How It Works

Input Mapping

Map the input names of the SuperComponent to the actual sockets inside the pipeline.

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

Output Mapping

Map the pipeline's output sockets that you want to expose to the SuperComponent's output names.

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.

Example

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

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).

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.')],...)