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Improve wording for the 2 search mode of neural sparse documentation #7718

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Jul 16, 2024
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Improve wording for the 2 search mode of neural sparse documentation #7718

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improve wording for ns
zhichao-aws Jul 16, 2024
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kolchfa-aws Jul 16, 2024
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4 changes: 2 additions & 2 deletions _search-plugins/neural-sparse-search.md
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Expand Up @@ -16,8 +16,8 @@ Introduced 2.11

When selecting a model, choose one of the following options:

- Use a sparse encoding model at both ingestion time and search time (high performance, relatively high latency).
- Use a sparse encoding model at ingestion time and a tokenizer at search time for relatively low performance and low latency. The tokenism doesn't conduct model inference, so you can deploy and invoke a tokenizer using the ML Commons Model API for a more consistent experience.
- Use a sparse encoding model at both ingestion time and search time for high search relevance with relatively high latency.
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- Use a sparse encoding model at ingestion time and a tokenizer at search time for low search latency with relatively low search relevance. The tokenism doesn't conduct model inference, so you can deploy and invoke a tokenizer using the ML Commons Model API for a more consistent experience.
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**PREREQUISITE**<br>
Before using neural sparse search, make sure to set up a [pretrained sparse embedding model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/pretrained-models/#sparse-encoding-models) or your own sparse 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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