
Mandatory init variables	"pipeline": The Haystack pipeline to wrap "name": The name of the tool "description": Description of the tool
API reference	Tools
GitHub link	https://github.com/deepset-ai/haystack/blob/main/haystack/tools/pipeline_tool.py

Overview

PipelineTool lets you wrap a whole Haystack pipeline and expose it as a tool that an LLM can call.
It replaces the older workflow of first wrapping a pipeline in a SuperComponent and then passing that to
ComponentTool.

PipelineTool builds the tool parameter schema from the pipeline’s input sockets and uses the underlying components’ docstrings for input descriptions. You can choose which pipeline inputs and outputs to expose with
input_mapping and output_mapping. It works with both Pipeline and AsyncPipeline and can be used in a pipeline with ToolInvoker or directly with the Agent component.

Parameters

pipeline is mandatory and must be a Pipeline or AsyncPipeline instance.
name is mandatory and specifies the tool name.
description is mandatory and explains what the tool does.
input_mapping is optional. It maps tool input names to pipeline input socket paths. If omitted, a default
mapping is created from all pipeline inputs.
output_mapping is optional. It maps pipeline output socket paths to tool output names. If omitted, a default
mapping is created from all pipeline outputs.

Usage

Basic Usage

You can create a PipelineTool from any existing Haystack pipeline:

from haystack import Document, Pipeline
from haystack.tools import PipelineTool
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
from haystack.components.rankers.sentence_transformers_similarity import SentenceTransformersSimilarityRanker
from haystack.document_stores.in_memory import InMemoryDocumentStore

# Create your pipeline
document_store = InMemoryDocumentStore()
# Add some example documents
document_store.write_documents([
    Document(content="Nikola Tesla was a Serbian-American inventor and electrical engineer."),
    Document(content="Alternating current (AC) is an electric current which periodically reverses direction."),
    Document(content="Thomas Edison promoted direct current (DC) and competed with AC in the War of Currents."),
])
retrieval_pipeline = Pipeline()
retrieval_pipeline.add_component("bm25_retriever", InMemoryBM25Retriever(document_store=document_store))
retrieval_pipeline.add_component("ranker", SentenceTransformersSimilarityRanker(model="cross-encoder/ms-marco-MiniLM-L-6-v2"))
retrieval_pipeline.connect("bm25_retriever.documents", "ranker.documents")

# Wrap the pipeline as a tool
retrieval_tool = PipelineTool(
    pipeline=retrieval_pipeline,
    input_mapping={"query": ["bm25_retriever.query", "ranker.query"]},
    output_mapping={"ranker.documents": "documents"},
    name="retrieval_tool",
    description="Search short articles about Nikola Tesla, AC electricity, and related inventors",
)

In a pipeline

Create a PipelineTool from a retrieval pipeline and let an OpenAIChatGenerator use it as a tool in a pipeline.

from haystack import Document, Pipeline
from haystack.tools import PipelineTool
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.embedders.sentence_transformers_text_embedder import SentenceTransformersTextEmbedder
from haystack.components.embedders.sentence_transformers_document_embedder import SentenceTransformersDocumentEmbedder
from haystack.components.retrievers import InMemoryEmbeddingRetriever
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.components.tools.tool_invoker import ToolInvoker
from haystack.dataclasses import ChatMessage

# Initialize a document store and add some documents
document_store = InMemoryDocumentStore()
document_embedder = SentenceTransformersDocumentEmbedder(model="sentence-transformers/all-MiniLM-L6-v2")
documents = [
    Document(content="Nikola Tesla was a Serbian-American inventor and electrical engineer."),
    Document(content="He is best known for his contributions to the design of the modern alternating current (AC) electricity supply system."),
]
document_embedder.warm_up()
docs_with_embeddings = document_embedder.run(documents=documents)["documents"]
document_store.write_documents(docs_with_embeddings)

# Build a simple retrieval pipeline
retrieval_pipeline = Pipeline()
retrieval_pipeline.add_component(
    "embedder", SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2")
)
retrieval_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
retrieval_pipeline.connect("embedder.embedding", "retriever.query_embedding")

# Wrap the pipeline as a tool
retriever_tool = PipelineTool(
    pipeline=retrieval_pipeline,
    input_mapping={"query": ["embedder.text"]},
    output_mapping={"retriever.documents": "documents"},
    name="document_retriever",
    description="For any questions about Nikola Tesla, always use this tool",
)

# Create pipeline with OpenAIChatGenerator and ToolInvoker
pipeline = Pipeline()
pipeline.add_component("llm", OpenAIChatGenerator(model="gpt-4o-mini", tools=[retriever_tool]))
pipeline.add_component("tool_invoker", ToolInvoker(tools=[retriever_tool]))

# Connect components
pipeline.connect("llm.replies", "tool_invoker.messages")

message = ChatMessage.from_user("Use the document retriever tool to find information about Nikola Tesla")

# Run pipeline
result = pipeline.run({"llm": {"messages": [message]}})

print(result)

With the Agent Component

Use PipelineTool with the Agent component. The Agent includes a ToolInvoker and your chosen ChatGenerator to execute tool calls and process tool results.

from haystack import Document, Pipeline
from haystack.tools import PipelineTool
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.embedders.sentence_transformers_text_embedder import SentenceTransformersTextEmbedder
from haystack.components.embedders.sentence_transformers_document_embedder import SentenceTransformersDocumentEmbedder
from haystack.components.retrievers import InMemoryEmbeddingRetriever
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.components.agents import Agent
from haystack.dataclasses import ChatMessage

# Initialize a document store and add some documents
document_store = InMemoryDocumentStore()
document_embedder = SentenceTransformersDocumentEmbedder(model="sentence-transformers/all-MiniLM-L6-v2")
documents = [
    Document(content="Nikola Tesla was a Serbian-American inventor and electrical engineer."),
    Document(content="He is best known for his contributions to the design of the modern alternating current (AC) electricity supply system."),
]
document_embedder.warm_up()
docs_with_embeddings = document_embedder.run(documents=documents)["documents"]
document_store.write_documents(docs_with_embeddings)

# Build a simple retrieval pipeline
retrieval_pipeline = Pipeline()
retrieval_pipeline.add_component(
    "embedder", SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2")
)
retrieval_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
retrieval_pipeline.connect("embedder.embedding", "retriever.query_embedding")

# Wrap the pipeline as a tool
retriever_tool = PipelineTool(
    pipeline=retrieval_pipeline,
    input_mapping={"query": ["embedder.text"]},
    output_mapping={"retriever.documents": "documents"},
    name="document_retriever",
    description="For any questions about Nikola Tesla, always use this tool",
)

# Create an Agent with the tool
agent = Agent(
    chat_generator=OpenAIChatGenerator(model="gpt-4o-mini"),
    tools=[retriever_tool]
)

# Let the Agent handle a query
result = agent.run([ChatMessage.from_user("Who was Nikola Tesla?")])

# Print result of the tool call
print("Tool Call Result:")
print(result["messages"][2].tool_call_result.result)
print("")

# Print answer
print("Answer:")
print(result["messages"][-1].text)