SuperComponent

Wrap an entire pipeline in SuperComponent and use itas a single reusable component with simplified inputs and outputs.

Basic Information

• Type: haystack.core.super_component.super_component.SuperComponent
• Components it can connect with depend on the pipeline inputs and outputs.

Inputs

Inputs are dynamically defined based on the input_mapping configuration. By default, all pipeline inputs are exposed.

Outputs

Outputs are dynamically defined based on the output_mapping configuration. By default, all pipeline outputs are exposed.

Overview

Use SuperComponent to encapsulate a complete pipeline as a single component. This is useful for:

• Creating reusable pipeline building blocks
• Simplifying complex pipelines by hiding internal complexity
• Defining tools for agents that execute entire pipelines

With SuperComponent, you can remap input and output names, making the wrapped pipeline's interface cleaner and easier to use.

Usage Example

This is an example RAG pipeline using SuperComponent to wrap a retrieval sub-pipeline as a reusable tool:

components:
  rag_tool:
    type: haystack.core.super_component.super_component.SuperComponent
    init_parameters:
      input_mapping:
        query:
        - bm25_retriever.query
        - query_embedder.text
        - ranker.query
        - prompt_builder.query
      output_mapping:
        generator.replies: answer
        ranker.documents: documents
      pipeline:
        components:
          bm25_retriever:
            type: haystack_integrations.components.retrievers.opensearch.bm25_retriever.OpenSearchBM25Retriever
            init_parameters:
              document_store:
                type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
                init_parameters:
                  hosts:
                  index: 'default'
                  max_chunk_bytes: 104857600
                  embedding_dim: 768
                  return_embedding: false
                  method:
                  mappings:
                  settings:
                  create_index: true
                  http_auth:
                  use_ssl:
                  verify_certs:
                  timeout:
              top_k: 20
              fuzziness: 0
          query_embedder:
            type: deepset_cloud_custom_nodes.embedders.nvidia.text_embedder.DeepsetNvidiaTextEmbedder
            init_parameters:
              normalize_embeddings: true
              model: intfloat/e5-base-v2
          embedding_retriever:
            type: haystack_integrations.components.retrievers.opensearch.embedding_retriever.OpenSearchEmbeddingRetriever
            init_parameters:
              document_store:
                type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
                init_parameters:
                  hosts:
                  index: 'default'
                  max_chunk_bytes: 104857600
                  embedding_dim: 768
                  return_embedding: false
                  method:
                  mappings:
                  settings:
                  create_index: true
                  http_auth:
                  use_ssl:
                  verify_certs:
                  timeout:
              top_k: 20
          document_joiner:
            type: haystack.components.joiners.document_joiner.DocumentJoiner
            init_parameters:
              join_mode: concatenate
          ranker:
            type: deepset_cloud_custom_nodes.rankers.nvidia.ranker.DeepsetNvidiaRanker
            init_parameters:
              model: intfloat/simlm-msmarco-reranker
              top_k: 8
          prompt_builder:
            type: haystack.components.builders.prompt_builder.PromptBuilder
            init_parameters:
              template: "You are a helpful assistant answering the user's questions based on the provided documents.\nDo not use your own knowledge.\n\nProvided documents:\n{% for document in documents %}\nDocument [{{ loop.index }}]:\n{{ document.content }}\n{% endfor %}\n\nQuestion: {{ query }}\nAnswer:"
          generator:
            type: haystack.components.generators.chat.openai.OpenAIChatGenerator
            init_parameters:
              api_key:
                type: env_var
                env_vars:
                - OPENAI_API_KEY
                strict: false
              model: gpt-4o
              generation_kwargs:
                max_tokens: 1000
                temperature: 0.7
        connections:
        - sender: bm25_retriever.documents
          receiver: document_joiner.documents
        - sender: query_embedder.embedding
          receiver: embedding_retriever.query_embedding
        - sender: embedding_retriever.documents
          receiver: document_joiner.documents
        - sender: document_joiner.documents
          receiver: ranker.documents
        - sender: ranker.documents
          receiver: prompt_builder.documents
        - sender: prompt_builder.prompt
          receiver: generator.messages

  answer_builder:
    type: deepset_cloud_custom_nodes.augmenters.deepset_answer_builder.DeepsetAnswerBuilder
    init_parameters:
      reference_pattern: acm

connections:
- sender: rag_tool.answer
  receiver: answer_builder.replies
- sender: rag_tool.documents
  receiver: answer_builder.documents

inputs:
  query:
  - "rag_tool.query"
  - "answer_builder.query"

outputs:
  documents: "rag_tool.documents"
  answers: "answer_builder.answers"

max_runs_per_component: 100

metadata: {}

Parameters

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
pipeline	Union[Pipeline, AsyncPipeline]		The pipeline instance to be wrapped.
input_mapping	Optional[Dict[str, List[str]]]	None	A dictionary mapping component input names to pipeline input socket paths. If not provided, a default mapping is created based on all pipeline inputs. Example: {"query": ["retriever.query", "prompt_builder.query"]}
output_mapping	Optional[Dict[str, str]]	None	A dictionary mapping pipeline output socket paths to component output names. If not provided, a default mapping is created based on all pipeline outputs. Example: {"generator.replies": "replies"}

Run Method Parameters

The run method parameters are dynamically determined by the input_mapping configuration. Each key in the input mapping becomes a parameter that can be passed at runtime.