Cannot connect to host rasa_worker

[Deprecated] Rasa X Community Edition
1

When I trained on the train model error message will appearing “Training Failed” “Your model training has failed”. After I inspect the Developer Tools → Network in the browser following error message highlighted

{“version”:“0.41.2”,“status”:“failure”,“message”:“Failed to train a Rasa model.”,“reason”:“StackTrainingFailed”,“details”:“Cannot connect to host rasa-worker:5005 ssl:default [Temporary failure in name resolution]”,“help”:null,“code”:500}

2

The first task after building a chatbot with rasa is to connect it. Now we just can’t do the same to host our chatbot as it won’t work. My eClass Portal

3

The error message you are seeing indicates that there was a problem connecting to the Rasa worker server at “rasa-worker:5005”. The error “Temporary failure in name resolution” suggests that the domain name could not be resolved to an IP address, which could be caused by various reasons such as DNS configuration issues, network connectivity problems, or the Rasa worker server being down.

Here are some steps you can try to troubleshoot and resolve the issue:

  1. Check the DNS configuration: Verify that the hostname “rasa-worker” is correctly configured and can be resolved to an IP address. You can try running the command “nslookup rasa-worker” to check if the hostname can be resolved.
  2. Check network connectivity: Ensure that there are no network connectivity issues between your machine and the Rasa worker server. You can try pinging the server to check if it’s reachable.
  3. Check the Rasa worker server: Verify that the Rasa worker server is up and running. You can try accessing the server directly in your browser using the URL “http://rasa-worker:5005” to see if it’s responding.
  4. Check the Rasa worker logs: If the above steps don’t help, you can check the Rasa worker logs to see if there are any errors or issues with the server. You can access the logs by running the command “docker logs rasa-worker”.

Once you have identified and resolved the underlying issue, you can try running the model training again to see if it completes successfully.