From 6dca3207e9544dc60c2670712ca65315f3403cd8 Mon Sep 17 00:00:00 2001 From: Abdon Pijpelink Date: Mon, 17 Jul 2023 14:38:56 +0200 Subject: [PATCH] Temporarily remove deep link to knn semantic search (#2474) --- .../stack/ml/nlp/ml-nlp-text-emb-vector-search-example.asciidoc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/en/stack/ml/nlp/ml-nlp-text-emb-vector-search-example.asciidoc b/docs/en/stack/ml/nlp/ml-nlp-text-emb-vector-search-example.asciidoc index 64e2a91ec..15d135f63 100644 --- a/docs/en/stack/ml/nlp/ml-nlp-text-emb-vector-search-example.asciidoc +++ b/docs/en/stack/ml/nlp/ml-nlp-text-emb-vector-search-example.asciidoc @@ -265,7 +265,7 @@ After the reindexing is finished, the documents in the new index contain the == Semantic search After the dataset has been enriched with vector embeddings, you can query the -data using {ref}/knn-search.html#semantic-search[semantic search]. Pass a +data using {ref}/knn-search.html[semantic search]. Pass a `query_vector_builder` to the k-nearest neighbor (kNN) vector search API, and provide the query text and the model you have used to create vector embeddings. This example searches for "How is the weather in Jamaica?":