chore: import upstream snapshot with attribution

haystack/components/joiners/list_joiner.py

@@ -0,0 +1,112 @@
+# 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}