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113 lines
4.2 KiB
Python
113 lines
4.2 KiB
Python
# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
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#
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# SPDX-License-Identifier: Apache-2.0
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from itertools import chain
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from typing import Any
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from haystack import component, default_from_dict, default_to_dict
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from haystack.core.component.types import Variadic
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from haystack.utils import deserialize_type, serialize_type
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@component
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class ListJoiner:
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"""
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A component that joins multiple lists into a single flat list.
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The ListJoiner receives multiple lists of the same type and concatenates them into a single flat list.
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The output order respects the pipeline's execution sequence, with earlier inputs being added first.
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Usage example:
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```python
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from haystack.components.builders import ChatPromptBuilder
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from haystack.components.generators.chat import OpenAIChatGenerator
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from haystack.dataclasses import ChatMessage
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from haystack import Pipeline
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from haystack.components.joiners import ListJoiner
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user_message = [ChatMessage.from_user("Give a brief answer the following question: {{query}}")]
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feedback_prompt = \"""
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You are given a question and an answer.
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Your task is to provide a score and a brief feedback on the answer.
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Question: {{query}}
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Answer: {{response}}
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\"""
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feedback_message = [ChatMessage.from_system(feedback_prompt)]
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prompt_builder = ChatPromptBuilder(template=user_message)
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feedback_prompt_builder = ChatPromptBuilder(template=feedback_message)
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llm = OpenAIChatGenerator()
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feedback_llm = OpenAIChatGenerator()
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pipe = Pipeline()
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pipe.add_component("prompt_builder", prompt_builder)
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pipe.add_component("llm", llm)
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pipe.add_component("feedback_prompt_builder", feedback_prompt_builder)
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pipe.add_component("feedback_llm", feedback_llm)
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pipe.add_component("list_joiner", ListJoiner(list[ChatMessage]))
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pipe.connect("prompt_builder.prompt", "llm.messages")
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pipe.connect("prompt_builder.prompt", "list_joiner")
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pipe.connect("llm.replies", "list_joiner")
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pipe.connect("llm.replies", "feedback_prompt_builder.response")
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pipe.connect("feedback_prompt_builder.prompt", "feedback_llm.messages")
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pipe.connect("feedback_llm.replies", "list_joiner")
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query = "What is nuclear physics?"
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ans = pipe.run(data={"prompt_builder": {"template_variables":{"query": query}},
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"feedback_prompt_builder": {"template_variables":{"query": query}}})
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print(ans["list_joiner"]["values"])
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```
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"""
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def __init__(self, list_type_: type | None = None) -> None:
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"""
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Creates a ListJoiner component.
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:param list_type_: The expected type of the lists this component will join (e.g., list[ChatMessage]).
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If specified, all input lists must conform to this type. If None, the component defaults to handling
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lists of any type including mixed types.
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"""
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self.list_type_ = list_type_
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if list_type_ is not None:
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component.set_output_types(self, values=list_type_)
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else:
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component.set_output_types(self, values=list[Any])
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def to_dict(self) -> dict[str, Any]:
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"""
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Serializes the component to a dictionary.
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:returns: Dictionary with serialized data.
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"""
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return default_to_dict(
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self, list_type_=serialize_type(self.list_type_) if self.list_type_ is not None else None
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)
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@classmethod
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def from_dict(cls, data: dict[str, Any]) -> "ListJoiner":
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"""
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Deserializes the component from a dictionary.
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:param data: Dictionary to deserialize from.
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:returns: Deserialized component.
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"""
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init_parameters = data.get("init_parameters")
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if init_parameters is not None and init_parameters.get("list_type_") is not None:
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data["init_parameters"]["list_type_"] = deserialize_type(data["init_parameters"]["list_type_"])
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return default_from_dict(cls, data)
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def run(self, values: Variadic[list[Any]]) -> dict[str, list[Any]]:
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"""
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Joins multiple lists into a single flat list.
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:param values: The list to be joined.
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:returns: Dictionary with 'values' key containing the joined list.
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"""
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result = list(chain(*values))
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return {"values": result}
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