Fixing AzureOpenAI Client With ChatMessage Properties

Introduction

When working with the AzureOpenAIResponsesClient in Python, you might encounter issues when providing ChatMessage objects that include additional_properties. Specifically, the client may fail with a BadRequestError, indicating an unknown parameter. This article delves into the problem, provides a detailed explanation, and offers solutions to ensure your code functions correctly. We'll explore the differences between AzureOpenAIResponsesClient and AzureOpenAIChatClient and how they handle additional_properties. Understanding these nuances is crucial for building robust and reliable applications using Azure OpenAI services.

The main focus will be on troubleshooting the AzureOpenAIResponsesClient when it encounters ChatMessage objects containing additional_properties. This is a common issue that developers face when integrating Azure OpenAI with their applications, particularly when trying to pass metadata or custom properties along with chat messages. By the end of this article, you should have a clear understanding of why this error occurs and how to effectively resolve it, ensuring smooth communication with the Azure OpenAI service.

Understanding the Issue

The core problem lies in how the AzureOpenAIResponsesClient handles ChatMessage objects with additional_properties compared to AzureOpenAIChatClient. When you include additional_properties in a ChatMessage and use AzureOpenAIResponsesClient, it results in a BadRequestError. The error message typically states: "Unknown parameter: 'input[1].metadata'." This indicates that the AzureOpenAIResponsesClient does not correctly process the additional_properties field in the same way as the AzureOpenAIChatClient.

The AzureOpenAIChatClient is designed to handle a broader range of parameters and metadata, making it more flexible when dealing with custom properties. On the other hand, the AzureOpenAIResponsesClient seems to have stricter requirements or limitations on the parameters it accepts. This discrepancy can lead to unexpected errors when you switch between the two clients or when you try to pass messages with additional metadata through the ResponsesClient.

The error message Unknown parameter: 'input[1].metadata' suggests that the client is interpreting the additional_properties as metadata directly attached to the input, which it is not configured to handle. This is why the same message works flawlessly with AzureOpenAIChatClient, as it is designed to accommodate such metadata. To effectively use AzureOpenAIResponsesClient, you need to either avoid using additional_properties or find a workaround that allows you to pass the necessary information without triggering the error. This might involve restructuring your data or using alternative methods to convey the additional information to the client.

Sample Code and Error Reproduction

To illustrate the issue, consider the following Python code snippet:

import asyncio
from azure.identity import AzureCliCredential

from agent_framework.agents import ChatAgent, AgentRunResponse
from agent_framework.clients import AzureOpenAIChatClient, AzureOpenAIResponsesClient
from agent_framework.messages import ChatMessage

async def get_weather(city: str) -> str:
    """Simulates fetching weather data for a given city."""
    return f"{city}: Sunny with a high of 21°C.\nWould you like an hourly forecast or details like humidity, wind, or precipitation chances?"

async def example_with_chat_message_additional_props() -> None:
    
    agent = ChatAgent(
        chat_client=AzureOpenAIChatClient(credential=AzureCliCredential()),
        instructions="You are a helpful weather agent.",
        tools=get_weather,
    )

    chat_message = ChatMessage(
        role="user",
        text="What is the weather in Tokyo?",
        additional_properties={"client": "ChatClient", "nested": {"level1": {"level2": "value"}}},
    )
    response: AgentRunResponse = await agent.run(chat_message)
    print(f"Agent: {response.text}")

    print("\n====== Invoke Agent with ResponsesClient. =====\n")

    agent = ChatAgent(
        chat_client=AzureOpenAIResponsesClient(credential=AzureCliCredential()),
        instructions="You are a helpful weather agent.",
        tools=get_weather,
    )
    chat_message = ChatMessage(
        role="user",
        text="What was the last city I asked about?",
        additional_properties={"client": "ResponsesClient", "nested": {"level1": {"level2": "value"}}},
    )
    response = await agent.run(chat_message)
    print(f"Agent: {response.text}")

async def main():
    await example_with_chat_message_additional_props()

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

When this code is executed, the AzureOpenAIChatClient works as expected, but the AzureOpenAIResponsesClient throws an error. The traceback clearly indicates that the issue arises within the _inner_get_response method of the AzureOpenAIResponsesClient, specifically when calling client.responses.create. The error message, "Unknown parameter: 'input[1].metadata'," confirms that the additional_properties are not being correctly processed by the ResponsesClient.

