<\/span><\/a><span style=\"font-weight: 400;\"> link for connecting your Rasa chatbot to SmatBot chatbot platform.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>What is Rasa: In simple terms, Rasa is an open-source platform used to build conversational assistants that can be deployed anywhere. Rasa provides Rasa X tool that can be used to build complete chatbots using a graphical interface(GUI).<\/p>\n","protected":false},"author":4,"featured_media":6342,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[33,4,6],"tags":[],"class_list":["post-6326","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-engine","category-chatbot","category-smatbot"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.8.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Create a simple AI chatbot with rasa-x<\/title>\n<meta name=\"description\" content=\"Rasa X is a tool that runs above Rasa Core and Rasa NLU that can be used to build complete chatbots using a graphical interface(GUI).\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.smatbot.com\/blog\/how-to-use-rasax-to-create-an-ai-chatbot\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Create a simple AI chatbot with rasa-x\" \/>\n<meta property=\"og:description\" content=\"Rasa X is a tool that runs above Rasa Core and Rasa NLU that can be used to build complete chatbots using a graphical interface(GUI).\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.smatbot.com\/blog\/how-to-use-rasax-to-create-an-ai-chatbot\/\" \/>\n<meta property=\"og:site_name\" content=\"Smatbot - 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