You can also use the environment variable EMAIL_SENDER_PASSWORD to change the values.
#Grupo kairon password#
The password of the account which sends the confirmation mail. You can also use the environment variable EMAIL_SENDER_EMAIL to change the values. The mail id of the account which sends the confirmation mail. You can also use the environment variable APP_URL to change the values.
#Grupo kairon verification#
This url, along with a unique token is sent to the user's mail id for account verification as well as for password reset tasks. You can also use the environment variable EMAIL_ENABLE to change the values. Set value to True for enabling email verification, and False to disable. The email.yaml file can be used to configure the process for account confirmation through a verification link sent to the user's mail id. System Configuration Email verification setup Python -m uvicorn :app -host 0.0.0.0 -port 8080 Use below command for generating random secret key Set env variable SECRET_KEY to some random key. Optional, if you want to have google analytics enabled then uncomment trackingid Set env variable server to public IP of the machine where trainer api docker container is running for example: Please do the below changes in docker/docker-compose.yml Kairon only requires a recent version of Docker and Docker Compose. Our teams current focus within NLP is Knowledge Graphs – Do let us know if you are interested.Īt this juncture it layers on top of Rasa Open Source Deployment Teams who want to host the chatbot trainer in-house. One can directly access these features from our hosted website. Teams and Individuals who want an easy no-coding interface to create, train, test and deploy chatbots. This website can be found at Kairon and is hosted by Digite Inc. Model Training and Deployment from Interface.Question augmentation to auto generate questions and enrich training data.Easy to use UI for adding – editing Intents, Questions and Responses.These are the features in the 0.1 version with many more features incoming! Kairon’s aim is to provide a no-coding self service framework that helps users achieve this. This means extensive work creating intents by going through documentation, testing accuracy of responses, etc. One of the biggest problems for users is adapting contextual AI assistants to specific domain is one of the bigger problems adopting chatbots within organizations.
It also deals with the post processing and maintenance of these bots such metrics / follow-up messages etc. These include question augmentation and generation of knowledge graphs that can be used to automatically generate intents, questions and responses. Kairon on the other hand focuses on technology that deal with pre-processing of data that are needed by this framework.
While RASA focuses on technology of chatbots itself. Kairon is currently a set of tools built on the RASA framework with a helpful UI interface. It is designed to make the lives of those who work with ai-assistants easy, by giving them a no-coding web interface to adapt, train, test and maintain such assistants. Kairon is envisioned as a web based microservices driven suite that helps train Rasa contextual AI assistants at scale.
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