Python: AzureChatCompletion service fails with "Missing required parameter" error

Description

When running a group chat between a critic (ChatCompletionAgent) and a data scientist (AzureAssistantAgent), the AzureChatCompletion service fails with a "Missing required parameter" error.

Error Message

("<class 'semantic_kernel.connectors.ai.open_ai.services.azure_chat_completion.AzureChatCompletion'> service failed to complete the prompt", BadRequestError('Error code: 400 - {'error': {'message': "Missing required parameter: 'messages[5].content[0].type'.", 'type': 'invalid_request_error', 'param': 'messages[5].content[0].type', 'code': 'missing_required_parameter'}}'))

Relevant Code

# Selection config
SELECTION_PROMPT: str = f"""Determine which participant takes the next turn in a conversation based on the the most recent participant.
State only the name of the participant to take the next turn.
No participant should take more than one turn in a row.

Choose only from these participants:
- {CRITIC_NAME}
- {DS_NAME}

Always follow these rules when selecting the next participant:
- After user input, it is {DS_NAME}'s turn.
- After {DS_NAME} replies, it is {CRITIC_NAME}'s turn.
- After {CRITIC_NAME} provides feedback, it is {DS_NAME}'s turn.

History:
{{{{$history}}}}"""

# Termination config
TERMINATION_KEYWORD = "yes"

TERMINATION_PROMPT: str = f"""Examine the RESPONSE and determine whether the data scientist has met the targeted performance metric based on the critic's feedback.
If the critic provides specific suggestions for improvement, the results are not satisfactory.
If no correction is suggested, the results are satisfactory.

When the results are satisfactory, respond with a single word without explanation: {TERMINATION_KEYWORD}.

RESPONSE:
{{{{$history}}}}"""

# Init the group chat
chat = AgentGroupChat(
    agents=[ds_scientist, critic],
    selection_strategy=KernelFunctionSelectionStrategy(
        function=selection_function,
        kernel=_create_kernel_with_chat_completion("selection"),
        result_parser=lambda result: (
            str(result.value[0]) if result.value is not None else ds_scientist
        ),
        agent_variable_name="agents",
        history_variable_name="history",
    ),
    termination_strategy=KernelFunctionTerminationStrategy(
        agents=[critic],
        function=termination_function,
        kernel=_create_kernel_with_chat_completion("termination"),
        result_parser=lambda result: TERMINATION_KEYWORD in str(result.value[0]).lower(),
        history_variable_name="history",
        maximum_iterations=10,
    ),
)

Steps to Reproduce

Set up a group chat with a ChatCompletionAgent and an AzureAssistantAgent
Configure the chat with KernelFunctionSelectionStrategy and KernelFunctionTerminationStrategy
Attempt to run the group chat

try:
        # Loop variables
        is_complete: bool = False  # Whether to complete the conversation
        file_ids: list[str] = []  # File ids created by the assistant to track

        # User message
        USER_MSG: str = "A brief introduction to the dataset with some visual aids."
        print("TASK:", USER_MSG)
        # Add the user message to the history
        await chat.add_chat_message(
            message=ChatMessageContent(role=AuthorRole.USER, content=USER_MSG),
        )

        while not is_complete:
            # Start the groupchat
            is_code: bool = False
            async for response in chat.invoke():
                # Whether the content is a code snippet
                if is_code != response.metadata.get("code"):
                    print()
                    print(f"{'-'* 10} CODE {'-'* 10}\n")
                    is_code = not is_code

                # Print the response
                print(
                    f"# {response.role} - {response.name or '*'}: '{response.content}'"
                )

                # Collect the file ids
                file_ids.extend(
                    [
                        item.file_id
                        for item in response.items
                        if isinstance(item, FileReferenceContent)
                    ]
                )

                print()

                # Download any image created by the Azure Assistant
                await download_response_image(ds_scientist, file_ids)
                # Then clear the list for the subsequent session
                file_ids.clear()

            # Whether the chat ended
            if chat.is_complete:
                is_complete = True

    except Exception as e:
        # Trace the error
        print(e)

    finally:
        # Clean up resources
        print("Cleaning up Azure resources...")
        if ds_scientist is not None:
            await _clean_up_resources(agent=ds_scientist, thread_id=ds_thread_id)

Expected Behavior

The group chat should run without errors, allowing the agents to communicate and complete the task.

Actual Behavior

The AzureChatCompletion service fails with a "Missing required parameter" error, specifically mentioning 'messages[5].content[0].type'. I believe the error occurs during internal group chat conversations when the turn is held by the critic.

