haystack/docs-website/versioned_docs/version-2.20/concepts/components/supercomponents.mdx

---
title: "SuperComponents"
id: supercomponents
slug: "/supercomponents"
description: "`SuperComponent` lets you wrap a complete pipeline and use it like a single component. This is helpful when you want to simplify the interface of a complex pipeline, reuse it in different contexts, or expose only the necessary inputs and outputs."
---

# SuperComponents

`SuperComponent` lets you wrap a complete pipeline and use it like a single component. This is helpful when you want to simplify the interface of a complex pipeline, reuse it in different contexts, or expose only the necessary inputs and outputs.

## `@super_component` decorator (recommended)

Haystack now provides a simple `@super_component` decorator for wrapping a pipeline as a component. All you need is to create a class with the decorator, and to include an `pipeline` attribute.

With this decorator, the `to_dict` and `from_dict` serialization is optional, as is the input and output mapping.

### Example

The custom HybridRetriever example SuperComponent below turns your query into embeddings, then runs both a BM25 search and an embedding-based search at the same time. It finally merges those two result sets and returns the combined documents.

```python
## pip install haystack-ai datasets "sentence-transformers>=3.0.0"

from haystack import Document, Pipeline, super_component
from haystack.components.joiners import DocumentJoiner
from haystack.components.embedders import SentenceTransformersTextEmbedder
from haystack.components.retrievers import InMemoryBM25Retriever, InMemoryEmbeddingRetriever
from haystack.document_stores.in_memory import InMemoryDocumentStore

from datasets import load_dataset

@super_component
class HybridRetriever:
    def __init__(self, document_store: InMemoryDocumentStore, embedder_model: str = "BAAI/bge-small-en-v1.5"):
        embedding_retriever = InMemoryEmbeddingRetriever(document_store)
        bm25_retriever = InMemoryBM25Retriever(document_store)
        text_embedder = SentenceTransformersTextEmbedder(embedder_model)
        document_joiner = DocumentJoiner()

        self.pipeline = Pipeline()
        self.pipeline.add_component("text_embedder", text_embedder)
        self.pipeline.add_component("embedding_retriever", embedding_retriever)
        self.pipeline.add_component("bm25_retriever", bm25_retriever)
        self.pipeline.add_component("document_joiner", document_joiner)

        self.pipeline.connect("text_embedder", "embedding_retriever")
        self.pipeline.connect("bm25_retriever", "document_joiner")
        self.pipeline.connect("embedding_retriever", "document_joiner")

dataset = load_dataset("HaystackBot/medrag-pubmed-chunk-with-embeddings", split="train")
docs = [Document(content=doc["contents"], embedding=doc["embedding"]) for doc in dataset]
document_store = InMemoryDocumentStore()
document_store.write_documents(docs)

query = "What treatments are available for chronic bronchitis?"

result = HybridRetriever(document_store).run(text=query, query=query)
print(result)
```

### Input Mapping

You can optionally map the input names of your SuperComponent to the actual sockets inside the pipeline.

```python
input_mapping = {
    "query": ["retriever.query", "prompt.query"]
}
```

### Output Mapping

You can also map the pipeline's output sockets that you want to expose to the SuperComponent's output names.

```python
output_mapping = {
    "llm.replies": "replies"
}
```

If you don’t provide mappings, SuperComponent will try to auto-detect them. So, if multiple components have outputs with the same name, we recommend using `output_mapping` to avoid conflicts.

## SuperComponent class

Haystack also gives you an option to inherit from SuperComponent class. This option requires `to_dict` and `from_dict` serialization, as well as the input and output mapping described above.

### Example

Here is a simple example of initializing a `SuperComponent` with a pipeline:

```python
from haystack import Pipeline, SuperComponent

with open("pipeline.yaml", "r") as file:
  pipeline = Pipeline.load(file)

super_component = SuperComponent(pipeline)
```

The example pipeline below retrieves relevant documents based on a user query, builds a custom prompt using those documents, then sends the prompt to an `OpenAIChatGenerator` to create an answer. The `SuperComponent` wraps the pipeline so it can be run with a simple input (`query`) and returns a clean output (`replies`).

```python
from haystack import Pipeline, SuperComponent
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.components.builders import ChatPromptBuilder
from haystack.components.retrievers import InMemoryBM25Retriever
from haystack.dataclasses.chat_message import ChatMessage
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.dataclasses import Document

document_store = InMemoryDocumentStore()
documents = [
    Document(content="Paris is the capital of France."),
    Document(content="London is the capital of England."),
]
document_store.write_documents(documents)

prompt_template = [
    ChatMessage.from_user(
    '''
    According to the following documents:
    {% for document in documents %}
    {{document.content}}
    {% endfor %}
    Answer the given question: {{query}}
    Answer:
    '''
    )
]

prompt_builder = ChatPromptBuilder(template=prompt_template, required_variables="*")

pipeline = Pipeline()
pipeline.add_component("retriever", InMemoryBM25Retriever(document_store=document_store))
pipeline.add_component("prompt_builder", prompt_builder)
pipeline.add_component("llm", OpenAIChatGenerator())
pipeline.connect("retriever.documents", "prompt_builder.documents")
pipeline.connect("prompt_builder.prompt", "llm.messages")

## Create a super component with simplified input/output mapping
wrapper = SuperComponent(
    pipeline=pipeline,
    input_mapping={
        "query": ["retriever.query", "prompt_builder.query"],
    },
    output_mapping={
        "llm.replies": "replies",
        "retriever.documents": "documents"
    }
)

## Run the pipeline with simplified interface
result = wrapper.run(query="What is the capital of France?")
print(result)
{'replies': [ChatMessage(_role=<ChatRole.ASSISTANT: 'assistant'>,
 _content=[TextContent(text='The capital of France is Paris.')],...)
```

## Type Checking and Static Code Analysis

Creating SuperComponents using the @supercomponent decorator can induce type or linting errors. One way to avoid these issues is to add the exposed public methods to your SuperComponent. Here's an example:

```python
from typing import TYPE_CHECKING

if TYPE_CHECKING:
    def run(self, *, documents: List[Document]) -> dict[str, list[Document]]:
        ...
    def warm_up(self) -> None:  # noqa: D102
        ...
```

## Ready-Made SuperComponents

You can see two implementations of SuperComponents already integrated in Haystack:

- [DocumentPreprocessor](../../pipeline-components/preprocessors/documentpreprocessor.mdx)
- [MultiFileConverter](../../pipeline-components/converters/multifileconverter.mdx)
- [OpenSearchHybridRetriever](../../pipeline-components/retrievers/opensearchhybridretriever.mdx)