Encountered Error When Trying A Workflow_intent_classifier Example

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Introduction

In this article, we will explore the issue of encountering an error when trying to run a workflow intent classifier example using the MCP Agent. The MCP Agent is a powerful tool for building and deploying conversational AI models, but it can be challenging to use, especially for beginners. In this article, we will walk through the code provided and identify the issue that is causing the error.

Code Review

The code provided is a simple example of how to use the MCP Agent to classify intents using a workflow. The code is written in Python and uses the asyncio library to run the example asynchronously.

import asyncio

from rich import print

from mcp_agent.app import MCPApp
from mcp_agent.workflows.intent_classifier.intent_classifier_base import Intent
from mcp_agent.workflows.intent_classifier.intent_classifier_llm_openai import (
    OpenAILLMIntentClassifier,
)

app = MCPApp(name="intent_classifier")


async def example_usage():
    async with app.run() as intent_app:
        logger = intent_app.logger
        context = intent_app.context
        logger.info("Current config:", data=context.config.model_dump())

        intent_classifier = OpenAILLMIntentClassifier(
            intents=[
                Intent(
                    name="greeting",
                    description="A friendly greeting",
                    examples=["Hello", "Hi there", "Good morning"],
                ),
                Intent(
                    name="farewell",
                    description="A friendly farewell",
                    examples=["Goodbye", "See you later", "Take care"],
                ),
            ],
            context=context,
        )

        results = await intent_classifier.classify(
            request="Hello, how are you?",
            top_k=1,
        )
        logger.warning("Intent classification results:", data=results)


if __name__ == "__main__":
    import time

    start = time.time()
    asyncio.run(example_usage())
    end = time.time()
    t = end - start

    print(f"Total run time: {t:.2f}s")

Error Analysis

The error message indicates that there is a TypeError when trying to initialize the OpenAILLMIntentClassifier class. The error message is:

TypeError: object.__init__() takes exactly one argument (the instance to initialize)

This error message suggests that the issue is with the way the OpenAILLMIntentClassifier class is being initialized.

Solution

After reviewing the code and the error message, it appears that the issue is with the way the OpenAILLMIntentClassifier class is being initialized. The OpenAILLMIntentClassifier class is a subclass of the IntentClassifier class, which is a subclass of the IntentClassifierBase class. The IntentClassifierBase class has an __init__ method that takes two arguments: context and kwargs.

However, in the OpenAILLMIntentClassifier class, the __init__ method is not defined correctly. The __init__ method is defined as:

def __init__(self, intents, context, **kwargs):
    super().__init__(intents=intents, context=context, **kwargs)

This is incorrect because IntentClassifierBase class's __init__ method takes only two arguments: context and kwargs. The intents argument is not a valid argument for the IntentClassifierBase class's __init__ method.

To fix this issue, the OpenAILLMIntentClassifier class's __init__ method should be defined as:

def __init__(self, context, **kwargs):
    super().__init__(context=context, **kwargs)

This will ensure that the OpenAILLMIntentClassifier class is initialized correctly.

Conclusion

In this article, we have identified the issue with the OpenAILLMIntentClassifier class's __init__ method and provided a solution to fix the issue. The solution involves changing the __init__ method to take only two arguments: context and kwargs. This will ensure that the OpenAILLMIntentClassifier class is initialized correctly and the error is resolved.

Best Practices

To avoid this issue in the future, it is essential to follow best practices when writing code. Here are some best practices to keep in mind:

  • Always review the documentation for the classes and methods you are using to ensure you understand their behavior and requirements.
  • Use a linter and a code formatter to catch errors and ensure your code is formatted correctly.
  • Test your code thoroughly to ensure it works as expected.
  • Use a debugger to step through your code and identify issues.

Introduction

In our previous article, we explored the issue of encountering an error when trying to run a workflow intent classifier example using the MCP Agent. We identified the issue with the OpenAILLMIntentClassifier class's __init__ method and provided a solution to fix the issue. In this article, we will provide a Q&A section to help you better understand the issue and how to resolve it.

Q&A

Q: What is the MCP Agent?

A: The MCP Agent is a powerful tool for building and deploying conversational AI models. It provides a simple and intuitive way to create and train AI models using a variety of techniques, including machine learning and deep learning.

Q: What is the issue with the OpenAILLMIntentClassifier class?

A: The issue with the OpenAILLMIntentClassifier class is that its __init__ method is not defined correctly. The __init__ method is defined as def __init__(self, intents, context, **kwargs):, but it should be defined as def __init__(self, context, **kwargs):.

Q: What is the correct way to initialize the OpenAILLMIntentClassifier class?

A: The correct way to initialize the OpenAILLMIntentClassifier class is by calling its __init__ method with the context argument, like this: intent_classifier = OpenAILLMIntentClassifier(context=context).

Q: What are the best practices for writing code to avoid this issue?

A: The best practices for writing code to avoid this issue include:

  • Always reviewing the documentation for the classes and methods you are using to ensure you understand their behavior and requirements.
  • Using a linter and a code formatter to catch errors and ensure your code is formatted correctly.
  • Testing your code thoroughly to ensure it works as expected.
  • Using a debugger to step through your code and identify issues.

Q: How can I troubleshoot this issue?

A: To troubleshoot this issue, you can use a debugger to step through your code and identify the line of code that is causing the error. You can also use a linter and a code formatter to catch errors and ensure your code is formatted correctly.

Q: What are some common mistakes to avoid when writing code?

A: Some common mistakes to avoid when writing code include:

  • Not reviewing the documentation for the classes and methods you are using.
  • Not using a linter and a code formatter to catch errors.
  • Not testing your code thoroughly.
  • Not using a debugger to step through your code and identify issues.

Conclusion

In this article, we have provided a Q&A section to help you better understand the issue with the OpenAILLMIntentClassifier class and how to resolve it. We have also provided best practices for writing code to avoid this issue and common mistakes to avoid when writing code. By following these best practices and avoiding common mistakes, you can write high-quality code that is easy to maintain and debug.

Additional Resources

Related Articles

  • "Encountered Error When Trying a Workflow Intent Classifier Example" (previous article)
  • "MCP Agent Tutorial: Building a Conversational AI Model" (upcoming article)