
Most common position in a pipeline	In indexing and query pipelines, after components that return lists of documents such as multiple Retrievers or multiple Converters
Mandatory run variables	“values”: The dictionary of lists to be joined
Output variables	“values”: A dictionary with a `values` key containing the joined list
API reference	Joiners
GitHub link	https://github.com/deepset-ai/haystack/blob/main/haystack/components/joiners/list_joiner.py

Overview

The ListJoiner component combines multiple lists into one list. It is useful for combining multiple lists from different pipeline components, merging LLM responses, handling multi-step data processing, and gathering data from different sources into one list.

The items stay in order based on when each input list was processed in a pipeline.

You can optionally specify a list_type_ parameter to set the expected type of the lists being joined (for example, List[ChatMessage]). If not set, ListJoiner will accept lists containing mixed data types.

Usage

On its own

from haystack.components.joiners import ListJoiner

list1 = ["Hello", "world"]
list2 = ["This", "is", "Haystack"]
list3 = ["ListJoiner", "Example"]

joiner = ListJoiner()

result = joiner.run(values=[list1, list2, list3])

print(result["values"])

In a pipeline

from haystack.components.builders import ChatPromptBuilder
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage
from haystack import Pipeline
from haystack.components.joiners import ListJoiner
from typing import List

user_message = [ChatMessage.from_user("Give a brief answer the following question: {{query}}")]

feedback_prompt = \"""
    You are given a question and an answer.
    Your task is to provide a score and a brief feedback on the answer.
    Question: {{query}}
    Answer: {{response}}
    \"""
feedback_message = [ChatMessage.from_system(feedback_prompt)]

prompt_builder = ChatPromptBuilder(template=user_message)
feedback_prompt_builder = ChatPromptBuilder(template=feedback_message)
llm = OpenAIChatGenerator(model="gpt-4o-mini")
feedback_llm = OpenAIChatGenerator(model="gpt-4o-mini")

pipe = Pipeline()
pipe.add_component("prompt_builder", prompt_builder)
pipe.add_component("llm", llm)
pipe.add_component("feedback_prompt_builder", feedback_prompt_builder)
pipe.add_component("feedback_llm", feedback_llm)
pipe.add_component("list_joiner", ListJoiner(List[ChatMessage]))

pipe.connect("prompt_builder.prompt", "llm.messages")
pipe.connect("prompt_builder.prompt", "list_joiner")
pipe.connect("llm.replies", "list_joiner")
pipe.connect("llm.replies", "feedback_prompt_builder.response")
pipe.connect("feedback_prompt_builder.prompt", "feedback_llm.messages")
pipe.connect("feedback_llm.replies", "list_joiner")

query = "What is nuclear physics?"
ans = pipe.run(data={"prompt_builder": {"template_variables":{"query": query}},
    "feedback_prompt_builder": {"template_variables":{"query": query}}})

print(ans["list_joiner"]["values"])