deepset-ai/haystack · error · ValueError

Pipeline must be provided to SuperComponent.

Error message

Pipeline must be provided to SuperComponent.

What it means

SuperComponent wraps an existing Pipeline, so a pipeline is mandatory. Constructing SuperComponent without the pipeline argument raises ValueError.

Source

Thrown at haystack/core/super_component/super_component.py:67

            ```python
            input_mapping={
                "query": ["retriever.query", "prompt_builder.query"],
            }
            ```
        :param output_mapping: A dictionary mapping pipeline output socket paths to component output names.
            If not provided, a default output mapping will be created based on all pipeline outputs.
            Example:
            ```python
            output_mapping={
                "retriever.documents": "documents",
                "generator.replies": "replies",
            }
            ```
        :raises InvalidMappingError: Raised if any mapping is invalid or type conflicts occur
        :raises ValueError: Raised if no pipeline is provided
        """
        if pipeline is None:
            raise ValueError("Pipeline must be provided to SuperComponent.")

        self.pipeline: Pipeline = pipeline

        # Determine input types based on pipeline and mapping
        pipeline_inputs = self.pipeline.inputs()
        resolved_input_mapping = (
            input_mapping if input_mapping is not None else self._create_input_mapping(pipeline_inputs)
        )
        self._validate_input_mapping(pipeline_inputs, resolved_input_mapping)
        input_types = self._resolve_input_types_from_mapping(pipeline_inputs, resolved_input_mapping)
        # Set input types on the component
        for input_name, info in input_types.items():
            component.set_input_type(self, name=input_name, **info)

        self.input_mapping: dict[str, list[str]] = resolved_input_mapping
        self._original_input_mapping = input_mapping

        # Set output types based on pipeline and mapping

View on GitHub (pinned to e318778c9b)

Solutions

1. Build a Pipeline and pass it: SuperComponent(pipeline=pipe)
2. Check that the variable holding the pipeline is not None before constructing

Example fix

// before
super_comp = SuperComponent()
// after
pipe = Pipeline()
pipe.add_component("embedder", SentenceTransformersTextEmbedder())
super_comp = SuperComponent(pipeline=pipe)

def build_super_component(pipeline):
    if pipeline is None:
        raise ValueError("pipeline must be a Pipeline instance before creating SuperComponent")
    return SuperComponent(pipeline=pipeline)

from haystack import Pipeline
def is_pipeline(p: object) -> bool:
    return isinstance(p, Pipeline)

try:
    super_comp = SuperComponent(pipeline=pipe)
except ValueError as e:
    if "Pipeline must be provided" in str(e):
        pipe = Pipeline()
        super_comp = SuperComponent(pipeline=pipe)
    else:
        raise

When it happens

Trigger: SuperComponent() or SuperComponent(pipeline=None) called without a valid Pipeline instance.

Common situations: Refactoring code where the pipeline is built conditionally and ends up None; forgetting to pass pipeline when copying constructor examples; a factory function returning None.

Understand the failure class

Background: "missing required argument" and "the following required arguments were not provided": what required-argument errors mean and how to fix them — this error's family across 20 libraries.

Related errors

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• Missing 'type' in component '{name}'
• Successfully imported module '{module}' but couldn't find '{
• Component '{component_type}' (name: '{name}') not imported.

AI-assisted analysis of deepset-ai/haystack@e318778c9b (2026-08-30). Data as JSON: /api/errors/117f6a6a0fb34f6b. Report an issue: GitHub.