Are you able to turn on debug logging and get the payload you're sending before the 400 occurs?

Unfortunately, I wasn't able to debug the logging because all events occur within the AgentGroupChat. Could you suggest a method that would allow me to observe and share the payload with you?

You can add this to your script that instantiates the group chat:

import logging

logging.basicConfig(level=logging.DEBUG)

Please be careful to replace any PII (resource names? endpoints?) so you don't communicate personal data/info.

Thanks. I thought I had to manage background debugging, but this process revealed everything happening under the hood. I believe the user message arrives and is managed without any issue; however, problems start after the chat bounces to the critic. Unfortunately, I still couldn't figure out what the main problem is.

P.S.: As soon as I substitute the AzureAssistantAgent with a ChatCompletionAgent, the issue goes away. Of course, no code executions—just a chat happens.

2025-01-29 11:09:49,926 - semantic_kernel.functions.kernel_function - INFO - Function selection succeeded.
2025-01-29 11:09:49,926 - semantic_kernel.functions.kernel_function - DEBUG - Function result: critic
2025-01-29 11:09:49,926 - semantic_kernel.functions.kernel_function - INFO - Function completed. Duration: 0.794336s
2025-01-29 11:09:49,927 - semantic_kernel.agents.group_chat.agent_chat - INFO - Invoking agent critic
2025-01-29 11:09:49,927 - semantic_kernel.agents.chat_completion.chat_completion_agent - DEBUG - [ChatCompletionAgent] Invoking AzureChatCompletion.
2025-01-29 11:09:49,939 - openai._base_client - DEBUG - Request options: {
    'method': 'post',
    'url': '/chat/completions',
    'headers': {'api-key': '<redacted>'},
    'json_data': {
        'messages': [
            {'role': 'system', 'content': 'You are an AI expert...', 'name': 'critic'},
            {'role': 'user', 'content': 'A brief introduction to the dataset with some visual aids.'},
            {'role': 'assistant', 'content': "import pandas as pd\n\n# Load the dataset\nfile_path = '<redacted_file_path>'\ndata = pd.read_csv(file_path)\n\n# Display basic information about the dataset\ndata_info = data.info()\ndata_head = data.head()\n\ndata_info, data_head", 'name': 'data-scientist'},
            {'role': 'assistant', 'content': 'The dataset consists of 891 entries with 12 columns...', 'name': 'data-scientist'},
            {'role': 'assistant', 'content': 'import matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Set the style for the plots\nsns.set(style="whitegrid")\n\n# Plot the distribution of passengers by class\n...', 'name': 'data-scientist'},
            {'role': 'assistant', 'content': [
                {'file_id': '<redacted_file_id_1>'},
                {'file_id': '<redacted_file_id_2>'},
                {'file_id': '<redacted_file_id_3>'},
                {'type': 'text', 'text': 'The visualizations provide insights into the dataset...'}
            ], 'name': 'data-scientist'}
        ],
        'model': '<redacted_model_id>',
        'stream': False
    }
}
2025-01-29 11:09:50,485 - httpx - INFO - HTTP Request: POST https://<redacted_azure_endpoint>/chat/completions?api-version=2024-09-01-preview "HTTP/1.1 400 model_error"
2025-01-29 11:09:50,486 - openai._base_client - DEBUG - HTTP Request: POST https://<redacted_azure_endpoint>/chat/completions?api-version=2024-09-01-preview "400 model_error"
2025-01-29 11:09:50,487 - openai._base_client - DEBUG - Encountered httpx.HTTPStatusError
Traceback (most recent call last):
  File ".../openai/_base_client.py", line 1623, in _request
    response.raise_for_status()
  File ".../httpx/_models.py", line 829, in raise_for_status
    raise HTTPStatusError(message, request=request, response=self)
httpx.HTTPStatusError: Client error '400 model_error' for url 'https://<redacted_azure_endpoint>/chat/completions?api-version=2024-09-01-preview'
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/400

2025-01-29 11:09:50,490 - __main__ - ERROR - An error occurred during the chat execution.
Traceback (most recent call last):
  File ".../openai_handler.py", line 87, in _send_completion_request
    response = await self.client.chat.completions.create(**settings_dict)
  ...
openai.BadRequestError: Error code: 400 - {'error': {'message': "Missing required parameter: 'messages[5].content[0].type'.", 'type': 'invalid_request_error', 'param': 'messages[5].content[0].type', 'code': 'missing_required_parameter'}}

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File ".../my_script.py", line 257, in main
    async for response in chat.invoke():
  File ".../agent_group_chat.py", line 144, in invoke
    async for message in super().invoke_agent(selected_agent):
  ...
semantic_kernel.exceptions.service_exceptions.ServiceResponseException: ("<class '...AzureChatCompletion'> service failed to complete the prompt", BadRequestError('Error code: 400 - {\'error\': {...}}'))

2025-01-29 11:09:50,494 - __main__ - INFO - Cleaning up Azure resources...
2025-01-29 11:09:51,540 - __main__ - DEBUG - Cleaned up resources for Azure Assistant.

