error=Error communicating with OpenAI. - 3.8

I am trying to just run the Tutorial. Tried with 3.80 and 3.81 and it I get this Error.

I run exactly the same Tutorial with rasa-plus (3.7x) and it is working

2024-05-26 08:20:23 INFO rasa.model_training - [info ] Your Rasa model is trained and saved at ‘models/20240526-082021-full-drone.tar.gz’. event_key=model_training.train.finished_training

? Do you want to speak to the trained assistant? :robot: Yes

2024-05-26 08:20:26 INFO root - Connecting to channel ‘rasa.core.channels.development_inspector.DevelopmentInspectInput’ which was specified by the ‘–connector’ argument. Any other channels will be ignored. To connect to all given channels, omit the ‘–connector’ argument.

2024-05-26 08:20:26 INFO root - Starting Rasa server on http://0.0.0.0:5005

2024-05-26 08:20:26 INFO rasa.core.processor - Loading model models/20240526-082021-full-drone.tar.gz…

2024-05-26 08:20:26 INFO rasa.dialogue_understanding.generator.llm_command_generator - [info ] llm_command_generator.flow_retrieval.enabled

/Users/srangaiah/repos/knowledge_engine/rasa_37/venv/lib/python3.10/site-packages/rasa/core/processor.py:129: UserWarning: The model metadata does not contain a value for the ‘assistant_id’ attribute. Check that ‘config.yml’ file contains a value for the ‘assistant_id’ key and re-train the model. Failure to do so will result in streaming events without a unique assistant identifier.

rasa.shared.utils.io.raise_warning(

2024-05-26 08:20:26 INFO root - Rasa server is up and running.

/Users/srangaiah/repos/knowledge_engine/rasa_37/venv/lib/python3.10/site-packages/sanic/server/websockets/impl.py:521: DeprecationWarning: The explicit passing of coroutine objects to asyncio.wait() is deprecated since Python 3.8, and scheduled for removal in Python 3.11.

done, pending = await asyncio.wait(

2024-05-26 08:20:47 ERROR rasa.dialogue_understanding.generator.command_generator - [error ] command_generator.predict.error error=Error communicating with OpenAI

Here is my config.yml

recipe: default.v1 language: en pipeline:

  • name: LLMCommandGenerator llm: model_name: gpt-4

policies:

  • name: FlowPolicy