readme.md

Google Cloud / Dialogflow - Banking Portal Demo

By Lee Boonstra, Developer Advocate @ Google Cloud.

This demo, showcases a dummy banking portal. It exists of the following containers:

• Web Front-end - An Angular app (front-end folder)
• Dialogflow SDK - A Node JS app (chatserver folder)
• CMS - A Python/Django app (server folder)

When a customer writes text into the chatbot, the Dialogflow agent matches the answer. It also pushes the contents to Pub/Sub.

A cloud function has a subscription on the Pub/Sub channel. See also the cloudfunctions folder.

Everytime a message comes in, the message will be passed to the NLP API to detect the sentiment, DLP API to remove sensitive information and to BigQuery. We run the BigQuery queries in a real-time dashboard on the front-end. The bq folder contains sample the queries.

On top of this, this project contains an continious integration flow. Conversational designers can work in a Dev Dialogflow Agent. Changes made, can be pushed to a Test Dialogflow Agent, by copying the contents in Cloud Storage, which will show the differences in a test screen on the front-end. From there, you can run unit tests on the conversations, before deploying it to the Production Dialogflow Agent.

• Video Banking Portal Demo

Disclaimer: This example is made by Lee Boonstra. Written code can be used as a baseline, it's not meant for production usage.

Copyright 2018 Google LLC. This software is provided as-is, without warranty or representation for any use or purpose. Your use of it is subject to your agreements with Google.

Automatic Setup on Google Cloud Platform:

Guided one click installation from Google Cloud Shell. No client tooling required.

Manual Setup / Run Locally

Setup Google Cloud

1. Download and install the Google Cloud SDK, which includes the gcloud command-line tool.

2. Open the Google Cloud Console: http://console.cloud.google.com

3. Make sure a Billing Account is setup & linked. (Select Billing in Main Menu)

4. Create a new Google Cloud Platform project from the Cloud Console or use an existing one.

   Click the + icon in the top bar. Enter an unique project name. For example: yourname-examples. It will take a few minutes till everything is ready.

5. Initialize the Cloud SDK:

    $ gcloud init
    2 (Create a new configuration)
    yourname-examples
    (login)
    list
    #number-of-choice
    y
   

6. Install Kubectl: gcloud components install kubectl

Authentication

Authentication is typically done through Application Default Credentials, which means you do not have to change the code to authenticate as long as your environment has credentials. You have a few options for setting up authentication:

1. When running locally, use the Google Cloud SDK

    gcloud auth application-default login
   

   Note that this command generates credentials for client libraries. To authenticate the CLI itself, use:

    gcloud auth login
   

2. You can create a Service Account key file. This file can be used to authenticate to Google Cloud Platform services from any environment. To use the file, set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path to the key file, for example:

    export GOOGLE_APPLICATION_CREDENTIALS=/path/to/service_account.json
   

• Application Default Credentials
• Additional scopes
• Service Account key file

Install Dependencies

1. Install Node and npm if you do not already have them.

2. Install Python3.6: brew install python3

3. Install pip: sudo easy_install pip3

4. Install virtualenv: sudo pip3 install virtualenv

5. Install Angular CLI - npm install -g @angular-cli

Setup Chatbase

1. Navigate to http://www.chatbase.com/bots and login
2. Create a new bot
3. Copy the API_KEY to env.txt into the MY_CHATBASE_KEY variable.

Setup Cloud Resources

1. (Optional) In case you only want to run the demo locally, you can comment out the part of GKE in the setup.sh script.
2. Create a Google Cloud Project, and assign a Billig Account to it.
3. Run: gcloud config set project <cloud project id>
4. Run: . setup.sh

Remove Cloud Resources

Done with the demo, and you want to tear down all your Cloud resources?

1. Run: . teardown.sh

Run the code locally

Run Django CMS

1. In case you want to run this for the first time:

virtualenv -p python3 myenv
source myenv/bin/activate .
chmod +x /usr/local/bin/django-admin.py
pip3 install -r requirements.txt
python3 manage.py migrate
python3 manage.py createsuperuser

2. Run on the command-line:

cd server/
source myenv/bin/activate .
python3 manage.py runserver 8080

3. SSH into the Django POD and run the following commands:

python manage.py migrate python manage.py collectstatic python manage.py createsuperuser exit;

Django can be reached via http://localhost:8080

Run the Front-end Container

Run on the command-line:

cd front-end
npm install
ng serve

The Front-end can be reached via http://localhost:4200

Run the ChatServer Container

In case you want to run this for the first time:

1. Rename the file from the command-line, and edit:

   cd ../chatserver/my-app/
   npm install
   mv env.txt .env
   nano .env
   

2. Modify the code:

   GCLOUD_PROJECT=<PROJECT NAME>
   GOOGLE_APPLICATION_CREDENTIALS=<LOCATION OF YOUR SERVICE ACCOUNT FILE>
   

3. Then run on the command-line:

   npm start
   

Dialogflow Demo flow:

1. First explain the concepts of Dialogflow: http://console.dialogflow.com

Dialogflow has Natural Language Understanding (Machine Learning). The chatbots work through intent matching. The user trains user trained phrases. As soon as Dialogflow detects a match with the intent with the highest confidence level, it returns the answer.

Show the Use Case 1 - transfer money intent and explain how to setup:

• Intents (User phrases & Responses)
• Entities
• Fulfillments
• Integrations

1. Open http://localhost:4200

2. Navigate to the Support tab.

3. Use the following chatflow:

   U: I would like to transfer money
   > To which bank account number?
   U: IBAN1233435
   > How much would you like to tranfer?
   U: 100 euro
   > To which country?
   U: Germany
   > Alright! I will tranfer 100 euro to IBAN1233435 Germany.
   

4. Now navigate to the Dashboard and explain the following:

   • We have collected x amount of messages over time.
   • x amount of these messages contained negative user sentiment.
   • Explain that we will optimize our chatbot based on the feedback of our users.
   • Let's query on the session id, to figure out what went wrong, and read the transcript.
   • Explain how this was built, by showing the architecture, and mentioning all the GCP components.

5. In case you want to demonstrate the Knowledge Base Connector:

   • Show http://localhost:4200/faq/faq.html explain you want to import these FAQs in Dialogflow
   • Make sure you enabled Enable Beta features & API in the Dialogflow Settings panel.
   • Click on the Knowledge menu item.
   • Create a new Knowledge Base: Future Bank
   • Click Create the first one.
   • Document name: Bank FAQ, Knowledge type: FAQ, Mime type: text/html
   • See the below note, or use this URL: http://www.futurebank.nl/assets/html/faq/faq.html
   • Click Save, Dialogflow will crawl your page, and index Question & Answer pairs.
   • Click Add response
   • Give it the following reponse: $Knowledge.Answer[1]
   • Click Save, wait till it got trained, and test with the following question:

   U: When did they form the Future Bank?
   >The Future Bank is a conceptional demo project. 
   If Google were to start a bank, what would it look like?
   

NOTE: In order to make the KB Connector work with your own FAQ pages, you will need a public available HTML website with (server generated) HTML. The website will need to allow Google Robots, and needs to be added to the Google search engine. (pages.github won't work.) You will need more than one Q&A pair and not more than 200. It helps when you use valid HTML5 markup for your Q&As, and base it on schema.org notations. See the markup of: https://github.com/savelee/kube-django-ng/blob/master/front-end/src/assets/html/faq/faq.html for a good overview.