Slot List Rasa
- Slot List Rasa Youtube
- Slot List Rasa Tv
- Slot List Rasa Example
- Slot List Rasa Movie
- Slot List Rasa Video
Slot List Rasa the Slot List Rasa third day (min £20). Winnings won with games that require deposit, have to be wagered 35x. Bonuses that require deposit, have to be wagered 35x. Currentslotvalues source ¶ Return the currently set values of the slots. Currentstate (eventverbosity: rasacore.trackers.EventVerbosity = ) → Dictstr, Any source ¶ Return the current tracker state as an object. Eventsafterlatestrestart source ¶ Return a list of events after the most recent restart.
A Step wise guide to create a Chatbot Using RASA
In this article, I will guide you on how to build your own Rasa chatbot in a simple way.
Why Rasa Stack for Building Chatbots?
To build AI assistants and chatbots the best open source machine learning framework is Rasa. To build complex chatbots it’s time efficient and one of the most effective tools.
Rasa has two main modules:
- Rasa NLU for understanding user messages
- Rasa Core for holding conversations and determining what to do next
Rasa X — It’s a Browser based GUI tool. By using GUI based interactive mode it will allow you to train Machine learning model. It’s an optional tool in Rasa Software Stack.
Rasa NLU — This is the area, where rasa tries to know User messages to detect Intent and Entity in your message. Rasa NLU is a tool for intent classification, response retrieval and entity extraction in chatbots,most of which have some additional dependencies.
- Spacy (You need to install it separately)
- Tensorflow (By Default available with Rasa)
Rasa Core — It helps you with contextual message flow. Depending on User message, it can forecast dialogue as a reply and can trigger Rasa Action Server.
Whenever you do “pip install rasa” Rasa uses Tensorflow internally, or “pip install rasa-x”, by default it installs Tensorflow.
Prerequisite: Anaconda distribution to be installed.
Step 1:
Make a conda environment with the name of rasa(you can name anything).
Activate the rasa environment.
Step 2:
Install Rasa Open Source using pip.
Step 3:
Now create a new folder where you want to create the Rasa project and navigate into the folder.Now execute the below command to create a new Rasa project.
Below command creates a new basic Rasa project along with some required files that are essential to run Rasa project.
It prompts with the below message. Then press enter.
Please enter a path where the project has to be created.
It prompts with another message
“Do you want to train an initial model?” . Press Y/n.
If you press “Y” then the essential model will be trained and this basic model is prepared to run.
If you press “n” then the model will not be trained and if you want to train the model later use the command “rasa train”
Once the project is made, you can see the below files are going to be created within the project folder.
Let’s understand the files created by Rasa.
Slot List Rasa Youtube
Under the data folder, two files are created.The first file is nlu.md and therefore the other one is stories.md.
nlu.md: NLU means Natural Language Understanding and contains your training data.
The messages that have same meaning are grouped together and named with intents. Intent means finding the user’s intentions. After you train the model, Rasa will find the correct intent for your new/unseen messages.
Now, the new intent named as inform is added to our nlu.md file.
So, we have added the new intent(inform) and entities language, movie name, planned date, theater name, planned time, no_of_tickets.
Entity which is nothing but significant data and usually this can be names, date, time etc. The entities which are useful for taking next actions can be extracted from the intent.
Stories.md:The user interaction stories will be present here. In this file, we will explain our assistant how to reply to specific intents either in the form of text or actions. Actions can be whatever either calling the backend or some third-party API’s.
From the above code, we are teaching our assistant to execute the action(find_language_types) once the greet intent is identified, and fill the slot language. Language slot may be filled with any value.
Slot is nothing but a variable which holds the values entered by the user throughout the session and the slot values can be passed through next conversation.
Step 4:
domain.yml file defines your assistant domain. It contains list of intents, entities, slots, actions, responses.
Rasa predicts the user intention as per the user message and identifies the intent from nlu.md file and depending on the intent the respective utterance will be activated as per we have mentioned in stories.md file.
So, if the utterance is action then respective action will be triggered from actions.py file and if the utterance is response then the respective utterance response(“Bye”) will be executed as per domain.yml file.
In the same way, If the user enters as “hello”, the Rasa predicts the intent as “greet” from nlu.md file and the action “find_language_types” will be activated as per stories.md file which we will be implementing now in actions.py.
Step 5:
actions.py contains code for custom actions. If your bot wants to do some action when a specific intent is triggered then you can implement the custom actions in actons.py file. Whereas, if your bot wants to display a simple response then you can manage these responses in the domain.yml file.
The above function displays buttons to choose available language movies. You can call the API to fetch the data, instead of considering the Languages dictionary,
So, if you run the application and enter a message “hi”, then “greet” intent will be identified by Rasa and then “find_language_types” action will be executed and displays buttons as below.
Let’s assume, If you click on “telugu”, then the “/inform{“language”: “telugu”}”(which we have appended the payload to the buttons in the find_language_types action) intent will be identified and then corresponding action “find_movie_names” will be called.
Step 6:
Credentials file contains credentials of various platforms in which your bot is running.We run our bot in a simple webapage which uses socket connection to connect the bot. A Socket is helpful to send/receive messages and it is a bi-directional protocol which listens continuously.
To run our bot through socket channel add the below code.
Step 7:
endpoints.yml: It contains different endpoints your bot can use. We will tell the bot to listen our actions from the host — localhost, through the port — 5055 and /webhook is the endpoint. This can be done by adding the below code into the endpoints file.
Step 8:
Config.yml: Configurations related to NLU and the Rasa core is contained in this file. It also contains different pipelines which are used to predict the specific intent and extract the entities. To respond to how and what the next action should be we use policies.
Step 9:
Run the bot with the following command.
Slot List Rasa Tv
So, our bot will run in http://localhost:5005with the above command.
Slot List Rasa Example
And simultaneously run the rasa actions with the below command
By running below command you can test your bot with Rasa UI and click on top left icon talk to your bot.
rasa x
Hence, can integrate your bot into any webpage or into your existing website.
So finally, you can build a simple movie ticket booking bot by following with these steps.