Rasa-Chatbot

A chatbot that takes user queries and returns the best restaurants in the area using Zomato API

Setup

if you have anaconda installed in your system, please run this command to activate the environment:

conda env create -f environment.yml

or just refer to requirements.txt for package version information, Python3.7.9 is used.

Mandatory to install these Spacy Models:

python3 -m spacy download en_core_web_md
python3 -m spacy link en_core_web_md en

Data Files

data/nlu/nlu.md : contains training examples for the NLU model

data/core/stories.md : contains training stories for the Core model

Script Files

zomato : Contains Zomato API integration code

zomato_api.py : Contains functions to consume common Zomato APIs like fetch location details, type of cuisines, search for restaurants, etc

actions.py : Contains the following custom actions (insert ZOMATO API key in this script file before starting RASA server)

• search restaurant
• validate location
• validate cuisine
• send email
• restart conversation
• reset slots

nlu_test.py : Contains code to test generated NLU models

rasa_train.py :

• primary script file to test chatbot from CLI. Contains code to:
• train both NLU and Core model
• persist packaged model in 'models' folder

Config Files

config.yml: Contains model configuration and custom policy

domain.yml: Defines chatbot domain like entities, actions, templates, slots

endpoints.yml: Contains the webhook configuration for custom action

smtpconfig.txt: Contains SMTP server configuration information (If using GMAIL, setup account and generate app password) .

Usage

To Invoke Chatbot in Rasa Shell

In Terminal 1:

rasa run actions

In Terminal 2:

rasa shell

To Invoke Rasa NLU Shell

rasa shell nlu

To Train the Model: (trains both nlu and core and saves the model in models folder)

python3 rasa_train.py

MODEL FILE is saved in models folder: (models/restaurant_rasa_model.tar.gz)

NOTE: Both nlu and core model are saved inside one model file, i.e models/restaurant_rasa_model.tar.gz

To Validate NLU Model Accuracy: (Classification report saved to results/intent_report.json)

* DEFAULT : separate test set is created
* VALIDATION : uses cross - validation

bash python3 nlu_test.py --type DEFAULT

bash python3 nlu_test.py --type VALIDATION