Thanks for your response. Looks like it has to do with:

{'role': 'assistant', 'content': [
        {'file_id': '<redacted_file_id_1>'},
        {'file_id': '<redacted_file_id_2>'},
        {'file_id': '<redacted_file_id_3>'},
        {'type': 'text', 'text': 'The visualizations provide insights into the dataset...'}
    ], 'name': 'data-scientist'
}

We currently allow adding FunctionResultContent to the chat between the agents. If I am understanding correctly, your data-scientist AzureAssistantAgent created content with file_id references and it's now calling your critic (ChatCompletionAgent). Does that flow sound right?

Let me look into this further.

Evan Mattson (@moonbox3)

That's right Evan Mattson (@moonbox3) I insert the titanic.csv dataset with a request for basic intro to the dataset with some visual aids. Then, agent covers everything and returns the charts. In this scenario, the critic should provide some feedback on the response considering the task from the user. The context is simple by design for sure but my intention is to observe the communication between a ChatCompletionAgent and a AzureAssistantAgent.

Okay, thanks. Let me dig in today.

Looks like it has to do with:

{'role': 'assistant', 'content': [
        {'file_id': '<redacted_file_id_1>'},
        {'file_id': '<redacted_file_id_2>'},
        {'file_id': '<redacted_file_id_3>'},
        {'type': 'text', 'text': 'The visualizations provide insights into the dataset...'}
    ], 'name': 'data-scientist'
}

After you said that, I wanted to try giving a task without any file delivery. The conversation between the ds and the critic went well without any errors. However, something happens when the assistant provides files.

Yeah, the bug is that the file_id content doesn't have a type. With that said, we don't even need to be sending the file_id content to the Chat Completion Agent because it will have no notion of what to do with the file_id(s). I'll get a fix out soon to perform that filtering.

I believe you solved the issue, right? How can I pull that change?

I believe you solved the issue, right? How can I pull that change?

Once the PR merges to main (in-progress), you can do a temp pip install, until we get a new package released (next week):

pip install git+https://github.com/microsoft/semantic-kernel.git@main#subdirectory=python

If you use uv (or Poetry) you'd do something like:

uv pip install git+https://github.com/microsoft/semantic-kernel.git@main#subdirectory=python

If you're developing in code directly, just sync your fork with main (once the change is in).

Python: intro allowed content types in chat history channel receive. …

Hm, getting the same error message, so I am not sure if this bug has been resolved already?

  File "C:\Dev\.venv\Lib\site-packages\semantic_kernel\connectors\ai\open_ai\services\open_ai_handler.py", line 99, in _send_completion_request
    raise ServiceResponseException(
semantic_kernel.exceptions.service_exceptions.ServiceResponseException: ("<class 'semantic_kernel.connectors.ai.open_ai.services.azure_chat_completion.AzureChatCompletion'> service failed to complete the prompt", BadRequestError('Error code: 400 - {\'error\': {\'message\': "Missing required parameter: \'messages[6].content[1].type\'.", \'type\': \'invalid_request_error\', \'param\': \'messages[6].content[1].type\', \'code\': \'missing_required_parameter\'}}'))

I was trying to adapt the Magentic one example to AzureOpenAI:

# Copyright (c) Microsoft. All rights reserved.

import asyncio

from semantic_kernel.agents import (
    Agent,
    ChatCompletionAgent,
    MagenticOrchestration,
    OpenAIAssistantAgent,
    StandardMagenticManager,
    AzureAssistantAgent
)

from semantic_kernel.agents import AgentGroupChat, AzureAssistantAgent, ChatCompletionAgent
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion, AzureOpenAISettings

from azure.identity.aio import DefaultAzureCredential

from semantic_kernel.agents.runtime import InProcessRuntime
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion, OpenAISettings
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
from semantic_kernel.contents import ChatMessageContent

"""
The following sample demonstrates how to create a Magentic orchestration with two agents:
- A Research agent that can perform web searches
- A Coder agent that can run code using the code interpreter

Read more about Magentic here:
https://www.microsoft.com/en-us/research/articles/magentic-one-a-generalist-multi-agent-system-for-solving-complex-tasks/

This sample demonstrates the basic steps of creating and starting a runtime, creating
a Magentic orchestration with two agents and a Magentic manager, invoking the
orchestration, and finally waiting for the results.

