Databricks Bad Request Error

[Aljgutier]

What happened?

trying to run AutoGen Databricks Hello World example

Running on - Databricks LTS 14.3 ML

%pip install autogen-agentchat==0.2.40 openai==1.21.2 typing_extensions==4.11.0 --upgrade

the rest of the configurations are as specified in the example

os.environ["DATABRICKS_TOKEN"] = "dapi...."

llm_config = {
    "config_list": [
        {
            "model": "databricks-dbrx-instruct",
            "api_key": str(os.environ["DATABRICKS_TOKEN"]),
            "base_url": str(os.getenv("DATABRICKS_HOST")) + "/serving-endpoints",
        }
    ],
}

import autogen

# Create Assistant and User
assistant = autogen.AssistantAgent(name="assistant", llm_config=llm_config)

user_proxy = autogen.UserProxyAgent(name="user", code_execution_config=False)

# Initiate chat from user_proxy side
chat_result = user_proxy.initiate_chat(assistant, message="What is MLflow?")

------
user (to assistant):

What is MLflow?
-------
BadRequestError: Error code: 400 - {'error_code': 'BAD_REQUEST', 'message': 'Bad request: json: unknown field "name"\n'}

...

.../openai/_base_client.py 922
return ...

What did you expect to happen?

I expected a response like in the referenced article. Something like

Sure, I'd be happy to explain MLflow to you. MLflow is an open-source platform for managing machine learning workflows. It was developed by Databricks and was open-sourced in 2018. MLflow provides a number of features to help data scientists and machine learning engineers manage the end-to-end machine learning lifecycle, including:

1. **MLflow Tracking**: This is a logging API that allows you to record and query experiments, including code, data, config, and results.
2. **MLflow Projects**: This is a format for packaging reusable and reproducible data science code, which can be run on different platforms.
3. **MLflow Models**: This is a convention for packaging machine learning models in multiple formats, making it easy to deploy in different environments.
4. **MLflow Model Registry**: This is a central repository to store, manage, and serve machine learning models.
...

How can we reproduce it (as minimally and precisely as possible)?

Try to run the code (as in the blog post) on a Databricks cluster. Does it still work. Note, in addition to the code above, I had to add to get around the error" "OpenAI Client.init() got an unexpected keyword argument proxies"

%pip install httpx==0.27.2

AutoGen version

0.2.40

Which package was this bug in

Core

Model used

No response

Python version

3.10

Operating system

Databricks LTS 14.3 ML

Any additional info you think would be helpful for fixing this bug

No response

[ekzhu]

@tj-cycyota Could you take a look at this?