We are building an amazing chatbot Lemonade In this section we are going to work on the Dialogue part for “building a simple using conversational AI solution”. chatbot RASA If you haven’t went through I recommend you to first read and only then read this one. part 1 part 1 Now things are getting interesting, chatbot wise — till now we’ve built the /U part and now we are going to work on the dialogue side. NLP As I mentioned in part 1, for every chatbot that actually do a little bit more that just question answering, the very basic one is a chatbot that supports “slot filling”, which means (in our example) to interact with the user to get the mandatory slots that it needs (according to the “domain definition” — don’t worry we’ll get to that later on). One slot filling scenario could be: user: “I want Pizza” ; #missing slots: [size, toppings, etc.] bot: “what size?” user: “large” bot: “Which toppings would you like?” user: “Olives” In this example we have 2 interactions with the user — 2 slots to fill. Building the Dialogue Similar to what we’ve built in the NLU (part 1 mentioned above) we should create the domain and the training data. Building the Domain Here we would create the file chat_domail.yml. You can use this file: Pay attention to the last one — more info below on the explanation part. But if you create your own or changing this one, it is always a good practice to validate the format (this is a fragile format and things won’t work in case you break it): to online yml validation. Link Let’s try to explain the file above. Intents — is the parent structure, usually are the supported actions of your bot (but there are exceptions) Entities — are the words that you want the bot to extract from the free text Slots — Defining them here, is like saying to the bot which entities to track through the dialogue, for example, if the user is saying: “I want pizza”, we want the bot to interact with the user to get the “size” or the “topping” slots fulfilled. Actions — are either utterances, which means, the output that we want to send the user, or you can actually create your own class that inherent form Action. In our example its: “actions.ActionOrderPizza”, here again Rasa has a very cool interface, you should inherent from Class Action and add your logic in the derived class according to what you need, here is an example — create the file : action.py Top 3 Bot Tutorials 1. Best chatbot platforms to build a chatbot 2. Build simple ChatBot in Python with RASA — Part 1 3. How I developed my own ‘learning’ chatbot in Python In the above example I created it as “stubs” no real logic there, just for the sake of the example. Building the Training Data for the Dialogue Here we are going to build a mark down file, we’ll named it: stories.md, as the name implies here we describe the possible dialogue flows as user stories. Here you may raise a concern that its a very manual process but don’t worry RASA has a great tool for you to online train you dialogue! or even more load pre-trained dialogues. But first let’s create a very short and basic flow, and train the dialogue model, we’ll get back to the online training later on. For the basic flow you can use this file stories.md: Pay attention that in the above flow we are covering the following scenario: Bot: “Hello, how can I help you?” User: “I want pizza” #missing the slots: “size” and “toppings” Bot: “What size?” User: “large” Bot: “what toppings would like on your pizza?” User: “cheese” Now we are ready to train a dialogue using the above training data. Let’s write some code, you can use this file: Now run: python dialogue_model.py Note: the code above is running with no errors on Python3 but for some reason I had some issues with Python2. If everything went well you should have the model trained and the output is under, models/dialogue/, it should look like this: NOW we are ready for the online training! yay!!! As I promised RASA has an amazing capability to online train your bot and create a much more elaborated stories.md file for you to use after online training session is finished. Now create the online_train.py: No we are ready to run: python online_train.py You should get an interactive session in which you should follow the expected flow, for example: In the first green line it says that the bot has been loaded and is waiting for the user input. Next we type: “hi” Now you can see that the system prints the “Chat history”: we can see that the flow is correct: bot did: action_listen user said: hi whose intent is: greet 4. Now the system is waiting for your feedback, in this case the flow is correct, so we should type: 2. 5. You can continue doing this as long as you want, when you finish type “0” to export the new stories.md file (give it new name, such: “stories_v1.md”). Now replace the previous stories file (back it up first!) or concatenate them in the same stories file, that way you should have more and more training data! That’s it — now the last part is to test it — more to come… Please feel free ask question here or go to relevant gitter section: _machine learning based dialogue engine for conversational software_gitter.im RasaHQ/rasa_core
