In conversations, context is king! We’ll build a chatbot framework using Tensorflow and add some context handling to show how this can be approached.
Ever wonder why most chatbots lack conversational context?
How is this possible given the importance of context in nearly all conversations?
We’re going to create a chatbot framework and build a conversational model for an island moped rental shop. The chatbot for this small business needs to handle simple questions about hours of operation, reservation options and so on. We also want it to handle contextual responses such as inquiries about same-day rentals. Getting this right could save a vacation!
We’ll be working through 3 steps:
- We’ll transform conversational intent definitions to a Tensorflow model
- Next, we will build a chatbot framework to process responses
- Lastly, we’ll show how basic context can be incorporated into our response processor
We’ll be using tflearn, a layer above tensorflow, and of course Python. As always we’ll use iPython notebook as a tool to facilitate our work.
We’ll be using tflearn, a layer above tensorflow, and of course Python. As always we’ll use iPython notebook as a tool to facilitate our work. ….