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Copy file name to clipboardExpand all lines: solutions/search/get-started/semantic-search.md
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@@ -39,7 +39,7 @@ The examples in this guide use the default Learned Sparse Encoder ([ELSER](/expl
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The way that you store vectors has a significant impact on the performance and accuracy of search results.
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They must be stored in specialized data structures designed to ensure efficient similarity search and speedy vector distance calculations.
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This guide uses the [semantic text field type](elasticsearch://reference/elasticsearch/mapping-reference/semantic-text.md), which provide sensible defaults and automation.
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This guide uses the [semantic text field type](elasticsearch://reference/elasticsearch/mapping-reference/semantic-text.md), which provides sensible defaults and automation.
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Try vectorizing a small set of documents.
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You can follow the guided index workflow:
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The bulk ingestion might take longer than the default request timeout.
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If it times out, wait for the ELSER model to load (typically 1-5 minutes) then retry it.
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What just happened? The content was transformed into sparse vectors, which involves two main steps.
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What just happened?
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First, the content was divided into smaller, manageable chunks to ensure that meaningful segments can be more effectively processed and searched.
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Then each chunk of text was transformed into a sparse vector representation using text expansion techniques.
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Then each chunk of text was transformed into a sparse vector by using the ELSER model's text expansion techniques.
With a few vectors stored in {{es}}, semantic search can now occur.
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##Explore the data
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The vectors are stored in {{es}} and semantic search can now occur.
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::::{step} Explore the data
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To familiarize yourself with this data set, open [Discover](/explore-analyze/discover.md) from the navigation menu or by using the [global search field](/explore-analyze/find-and-organize/find-apps-and-objects.md).
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@@ -110,6 +108,8 @@ In **Discover**, you can click the expand icon .
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