naveenpolamredditr/nlp_bot_restaurant

a restaurant chatbot using Rasa bot framework

Restaurant Bot

a restaurant chatbot using open source chat framework RASA. Integrates with Zomato API to fetch restaurant information.

Pre-requisites

• python 3.7.0
• rasa 1.1.4
• spacy 2.1.4
• en_core_web_md 2.1.0

Installation

RASA

Refer to official installation guide to install RASA

Spacy

1. Package Installation

   pip install -U spacy

2. Model Installation

   python -m spacy download en_core_web_md

3. Create custom shortcut link to Spacy model

   python -m spacy link en_core_web_md en

Repo Information

This repo contains training data and script files necessary to compile and execute this restaurant chatbot. It comprises of the following files:

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
  • zomato_test.py : contains 3 test functions for Zomato API - location details, cuisine details and restaurant search
• action_api_test.py : executes Zomato API test functions from command line
• actions_server.py : contains code to start a RASA actions server. This is used to serve RASA custom actions.
• 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
• nlu_train.py : contains code to
  • train a NLU model
  • persist NLU model in 'models' folder
  • run CLI to interact with generated model and validate extracted intents and entities
• rasa_slack.py : contains code to integrate with slack channel
• 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
  • start CLI interface to interact with chatbot (RASA action server must be running for this to work)

Config Files

• config.yml contains model configuration and custom policy
• credentials.yml contains authentication token to connect with channels like slack
• 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) .

Usages

• Train ONLY NLU model and validate

  python nlu_train.py --shell

  This will generate restaurant-nlu-model.tar.gz inside models folder and start an interactive shell

• Test generated NLU model (generated NLU model should be available in 'models' folder prior to test). 2 options are available:

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

  python nlu_test.py --type DEFAULT
  
  python nlu_test.py --type VALIDATION

• Run RASA action server ( mandatory step to interact with chatbot) on port 5055

  python actions_server.py

  Equivalent RASA CLI command

  rasa run actions

• Train RASA NLU and Core model

  python rasa_train.py

  This will generate restaurant-rasa-model.tar.gz inside models folder

  Equivalent RASA CLI command

  rasa train

• Starts an interactive session with restaurant chatbot

  python rasa_train.py --shell

  This step will recreate RASA NLU and Core models

  Equivalent RASA CLI command

  rasa shell

• Run RASA server to connect slack channel

  rasa run -m models -p 5004 --connctor slack --credentials credentials.yml

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