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@@ -5,14 +5,12 @@
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<titleabbrev>Semantic text</titleabbrev>
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++++
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-beta[]
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-
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The `semantic_text` field type automatically generates embeddings for text content using an inference endpoint.
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Long passages are <<auto-text-chunking, automatically chunked>> to smaller sections to enable the processing of larger corpuses of text.
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The `semantic_text` field type specifies an inference endpoint identifier that will be used to generate embeddings.
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You can create the inference endpoint by using the <<put-inference-api>>.
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-This field type and the <<query-dsl-semantic-query,`semantic` query>> type make it simpler to perform semantic search on your data.
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+This field type and the <<query-dsl-semantic-query,`semantic` query>> type make it simpler to perform semantic search on your data.
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The `semantic_text` field type may also be queried with <<query-dsl-match-query, match>>, <<query-dsl-sparse-vector-query, sparse_vector>> or <<query-dsl-knn-query, knn>> queries.
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If you don’t specify an inference endpoint, the `inference_id` field defaults to `.elser-2-elasticsearch`, a preconfigured endpoint for the elasticsearch service.
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@@ -193,8 +191,8 @@ types and create an ingest pipeline with an
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<<inference-processor, {infer} processor>> to generate the embeddings.
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<<semantic-search-inference,This tutorial>> walks you through the process. In
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these cases - when you use `sparse_vector` or `dense_vector` field types instead
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-of the `semantic_text` field type to customize indexing - using the
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-<<query-dsl-semantic-query,`semantic_query`>> is not supported for querying the
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+of the `semantic_text` field type to customize indexing - using the
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+<<query-dsl-semantic-query,`semantic_query`>> is not supported for querying the
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field data.
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