This Repo is to store progress for a conversational chatbot which queries a SQLite database using the Rasa framework

1. Run BAT File in default folder

2. Run BaseWPFApp.exe

1. Run actions server for custom actions

rasa run actions

2. Run Rasa REST API to allow connection from WPF App

rasa run -m 20230722-110206-citron-rivulet.tar.gz --enable-api --cors "*" //For regular use
rasa run -m 20230722-110206-citron-rivulet.tar.gz --enable-api --cors "*" --debug //For debugging purposes

3. Run BaseWPFApp.exe

1. Run actions server

rasa run actions

2. Run Rasa Chatbot

rasa shell //Run this to view the chatbot in terminal
rasa shell --debug //run this to view debug mode in terminal

• This folder contains the WPF Frontend connected with the developed Rasa Chatbot Backend

This folder contains the implementations for custom actions.

• The implementation can be found in the actions.py file.

This folder contains the main implementations of the Chatbot.

• The nlu.yml file contains the implementations for the NLU portion of the chatbot. Here, intents such as greet and ask_product are implemented to give the bot test cases to identify certain intents of the user.
• The rules.yml file represents the pre-set rules for user inputs. Rules ensure a certain flow of events will occur upon certain user inputs.
• The stories.yml file contains the various use cases that are anticipated by the user. These give the chatbot a guide to structure conversations and allows for custom inputs to be crafted.

This folder contains the implementation of a mock database to be queried by the chatbot. The database has been implemented in SQLite and is edited using a database editor

• The connectdb.ipynb file represents a python jupyter notebook which contains the testing code for the database to ensure that data can be retrieved and is of working order.
• The database1.db file contains the implementation of the database in SQLite

This folder contains the trained models for the chatbot. Once the files have been downloaded, the chatbot can be trained by running the following command while in the file.

rasa run action
rasa shell

This file contains the implementation for the display and Memoization policy of the chatbot

This file contains the implementations for external implementations

This file contains the implementations for the responses and actions that the bot can provide a user.

This file also contains the various slots and entities that are required for the bot to identify in order to execute certain functions.

1. Implement working staff and customer modes with different privileges afforded to both accounts

• Rasa Documentation
• Querying database with Rasa Chatbot