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* Update versionedReferenceLinks.js * fixing all links * github-hanlp-swap --------- Co-authored-by: Stefano Fiorucci <stefanofiorucci@gmail.com>
60 lines
3.3 KiB
Plaintext
60 lines
3.3 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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```
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