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Remove model requirement from hybrid search documentation #7511

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Jul 15, 2024
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Remove model requirement from hybrid search documentation #7511

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835d876
Remove model requirement from hybrid search documentation
kolchfa-aws Jun 26, 2024
dc5a7b8
Review comment
kolchfa-aws Jun 26, 2024
7b2ccea
Revised sentence
kolchfa-aws Jun 26, 2024
4b20daa
Update _search-plugins/hybrid-search.md
kolchfa-aws Jul 15, 2024
77fc879
Merge branch 'main' into hybrid-fix
kolchfa-aws Jul 15, 2024
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6 changes: 1 addition & 5 deletions _query-dsl/compound/hybrid.md
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Expand Up @@ -12,11 +12,7 @@ You can use a hybrid query to combine relevance scores from multiple queries int

## Example

Before using a `hybrid` query, you must set up a machine learning (ML) model, ingest documents, and configure a search pipeline with a [`normalization-processor`]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/normalization-processor/).

To learn how to set up an ML model, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model).

Once you set up an ML model, learn how to use the `hybrid` query by following the steps in [Using hybrid search]({{site.url}}{{site.baseurl}}/search-plugins/hybrid-search/#using-hybrid-search).
Learn how to use the `hybrid` query by following the steps in [Using hybrid search]({{site.url}}{{site.baseurl}}/search-plugins/hybrid-search/#using-hybrid-search).

For a comprehensive example, follow the [Neural search tutorial]({{site.url}}{{site.baseurl}}/ml-commons-plugin/semantic-search#tutorial).

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2 changes: 1 addition & 1 deletion _search-plugins/hybrid-search.md
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Expand Up @@ -12,7 +12,7 @@ Introduced 2.11
Hybrid search combines keyword and neural search to improve search relevance. To implement hybrid search, you need to set up a [search pipeline]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/index/) that runs at search time. The search pipeline you'll configure intercepts search results at an intermediate stage and applies the [`normalization_processor`]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/normalization-processor/) to them. The `normalization_processor` normalizes and combines the document scores from multiple query clauses, rescoring the documents according to the chosen normalization and combination techniques.

**PREREQUISITE**<br>
Before using hybrid search, you must set up a text embedding model. For more information, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model).
To follow this example, you must set up a text embedding model. For more information, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model).
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can we mention that having a embedding model is not required if you already have the embeddings for the text

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In this case, they can just directly set up hybrid search, correct? I added that sentence.

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no actually thats not the case. What we need to tell them is if you already have the embeddings directly ingest the embeddings and skip the ingest pipeline creation step.

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Sure. I revised the sentence.

{: .note}

## Using hybrid search
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