From 2d612ec2692acc906d01549de679470e38117ed5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Istv=C3=A1n=20Zolt=C3=A1n=20Szab=C3=B3?= Date: Mon, 4 Mar 2024 15:24:14 +0100 Subject: [PATCH] Apply suggestions from code review Co-authored-by: Jonathan Buttner <56361221+jonathan-buttner@users.noreply.github.com> --- .../tab-widgets/inference-api/infer-api-mapping.asciidoc | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/reference/tab-widgets/inference-api/infer-api-mapping.asciidoc b/docs/reference/tab-widgets/inference-api/infer-api-mapping.asciidoc index 2b857b7c54aa7..4b70a1b84f45f 100644 --- a/docs/reference/tab-widgets/inference-api/infer-api-mapping.asciidoc +++ b/docs/reference/tab-widgets/inference-api/infer-api-mapping.asciidoc @@ -24,7 +24,7 @@ in the {infer} pipeline configuration in the next step. <3> The output dimensions of the model. Find this value in the https://docs.cohere.com/reference/embed[Cohere documentation] of the model you use. -<4> The name of the field from which to create the sparse vector representation. +<4> The name of the field from which to create the dense vector representation. In this example, the name of the field is `content`. It must be referenced in the {infer} pipeline configuration in the next step. <5> The field type which is text in this example. @@ -63,7 +63,7 @@ of the model you use. because OpenAI embeddings are normalised to unit length. You can check the https://platform.openai.com/docs/guides/embeddings/which-distance-function-should-i-use[OpenAI docs] about which similarity function to use. -<5> The name of the field from which to create the sparse vector representation. +<5> The name of the field from which to create the dense vector representation. In this example, the name of the field is `content`. It must be referenced in the {infer} pipeline configuration in the next step. <6> The field type which is text in this example.