[E&A] Add docs for jina-reranker-v2

explore-analyze/machine-learning/nlp/ml-nlp-jina.md

@@ -13,16 +13,17 @@ This page collects all Jina models you can use as part of the {{stack}}.
 Currently, the following models are available as built-in models:
 
 * [`jina-embeddings-v3`](#jina-embeddings-v3)
+* [`jina-reranker-v2`](#jina-reranker-v2)
 
 ## `jina-embeddings-v3` [jina-embeddings-v3]
 
-The [`jina-embeddings-v3`](https://jina.ai/models/jina-embeddings-v3/) is a multilingual dense vector embedding model that you can use through the [Elastic {{infer-cap}} Service (EIS)](/explore-analyze/elastic-inference/eis.md).
+The [`jina-embeddings-v3`](https://jina.ai/models/jina-embeddings-v3/) is a multilingual dense vector embedding model that you can use through [Elastic {{infer-cap}} Service (EIS)](/explore-analyze/elastic-inference/eis.md).
 It provides long-context embeddings across a wide range of languages without requiring you to configure, download, or deploy any model artifacts yourself.
 As the model runs on EIS, Elastic's own infrastructure, no ML node scaling and configuration is required to use it.
 
 The `jina-embedings-v3` model supports input lengths of up to 8192 tokens and produces 1024-dimension embeddings by default. It uses task-specific adapters to optimize embeddings for different use cases (such as retrieval or classification), and includes support for Matryoshka Representation Learning, which allows you to truncate embeddings to fewer dimensions with minimal loss in quality.
 
-For more info, refer to the [model card](https://huggingface.co/jinaai/jina-embeddings-v3) on Hugging Face.
+For more information about the model, refer to the [model card](https://huggingface.co/jinaai/jina-embeddings-v3) on Hugging Face.
 
 ### Dense vector embeddings
 
@@ -32,7 +33,7 @@ Dense vector embeddings are fixed-length numerical representations of text. When
 
 To use `jina-embeddings-v3`, you must have the [appropriate subscription](https://www.elastic.co/subscriptions) level or the trial period activated.
 
-### Getting started with `jina-embeddings-v3` via the Elastic {{infer-cap}} Service
+### Getting started with `jina-embeddings-v3` through Elastic {{infer-cap}} Service
 
 Create an {{infer}} endpoint that references the `jina-embeddings-v3` model in the `model_id` field.
 
@@ -46,7 +47,7 @@ PUT _inference/text_embedding/eis-jina-embeddings-v3
 }
 ```
 
-The created {{infer}} endpoint uses the model for {{infer}} operations on the Elastic {{infer-cap}} Service. You can reference the `inference_id` of the endpoint in text_embedding {{infer}} tasks or search queries.
+The created {{infer}} endpoint uses the model for {{infer}} operations on Elastic {{infer-cap}} Service. You can reference the `inference_id` of the endpoint in `text_embedding` {{infer}} tasks or search queries.
 For example, the following API request ingests the input text and produce embeddings.
 
 ```console
@@ -65,3 +66,40 @@ Although `jina-embeddings-v3` has a context window of 8192 tokens, it's best to
 For larger fields that exceed this limit - for example, `body_content` on web crawler documents - consider chunking the content into multiple values, where each chunk can be under 4096 tokens.
 * Larger documents take longer at ingestion time, and {{infer}} time per document also increases the more fields in a document that need to be processed.
 * The more fields your pipeline has to perform {{infer}} on, the longer it takes per document to ingest.
+
+## `jina-reranker-v2` [jina-reranker-v2]
+
+[`jina-reranker-v2`](https://jina.ai/models/jina-reranker-v2-base-multilingual/) is a multilingual cross-encoder model that helps you to improve search relevance across over 100 languages and various data types. The model significantly improves information retrieval in multilingual environments. `jina-reranker-v2` is available out-of-the-box and supports Elastic deployments using the {{es}} Inference API. You can use the model to improve existing search applications like hybrid semantic search, retrieval augmented generation (RAG), and more. You can use the model through Elastic {{infer-cap}} Service (EIS), Elastic's own infrastructure, without the need of managing infrastructure and model resources.
+
+For more information about the model, refer to the [model card](https://huggingface.co/jinaai/jina-reranker-v2-base-multilingual) on Hugging Face.
+
+### Requirements [jina-reranker-v2-req]
+
+To use `jina-reranker-v2`, you must have the [appropriate subscription](https://www.elastic.co/subscriptions) level or the trial period activated.
+
+### Getting started with `jina-reranker-v2` through Elastic {{infer-cap}} Service
+
+Create an {{infer}} endpoint that references the `jina-reranker-v2` model in the `model_id` field.
+
+```console
+PUT _inference/rerank/eis-jina-reranker-v2
+{
+  "service": "elastic",
+  "service_settings": {
+    "model_id": "jina-reranker-v2"
+  }
+}
+```
+
+The created {{infer}} endpoint uses the model for {{infer}} operations on Elastic {{infer-cap}} Service. You can reference the `inference_id` of the endpoint in `rerank` {{infer}} tasks.
+For example, the following API request ingests the input strings and ranks them by relevance:
+
+```console
+POST _inference/rerank/eis-jina-reranker-v2
+{
+  "input": ["luke", "like", "leia", "chewy","r2d2", "star", "wars"],
+  "query": "star wars main character"
+}
+```
+
+### Performance considerations