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* Update documentation and remove unused assets. Enhanced the 'agents' and 'components' sections with clearer descriptions and examples. Removed obsolete images and updated links for better navigation. Adjusted formatting for consistency across various documentation pages. * remove dependency * address comments * delete more empty pages * broken link * unduplicate headings * alphabetical components nav
59 lines
2.9 KiB
Plaintext
59 lines
2.9 KiB
Plaintext
---
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title: "AnswerJoiner"
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id: answerjoiner
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slug: "/answerjoiner"
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description: "Merges multiple answers from different Generators into a single list."
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---
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# AnswerJoiner
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Merges multiple answers from different Generators into a single list.
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| --- | --- |
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| **Most common position in a pipeline** | In query pipelines, after [Generators](../generators.mdx) and, subsequently, components that return a list of answers such as [`AnswerBuilder`](../builders/answerbuilder.mdx) |
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| **Mandatory run variables** | “answers”: A nested list of answers to be merged, received from the Generator. This input is `variadic`, meaning you can connect a variable number of components to it. |
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| **Output variables** | “answers”: A merged list of answers |
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| **API reference** | [Joiners](/reference/joiners-api) |
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| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/joiners/answer_joiner.py |
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## Overvew
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`AnswerJoiner` joins input lists of [`Answer`](../../concepts/data-classes.mdx#answer) objects from multiple connections and returns them as one list.
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You can optionally set the `top_k` parameter, which specifies the maximum number of answers to return. If you don’t set this parameter, the component returns all answers it receives.
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## Usage
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In this simple example pipeline, the `AnswerJoiner` merges answers from two instances of Generators:
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```python
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from haystack.components.builders import AnswerBuilder
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from haystack.components.joiners import AnswerJoiner
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from haystack.core.pipeline import Pipeline
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from haystack.components.generators.chat import OpenAIChatGenerator
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from haystack.dataclasses import ChatMessage
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query = "What's Natural Language Processing?"
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messages = [ChatMessage.from_system("You are a helpful, respectful and honest assistant. Be super concise."),
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ChatMessage.from_user(query)]
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pipe = Pipeline()
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pipe.add_component("gpt-4o", OpenAIChatGenerator(model="gpt-4o"))
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pipe.add_component("llama", OpenAIChatGenerator(model="gpt-3.5-turbo"))
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pipe.add_component("aba", AnswerBuilder())
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pipe.add_component("abb", AnswerBuilder())
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pipe.add_component("joiner", AnswerJoiner())
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pipe.connect("gpt-4o.replies", "aba")
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pipe.connect("llama.replies", "abb")
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pipe.connect("aba.answers", "joiner")
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pipe.connect("abb.answers", "joiner")
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results = pipe.run(data={"gpt-4o": {"messages": messages},
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"llama": {"messages": messages},
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"aba": {"query": query},
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"abb": {"query": query}})
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``` |