The Magentic manager requires a chat completion model that supports structured output.
"""


async def agents() -> list[Agent]:
    """Return a list of agents that will participate in the Magentic orchestration.

    Feel free to add or remove agents.
    """
    research_agent = ChatCompletionAgent(
        name="ResearchAgent",
        description="A helpful assistant with access to web search. Ask it to perform web searches.",
        instructions=(
            "You are a Researcher. You find information without additional computation or quantitative analysis."
        ),
        # This agent requires the gpt-4o-search-preview model to perform web searches.
        # Feel free to explore with other agents that support web search, for example,
        # the `OpenAIResponseAgent` or `AzureAIAgent` with bing grounding.
        service=AzureChatCompletion(env_file_path=".env"),
    )

    # Create an OpenAI Assistant agent with code interpreter capability
    credential = DefaultAzureCredential()
    ai_agent_settings = AzureOpenAISettings()
    client = AzureAssistantAgent.create_client()
    code_interpreter_tool, code_interpreter_tool_resources = AzureAssistantAgent.configure_code_interpreter_tool()
    definition = await client.beta.assistants.create(
        model=ai_agent_settings.chat_deployment_name,
        name="CoderAgent",
        description="A helpful assistant that writes and executes code to process and analyze data.",
        instructions="You solve questions using code. Please provide detailed analysis and computation process.",
        tools=code_interpreter_tool,
        tool_resources=code_interpreter_tool_resources,
    )
    coder_agent = AzureAssistantAgent(
        client=client,
        definition=definition,
    )

    return [research_agent, coder_agent]


def agent_response_callback(message: ChatMessageContent) -> None:
    """Observer function to print the messages from the agents."""
    print(f"**{message.name}**\n{message.content}")


async def main():
    """Main function to run the agents."""
    # 1. Create a Magentic orchestration with two agents and a Magentic manager
    # Note, the Standard Magentic manager uses prompts that have been tuned very
    # carefully but it accepts custom prompts for advanced users and scenarios.
    # For even more advanced scenarios, you can subclass the MagenticManagerBase
    # and implement your own manager logic.
    # The standard manager also requires a chat completion model that supports
    # structured output.
    magentic_orchestration = MagenticOrchestration(
        members=await agents(),
        manager=StandardMagenticManager(chat_completion_service=AzureChatCompletion(env_file_path=".env")),
        agent_response_callback=agent_response_callback,
    )

    # 2. Create a runtime and start it
    runtime = InProcessRuntime()
    runtime.start()

    # 3. Invoke the orchestration with a task and the runtime
    orchestration_result = await magentic_orchestration.invoke(
        task=(
            "Get the list of Hungarian parliament parties and the number of seats they have in the parliament. Create a statistical analysis of the parties and their seats, "
        ),
        runtime=runtime,
    )

    # 4. Wait for the results
    value = await orchestration_result.get()

    print(f"\nFinal result:\n{value}")

    # 5. Stop the runtime when idle
    await runtime.stop_when_idle()


if __name__ == "__main__":
    asyncio.run(main())

Python: intro allowed content types in chat history channel receive. …

File ids are still being passed to chat completion agent. This is the error I am getting: semantic_kernel.exceptions.service_exceptions.ServiceResponseException: ("<class 'semantic_kernel.connectors.ai.open_ai.services.azure_chat_completion.AzureChatCompletion'> service failed to complete the prompt", BadRequestError('Error code: 400 - {'error': {'message': "Missing required parameter: 'messages[6].content[1].type'.", 'type': 'invalid_request_error', 'param': 'messages[6].content[1].type', 'code': 'missing_required_parameter'}}')) (I am using MagenticOrchestration)

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Python: AzureChatCompletion service fails with "Missing required parameter" error #10317

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Python: AzureChatCompletion service fails with "Missing required parameter" error #10317

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moonbox3 commented on Jan 28, 2025

anu43 commented on Jan 29, 2025

moonbox3 commented on Jan 29, 2025

anu43 commented on Jan 29, 2025

moonbox3 commented on Jan 29, 2025

anu43 commented on Jan 29, 2025

moonbox3 commented on Jan 30, 2025

anu43 commented on Jan 30, 2025

moonbox3 commented on Jan 30, 2025

anu43 commented on Jan 30, 2025

moonbox3 commented on Jan 30, 2025

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