Openai

zenml.integrations.openai

Initialization of the OpenAI integration.

Attributes

OPEN_AI = 'openai' module-attribute

Classes

Integration

Base class for integration in ZenML.

Functions

activate() -> None classmethod

Abstract method to activate the integration.

Source code in src/zenml/integrations/integration.py

@classmethod
def activate(cls) -> None:
    """Abstract method to activate the integration."""

check_installation() -> bool classmethod

Method to check whether the required packages are installed.

Returns:

Type	Description
bool	True if all required packages are installed, False otherwise.

Source code in src/zenml/integrations/integration.py

@classmethod
def check_installation(cls) -> bool:
    """Method to check whether the required packages are installed.

    Returns:
        True if all required packages are installed, False otherwise.
    """
    for requirement in cls.get_requirements():
        parsed_requirement = Requirement(requirement)

        if not requirement_installed(parsed_requirement):
            logger.debug(
                "Requirement '%s' for integration '%s' is not installed "
                "or installed with the wrong version.",
                requirement,
                cls.NAME,
            )
            return False

        dependencies = get_dependencies(parsed_requirement)

        for dependency in dependencies:
            if not requirement_installed(dependency):
                logger.debug(
                    "Requirement '%s' for integration '%s' is not "
                    "installed or installed with the wrong version.",
                    dependency,
                    cls.NAME,
                )
                return False

    logger.debug(
        f"Integration '{cls.NAME}' is installed correctly with "
        f"requirements {cls.get_requirements()}."
    )
    return True

flavors() -> List[Type[Flavor]] classmethod

Abstract method to declare new stack component flavors.

Returns:

Type	Description
List[Type[Flavor]]	A list of new stack component flavors.

Source code in src/zenml/integrations/integration.py

@classmethod
def flavors(cls) -> List[Type[Flavor]]:
    """Abstract method to declare new stack component flavors.

    Returns:
        A list of new stack component flavors.
    """
    return []

get_requirements(target_os: Optional[str] = None, python_version: Optional[str] = None) -> List[str] classmethod

Method to get the requirements for the integration.

Parameters:

Name	Type	Description	Default
target_os	Optional[str]	The target operating system to get the requirements for.	None
python_version	Optional[str]	The Python version to use for the requirements.	None

Returns:

Type	Description
List[str]	A list of requirements.

Source code in src/zenml/integrations/integration.py

@classmethod
def get_requirements(
    cls,
    target_os: Optional[str] = None,
    python_version: Optional[str] = None,
) -> List[str]:
    """Method to get the requirements for the integration.

    Args:
        target_os: The target operating system to get the requirements for.
        python_version: The Python version to use for the requirements.

    Returns:
        A list of requirements.
    """
    return cls.REQUIREMENTS

get_uninstall_requirements(target_os: Optional[str] = None) -> List[str] classmethod

Method to get the uninstall requirements for the integration.

Parameters:

Name	Type	Description	Default
target_os	Optional[str]	The target operating system to get the requirements for.	None

Returns:

Type	Description
List[str]	A list of requirements.

Source code in src/zenml/integrations/integration.py

@classmethod
def get_uninstall_requirements(
    cls, target_os: Optional[str] = None
) -> List[str]:
    """Method to get the uninstall requirements for the integration.

    Args:
        target_os: The target operating system to get the requirements for.

    Returns:
        A list of requirements.
    """
    ret = []
    for each in cls.get_requirements(target_os=target_os):
        is_ignored = False
        for ignored in cls.REQUIREMENTS_IGNORED_ON_UNINSTALL:
            if each.startswith(ignored):
                is_ignored = True
                break
        if not is_ignored:
            ret.append(each)
    return ret

plugin_flavors() -> List[Type[BasePluginFlavor]] classmethod

Abstract method to declare new plugin flavors.

Returns:

Type	Description
List[Type[BasePluginFlavor]]	A list of new plugin flavors.

Source code in src/zenml/integrations/integration.py

@classmethod
def plugin_flavors(cls) -> List[Type["BasePluginFlavor"]]:
    """Abstract method to declare new plugin flavors.

    Returns:
        A list of new plugin flavors.
    """
    return []

OpenAIIntegration

Bases: Integration

Definition of OpenAI integration for ZenML.

Modules

hooks

Initialization of the OpenAI hooks module.

Functions

Modules

open_ai_failure_hook

Functionality for OpenAI standard hooks.

Classes Functions

openai_alerter_failure_hook_helper(exception: BaseException, model_name: str) -> None

Standard failure hook that sends a message to an Alerter.

Your OpenAI API key must be stored in the secret store under the name "openai" and with the key "api_key".

Parameters:

Name	Type	Description	Default
exception	BaseException	The exception that was raised.	required
model_name	str	The OpenAI model to use for the chatbot.	required

This implementation uses the OpenAI v1 SDK with automatic retries and backoff.

Source code in src/zenml/integrations/openai/hooks/open_ai_failure_hook.py

def openai_alerter_failure_hook_helper(
    exception: BaseException,
    model_name: str,
) -> None:
    """Standard failure hook that sends a message to an Alerter.

    Your OpenAI API key must be stored in the secret store under the name
    "openai" and with the key "api_key".

    Args:
        exception: The exception that was raised.
        model_name: The OpenAI model to use for the chatbot.

    This implementation uses the OpenAI v1 SDK with automatic retries and backoff.
    """
    client = Client()
    context = get_step_context()

    # get the api_key from the secret store
    try:
        openai_secret = client.get_secret(
            "openai", allow_partial_name_match=False
        )
        openai_api_key: Optional[str] = openai_secret.secret_values.get(
            "api_key"
        )
    except (KeyError, NotImplementedError):
        openai_api_key = None

    alerter = client.active_stack.alerter
    if alerter and openai_api_key:
        # Capture rich traceback
        output_captured = io.StringIO()
        original_stdout = sys.stdout
        sys.stdout = output_captured
        console = Console()
        console.print_exception(show_locals=False)

        sys.stdout = original_stdout
        rich_traceback = output_captured.getvalue()

        # Initialize OpenAI client with timeout and retry settings
        openai_client = OpenAI(
            api_key=openai_api_key,
            max_retries=3,  # Will retry 3 times with exponential backoff
            timeout=60.0,  # 60 second timeout
        )

        # Create chat completion using the new client pattern
        response = openai_client.chat.completions.create(
            model=model_name,
            messages=[
                {
                    "role": "user",
                    "content": f"This is an error message (following an exception of type '{type(exception)}') "
                    f"I encountered while executing a ZenML step. Please suggest ways I might fix the problem. "
                    f"Feel free to give code snippets as examples, and note that your response will be piped "
                    f"to a Slack bot so make sure the formatting is appropriate: {exception} -- {rich_traceback}. "
                    f"Thank you!",
                }
            ],
        )

        suggestion = response.choices[0].message.content

        # Format the alert message
        message = "\n".join(
            [
                "*Failure Hook Notification! Step failed!*",
                "",
                f"Run name: `{context.pipeline_run.name}`",
                f"Step name: `{context.step_run.name}`",
                f"Parameters: `{context.step_run.config.parameters}`",
                f"Exception: `({type(exception)}) {exception}`",
                "",
                f"*OpenAI ChatGPT's suggestion (model = `{model_name}`) on how to fix it:*\n `{suggestion}`",
            ]
        )

        alerter.post(message)
    elif not openai_api_key:
        logger.warning(
            "Specified OpenAI failure hook but no OpenAI API key found. Skipping..."
        )
    else:
        logger.warning(
            "Specified OpenAI failure hook but no alerter configured in the stack. Skipping..."
        )

openai_chatgpt_alerter_failure_hook(exception: BaseException) -> None

Alerter hook that uses the OpenAI ChatGPT model.

Parameters:

Name	Type	Description	Default
exception	BaseException	The exception that was raised.	required

Source code in src/zenml/integrations/openai/hooks/open_ai_failure_hook.py

def openai_chatgpt_alerter_failure_hook(
    exception: BaseException,
) -> None:
    """Alerter hook that uses the OpenAI ChatGPT model.

    Args:
        exception: The exception that was raised.
    """
    openai_alerter_failure_hook_helper(exception, "gpt-3.5-turbo")

openai_gpt4_alerter_failure_hook(exception: BaseException) -> None

Alerter hook that uses the OpenAI GPT-4 model.

Parameters:

Name	Type	Description	Default
exception	BaseException	The exception that was raised.	required

Source code in src/zenml/integrations/openai/hooks/open_ai_failure_hook.py

def openai_gpt4_alerter_failure_hook(
    exception: BaseException,
) -> None:
    """Alerter hook that uses the OpenAI GPT-4 model.

    Args:
        exception: The exception that was raised.
    """
    openai_alerter_failure_hook_helper(exception, "gpt-4o")