This sample code effectively demonstrates the discrepancy between the two clients and highlights the problem developers face when trying to use additional_properties with AzureOpenAIResponsesClient. By reproducing this error, you can better understand the context in which it occurs and start exploring potential solutions or workarounds.

Analyzing the Error Message

The error message openai.BadRequestError: Error code: 400 - {'error': {'message': "Unknown parameter: 'input[1].metadata'.", 'type': 'invalid_request_error', 'param': 'input[1].metadata', 'code': 'unknown_parameter'}} provides valuable insights into the problem. The BadRequestError indicates that the request sent to the Azure OpenAI service was malformed, and the service could not understand it.

The specific message, "Unknown parameter: 'input[1].metadata'," points to the root cause: the AzureOpenAIResponsesClient does not recognize or support the metadata parameter being passed as part of the input. This is further clarified by the 'type': 'invalid_request_error' and 'code': 'unknown_parameter' fields, which confirm that the service considers this parameter invalid in the context of the ResponsesClient.

The 'param': 'input[1].metadata' part of the error message suggests that the client is trying to interpret the additional_properties as metadata attached to the input message. However, the ResponsesClient is not designed to handle arbitrary metadata in this manner. This is a critical distinction from the AzureOpenAIChatClient, which can often accommodate additional metadata or custom properties.

Understanding the error message is crucial for diagnosing and resolving the issue. It tells you exactly what the problem is (an unknown parameter), where it occurs (in the input metadata), and why it's happening (the ResponsesClient doesn't support it). With this information, you can focus on finding a workaround or alternative approach to pass the necessary information without triggering the error.

Solutions and Workarounds

1. Avoid Using additional_properties

The simplest solution is to avoid using additional_properties altogether when working with AzureOpenAIResponsesClient. If the additional information is not critical, you can omit it and ensure that your code runs without errors. This might involve restructuring your application to avoid passing metadata through chat messages or finding alternative ways to convey the necessary information.

2. Include Additional Information in the Message Text

Instead of using additional_properties, you can include the extra information directly in the text field of the ChatMessage. This approach requires careful formatting to ensure that the language model can understand and interpret the information correctly. For example, you can append the metadata to the end of the message or include it in a specific format that the model is trained to recognize.

chat_message = ChatMessage(
    role="user",
    text="What was the last city I asked about? client: ResponsesClient, nested: {'level1': {'level2': 'value'}}",
)

3. Use Environment Variables or Configuration Settings

If the additional_properties contain configuration or context-related information, consider using environment variables or configuration settings instead. This allows you to pass the necessary information to the agent without including it in the chat messages. You can then access these variables within your agent's logic to perform the required actions.

4. Create a Custom Message Class

Another approach is to create a custom message class that extends the ChatMessage class and includes the additional properties as named attributes. This allows you to pass the extra information in a structured and type-safe manner. However, you'll need to ensure that the AzureOpenAIResponsesClient can handle these custom attributes without throwing an error. This might involve modifying the client or using a custom serializer to format the message correctly.

5. Use AzureOpenAIChatClient Instead

If possible, consider using AzureOpenAIChatClient instead of AzureOpenAIResponsesClient. As demonstrated earlier, the ChatClient is more flexible and can handle additional_properties without any issues. This might be the easiest solution if you don't have specific requirements that necessitate the use of the ResponsesClient.

6. Implement a Middleware or Adapter

You can implement a middleware or adapter pattern to transform the ChatMessage object before it is passed to the AzureOpenAIResponsesClient. This allows you to extract the additional_properties, process them separately, and then pass a modified message to the client. The middleware can also handle any necessary error handling or fallback logic.

Conclusion

In conclusion, the AzureOpenAIResponsesClient failing when ChatMessage.additional_properties is provided is a common issue that can be resolved by understanding the limitations of the client and implementing appropriate workarounds. Whether it's avoiding the use of additional_properties, including the information in the message text, or using alternative methods such as environment variables or custom message classes, there are several strategies to ensure your code functions correctly.

By carefully analyzing the error message and considering the context of your application, you can choose the most suitable solution and build robust and reliable applications using Azure OpenAI services. Remember to test your code thoroughly after implementing any workaround to ensure that it behaves as expected and doesn't introduce any new issues.

For more information on Azure OpenAI services, you can visit the official Microsoft Azure documentation.