Worked on a artificial intelligence based conversational bot using a platform called RASA which uses Python programming language.
A Conversation chatbot understands the context of the conversation and can handle any user goal gracefully and help accomplish it as best as possible. This doesn’t always mean that the bot will be able to answer all questions but it can handle the conversation well.
Aim: to build a chatbot called capable of checking in on people’s mood and taking the necessary actions to cheer them up. We will then deploy it to Slack. It will be a fully function Slackbot capable of listening and responding to your requests.
Requirements The installation of Rasa Stack and a language model. The language model is going to be used to parse incoming text messages and extract the necessary information. We will be working with the SpaCy language model. (Check the file containing all the pre-requisites.)
Rasa Stack Rasa Stack is a set of open source machine learning tools for developers to create contextual AI assistants and chatbots. It is the leading open source machine learning toolkit that lets developers expand bots beyond answering simple questions with minimal training data. The bots are based on a machine learning model trained on example conversations.
Rasa NLU Rasa NLU: a library for natural language understanding with intent classification and entity extraction. This helps the chatbot to understand what the user is saying.
Rasa Core: a chatbot framework with machine learning-based dialogue management that predicts the next best action based on the input from NLU, the conversation history, and the training data.
Primary installations: A Rasa NLU, Rasa Core and a spaCy language model.
For other installations: Refer the pre-requisites pdf file uploaded.