Rasa UI is a web application for the Rasa NLU backend
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README.md

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Rasa UI

Rasa UI is a web application built on top of, and for, Rasa NLU and Rasa Core. Rasa UI provides a web application to quickly and easily be able to create agents, define intents and entities. It also provides some convenience features for Rasa NLU, like training your models, monitoring usage or viewing logs. Our goal is to replace api.ai/Dialogflow with Rasa, so a lot of the terminology and usage concepts are similar.

Features

  • UI for feeding training data- Intents, Entities, Synonyms, Regex, Actions, Stories to DB and Testing endpoints.
  • Log requests for usage tracking, history, improvements
  • Easily execute intent parsing using different models
  • Manage Multiple Agents in one place with shared NLU instances.
  • Webhook option for Agents to fetch response text and send back to bots.
  • Authentication module can be extended to a different IDP and session is handled by JWT token
  • Webhooks also receive User information part of JWT Token in the Bearer Authorization Header
  • User level Tracking of conversations
  • Insights to show the frequently used intents and their confidence %
  • Import Agents in rasa_nlu format
  • Docker container capabilities

Screenshot1

Screenshot1

Getting Started

Rasa UI can run directly on your Rasa NLU instance, or on a separate machine. Technically Rasa NLU is not required, you could just use the UI for managing training data.

Prerequisites

Rasa NLU - Version 0.14+

Rasa Core - Version 0.12+

PostgreSQL - Used for storing training data (entities, intents, synonyms, etc.)

Node.js/npm - Serves Rasa UI and acts as a middleware server for logging (to the PostgreSQL DB)

Installing

Please ensure prerequisites are fulfilled

Clone/download the Rasa UI repository. Install npm packages for both Server and Web.

git clone https://github.com/paschmann/rasaui.git
cd rasaui && npm install

Please see the wiki for more detailed instructions.

Docker Setup

Quick start

docker pull paschmann/rasa-ui and browse to localhost:5001

Full Build

The Docker file uses Multi Stage Build feature, ensure that your docker version is greater or equals to 17.05. In order to run this setup in docker you need to run the following command to build out the image:

docker build -t rasa-ui . - Make sure to perform this from the location where the Dockerfile is.

Now we can spin up our docker instance with the following command:

Use Your External Rasa Server In this command we are setting the env variables rasanluendpoint and rasacoreendpoint to our own specific values, you can supply only 1 or both of these depending on if you want to use NLU or Core or both externally.

docker run -e "rasanluendpoint=http://youripaddress:5000" -e "rasacoreendpoint=http://youripaddress:5005" -e "postgresserver=postgres://login:password@serveraddress:5432/rasa" -itd -p 5001:5001 rasa-ui

It's possible to fix the nlu model name for the training by passing "rasanlufixedmodelname" as an argument :

docker run -e "rasanluendpoint=http://youripaddress:5000" -e "rasacoreendpoint=http://youripaddress:5005" -e "postgresserver=postgres://login:password@serveraddress:5432/rasa" -e "rasanlufixedmodelname=nlu" -itd -p 5001:5001 rasa-ui

Docker compose

If you want to quickly load all the stack locally you can use the docker-compose file

docker-compose up

On the first launch, you have to add add your rasa configurations and training files in this filetree:

rasa-app-data
├── actions
│   ├── __pycache__
│   │   └── actions.cpython-36.pyc
│   └── actions.py
├── config
│   └── endpoints.yml
├── logs
├── models
│   └── current
│       └── dialogue
│           ├── domain.json
│           ├── domain.yml
└── project
    ├── domain.yml
    └── stories.md

Then launch the model training if it's not already done:

docker-compose run rasa_core train

And setup SQL database schema.

DB Setup

If the rasa UI Postgres user is different from the postgres database admin used for database creation, ensure it is created before the execution of the script CREATE USER <RASA_UI_DATABASE_USER> WITH PASSWORD '<RASA_UI_DATABASE_PWD>'

Flyway install

You can install the RASA UI database using Flyway - simply run a docker container with these options.

docker run --rm --mount type=bind,source=<PATH_TO_MIGRATION_FOLDER>,target=/flyway/sql \
 boxfuse/flyway -url=jdbc:postgresql://<POSTGRES_SERVER_URL>/<RASA_UI_DB> -user=<POSTGRES_ADMIN_USER> -password=<POSTGRES_ADMIN_PASSWORD> -schemas=rasa_ui,public -placeholders.postgres_user=<RASA_UI_DATABASE_USER>  migrate

This will create a flyway_schema_history table which will track the database state, and allow you to simplify database model migrations.

Manual install

Please specify the value of the postgres_user parameter with your Rasa Postgres User, using psql : psql -v postgres_user=<RASA_UI_DATABASE_USER> -h <POSTGRES_SERVER_URL> -U <POSTGRES_ADMIN_USER> -d <RASA_UI_DB> -a -f dbcreate.sql. If this is a clean install, simply execute dbcreate.sql on postgreSQL. If you are upgrading from a previous version, please execute the migration scripts sequentially to bring your DB model up to date.

RasaNLU Setup

  • Update your package.json file to include the IP Addresses of your rasa_nlu server and the connection string of your postgres instance.
  • Optional: Update your web/src/app.js file to include the IP Addresses of your local middleware server (no need to change this if they are running on the same instance)

Running

Run npm start from the server folder (rasa-ui)

npm start

Your web application should be available on http://localhost:5001

Logging

Since Rasa UI can be used to log events/intent parsing/training etc. we would suggest changing your endpoints for your API calls to "pass through" the Rasa UI middleware layer. All API requests are simply forwarded, logged and then returned.

e.g. Instead of calling: http://localhost:5000/parse?q=hello%20there rather call: http://localhost:5001/api/v2/rasa/parse?q=hello%20there

Contributing

Please read contributing.md for details on our code of conduct, and the process for submitting pull requests to us.

Contributers

  • Paul Aschmann
  • Pradeep Mamillapalli

License

This project is licensed under the MIT License - see the license file for details