From e9093916eeffca2800148ba855deb0fe22b2c394 Mon Sep 17 00:00:00 2001 From: Liam Thompson Date: Thu, 12 Dec 2024 15:42:14 +0100 Subject: [PATCH] [DOCS] Link to Elastic Rerank model landing page --- docs/reference/inference/service-elasticsearch.asciidoc | 2 +- docs/reference/search/retriever.asciidoc | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/reference/inference/service-elasticsearch.asciidoc b/docs/reference/inference/service-elasticsearch.asciidoc index cd06e6d7b2f64..5080df7499be3 100644 --- a/docs/reference/inference/service-elasticsearch.asciidoc +++ b/docs/reference/inference/service-elasticsearch.asciidoc @@ -153,7 +153,7 @@ For further details, refer to the {ml-docs}/ml-nlp-elser.html[ELSER model docume [[inference-example-elastic-reranker]] ==== Elastic Rerank via the `elasticsearch` service -The following example shows how to create an {infer} endpoint called `my-elastic-rerank` to perform a `rerank` task type using the built-in Elastic Rerank cross-encoder model. +The following example shows how to create an {infer} endpoint called `my-elastic-rerank` to perform a `rerank` task type using the built-in cross-encoder model. The API request below will automatically download the Elastic Rerank model if it isn't already downloaded and then deploy the model. Once deployed, the model can be used for semantic re-ranking with a <>. diff --git a/docs/reference/search/retriever.asciidoc b/docs/reference/search/retriever.asciidoc index cb04d4fb6fbf1..91857287033ab 100644 --- a/docs/reference/search/retriever.asciidoc +++ b/docs/reference/search/retriever.asciidoc @@ -442,7 +442,7 @@ If the child retriever already specifies any filters, then this top-level filter [[text-similarity-reranker-retriever-example-elastic-rerank]] ==== Example: Elastic Rerank -This examples demonstrates how to deploy the Elastic Rerank model and use it to re-rank search results using the `text_similarity_reranker` retriever. +This examples demonstrates how to deploy the {ml-docs}/ml-nlp-rerank.html[Elastic Rerank] model and use it to re-rank search results using the `text_similarity_reranker` retriever. Follow these steps: