class SpanHandler(ABC):
    """
    Abstract base class for handling spans in LangfuseTracer.
    Implement this class to customize how spans are processed after they are yielded.
    """
    
    @abstractmethod
    def handle(self, span: LangfuseSpan, component_type: Optional[str]) -> None:
        """
        Handle a span after it has been yielded.
        
        :param span: The LangfuseSpan that was yielded
        :param component_type: The type of the component that created this span
        """
        pass

class DefaultSpanHandler(SpanHandler):
    """Default implementation of SpanHandler that provides the original Langfuse tracing behavior."""
    
    def handle(self, span: LangfuseSpan, component_type: Optional[str]) -> None:
        if component_type in _SUPPORTED_GENERATORS:
            meta = span._data.get(_COMPONENT_OUTPUT_KEY, {}).get("meta")
            if meta:
                m = meta[0]
                span._span.update(usage=m.get("usage") or None, model=m.get("model"))
        elif component_type in _SUPPORTED_CHAT_GENERATORS:
            replies = span._data.get(_COMPONENT_OUTPUT_KEY, {}).get("replies")
            if replies:
                meta = replies[0].meta
                completion_start_time = meta.get("completion_start_time")
                if completion_start_time:
                    try:
                        completion_start_time = datetime.fromisoformat(completion_start_time)
                    except ValueError:
                        logger.error(f"Failed to parse completion_start_time: {completion_start_time}")
                        completion_start_time = None
                span._span.update(
                    usage=meta.get("usage") or None,
                    model=meta.get("model"),
                    completion_start_time=completion_start_time,
                )

class CustomSpanHandler(SpanHandler):
    def handle(self, span: LangfuseSpan, component_type: Optional[str]) -> None:
        # Custom logic for handling spans
        if component_type == "MyCustomChatGenerator":
            output = span._data.get("haystack.component.output", {})
            span._span.update(
                usage=output.get("usage"),
                model="my-custom-model",
                custom_metadata=output.get("extra_info"),
                # Add any other custom metadata
            )

# Use in pipeline
connector = LangfuseConnector(
    name="My Pipeline",
    span_handler=CustomSpanHandler()
)