The source code in this repository shows a simple Rasa chatbot fallback mechanism, which gets relevant information from ZIR Semantic Search.

• Rasa Bot & Zir
• Setup & Play
  • Docker
  • Manual
    • Set-up Rasa
    • Set-up repo
    • Running the demo
• Customizing the bot
  • Data
  • Rasa Custom Action

Rasa is the leading conversational AI platform, for personalized conversations at scale. Developers provide the questions they expect the end customer to ask and the Rasa bot, using AI, predicts and matches what the customer intended to ask. With this information at hand, developers can easily match what the bot should respond to.

This is all well unless when the customer asks a valid question the developer did not expect, for such a scenario Rasa provides a fallback mechanism.

Although Rasa will generalize to unseen messages, some messages might receive a low classification confidence. Using Fallbacks will help ensure that these low confidence messages are handled gracefully, giving your assistant the option to either respond with a default message or attempt to disambiguate the user input.

Using the mechanism the bot can ask a fallback action and search through the reviews uploaded in Zir-AI with the help of custom actions. The relevant information is shown if available otherwise if the confidence of the question to reviews available is low, we fallback to a generic statement.

The results sum up to be this:

After Cloning this repository, you have two ways of setting up the rasa bot.

1. Use Docker to setup the bot or
2. Manually install rasa on your machine

Open your shell and from within the repository folder run the following commands to build and run the docker image

docker build . -t <YOURNAME>/rasa-bot
docker run -p 5005:5005 <YOURNAME>/rasa-bot

Afterwards, load the index.html in the www folder in your browser to talk to the bot

If you want to setup the bot manually then please follow the steps below.

If you're installing rasa on your machine, you might want to create a virtual environment to isolate the dependencies rasa requires.

Follow the rasa installation instructions or copy-paste the following commands in terminal

pip3 install -U pip
pip3 install rasa
rasa --version

This should print the rasa version similar to follow

Rasa Version      :         2.7.0
Minimum Compatible Version: 2.6.0
Rasa SDK Version  :         2.7.0
Rasa X Version    :         None
Python Version    :         3.8.5
Operating System  :         Linux
Python Path       :         /bin/python3

Windows & WSL (Windows Subsystem for Linux) works too

You should be in the cloned repository folder before running following commands. The bot uses spacy in its pipeline & requires you to have it installed. Run the following commands in shell

pip install -r requirements.txt
python3 -m spacy download en_core_web_md

This will install the spacy model en_core_web_md the bot is configured with. Now you'll need to train the rasa bot

rasa train

While running the model, you are required to run the rasa action server along with the rasa bot. So in one terminal run

rasa run actions

In another terminal, you can either play with the bot in your shell or on the browser.

To talk to the bot in shell run

rasa shell

Or to talk to the bot in the browser run

rasa run --credentials ./credentials.yml  --enable-api --auth-token XYZ123 --model ./models --endpoints ./endpoints.yml --cors "*"

Now, open the index.html in your browser to talk to the bot.

Use the following information to customize the bot and the source of data.

Data for the bot is sourced from OpinRank and reviews for Hotel Jumeirah are specifically used for this bot. Reviews are parsed from https://github.com/amin3141/zir-souffle. Use hotels.py to generate the output json and the reviews.db. Copy the generated reviews.db file to this repository.

Create a corpus on Zir AI and upload all the JSON files. Create an API key for access and note down the following information

• API key
• corpus id
• customer id

Use the above-collected information and replace the information in actions/actions.py and you're good to go.

customer_id = "1835841754"
corpus_id = 1
header = {
    "customer-id": customer_id,
    "x-api-key": "zqt_WvU_2atFHgqYxxT2sQswwIUgogI8K3QeWs0oqA"
}