Features of the bot:

• Assists the customer in making a purchase
• Offers product recommendations
• Helps the customer in making a return, exchange or refund
• Assists the customer with credit card related issues
• Takes feedback from customer.

(This model was one of the top 26 models in Flipkart Grid 3.0 - Software development track)

To install , please clone the repo and run:

cd rasa-demo
make install

This will install the bot and all of its requirements. Note that this bot should be used with python 3.6 or 3.7.

Use rasa train to train a model (this will take a significant amount of memory to train, if you want to train it faster, try the training command with --augmentation 0).

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

rasa run actions --actions actions.actions

In another window, run the bot:

docker run -p 8000:8000 rasa/duckling
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.

If you would like to run the bot on your website, follow the instructions here to place the chat widget on your website.

After doing a rasa train, run the command:

rasa test nlu -u test/test_data.json --model models
rasa test core --stories test/test_stories.md

data/core/ - contains stories

data/nlu - contains NLU training data

actions - contains custom action code

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

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

To install requirements for development, run:

make install-dev

To run unit tests for custom actions:

make test-actions

To ensure proper database cleanup during testing, you will need to include a connection URL for your tracker store database in your .env file e.g.

TRACKER_DB_URL=postgresql:///tracker

This is not necessary for running the actions.