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chore: import upstream snapshot with attribution
2026-07-13 13:22:28 +08:00

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Python

# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
#
# SPDX-License-Identifier: Apache-2.0
from itertools import chain
from typing import Any
from haystack import component, default_from_dict, default_to_dict
from haystack.core.component.types import Variadic
from haystack.utils import deserialize_type, serialize_type
@component
class ListJoiner:
"""
A component that joins multiple lists into a single flat list.
The ListJoiner receives multiple lists of the same type and concatenates them into a single flat list.
The output order respects the pipeline's execution sequence, with earlier inputs being added first.
Usage example:
```python
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
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()
feedback_llm = OpenAIChatGenerator()
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"])
```
"""
def __init__(self, list_type_: type | None = None) -> None:
"""
Creates a ListJoiner component.
:param list_type_: The expected type of the lists this component will join (e.g., list[ChatMessage]).
If specified, all input lists must conform to this type. If None, the component defaults to handling
lists of any type including mixed types.
"""
self.list_type_ = list_type_
if list_type_ is not None:
component.set_output_types(self, values=list_type_)
else:
component.set_output_types(self, values=list[Any])
def to_dict(self) -> dict[str, Any]:
"""
Serializes the component to a dictionary.
:returns: Dictionary with serialized data.
"""
return default_to_dict(
self, list_type_=serialize_type(self.list_type_) if self.list_type_ is not None else None
)
@classmethod
def from_dict(cls, data: dict[str, Any]) -> "ListJoiner":
"""
Deserializes the component from a dictionary.
:param data: Dictionary to deserialize from.
:returns: Deserialized component.
"""
init_parameters = data.get("init_parameters")
if init_parameters is not None and init_parameters.get("list_type_") is not None:
data["init_parameters"]["list_type_"] = deserialize_type(data["init_parameters"]["list_type_"])
return default_from_dict(cls, data)
def run(self, values: Variadic[list[Any]]) -> dict[str, list[Any]]:
"""
Joins multiple lists into a single flat list.
:param values: The list to be joined.
:returns: Dictionary with 'values' key containing the joined list.
"""
result = list(chain(*values))
return {"values": result}