This bot can help you find your order,start a return, subscribe to our e-mails, find the best promotions and discounts,even see if we carry your desired shoes and cancel your ongoing order. Built using Rasa 2.3.1 This repo contains the chatbot

Run:

pip install -r requirements.txt

Use rasa train to train a model.

Then, to run, first set up your action server in one terminal window:

rasa run actions

Then, to run the server in another terminal window:

rasa run

In another window, run the duckling server (for entity extraction):

docker run -p 8000:8000 rasa/duckling

Then to connect the rasa model through Aimybox to run the voicebot(in another terminal window):

ngrok http 5005

To talk to your rasa model as a chatbot run:

rasa shell --debug

Note that --debug mode will produce a lot of output meant to help you understand how the bot is working under the hood. To simply talk to the bot, you can remove this flag.

data/stories.yml - contains stories

data/nlu.yml - contains NLU training data

data/rules.yml - contains the rules upon which the bot responds to queries

actions/actions.py - contains custom action/api code

domain.yml - the domain file, including bot response templates

config.yml - training configurations for the NLU pipeline and policy ensemble

tests/test_stories.yml - end-to-end test stories

1. Check the status of an order
2. Return an item
3. Cancel an item
4. Search a product inventory for shoes
5. Subscribe to product updates
6. Register for the Flipkart Axis credit card
7. Pay credit card bills

The main flows have the bot retrieving or changing information in a SQLite database (the file example.db). You can use initialize.db to change the data that exists in this file.

For the purposes of illustration, the bot has orders for the following contact numbers:

• [email protected]
• [email protected]
• [email protected]

And these are the shoes that should show as in stock (size, color):

inventory = [(7, 'blue'),
             (8, 'blue'),
             (9, 'blue'),
             (10, 'blue'),
             (11, 'blue'),
             (12, 'blue'),
             (7, 'black'),
             (8, 'black'),
             (9, 'black'),
             (10, 'black')
            ]

You can test the bot on test conversations by running rasa test. This will run end-to-end testing on the conversations in tests/test_stories.yml.

Note that if duckling isn't running when you do this, you'll see some failures.

To deploy this bot, it is highly recommended to make use of the one line deploy script for Rasa X. As part of the deployment, you'll need to set up git integration to pull in your data and configurations, and build or pull an action server image.

You will need to have docker installed in order to build the action server image. If you haven't made any changes to the action code, you can also use the public image on Dockerhub instead of building it yourself.

It is recommended to use an automated CI/CD process to keep your action server up to date in a production environment.