[DOCS] Retrievers and rerankers (elastic#110007)

Co-authored-by: Adam Demjen <[email protected]>

docs/reference/search/retriever.asciidoc

@@ -28,6 +28,9 @@ A <<knn-retriever, retriever>> that replaces the functionality of a <<search-api
 `rrf`::
 A <<rrf-retriever, retriever>> that produces top documents from <<rrf, reciprocal rank fusion (RRF)>>.
 
+`text_similarity_reranker`::
+A <<text-similarity-reranker-retriever, retriever>> that enhances search results by re-ranking documents based on semantic similarity to a specified inference text, using a machine learning model.
+
 [[standard-retriever]]
 ==== Standard Retriever
 
@@ -201,6 +204,70 @@ GET /index/_search
 ----
 // NOTCONSOLE
 
+[[text-similarity-reranker-retriever]]
+==== Text Similarity Re-ranker Retriever
+
+The `text_similarity_reranker` is a type of retriever that enhances search results by re-ranking documents based on semantic similarity to a specified inference text, using a machine learning model.
+
+===== Prerequisites
+
+To use `text_similarity_reranker` you must first set up a `rerank` task using the <<put-inference-api, Create {infer} API>>.
+The `rerank` task should be set up with a machine learning model that can compute text similarity.
+Currently you can integrate directly with the Cohere Rerank endpoint using the <<infer-service-cohere,`cohere-rerank`>> task, or upload a model to {es} <<inference-example-eland,using Eland>>.
+
+===== Parameters
+
+`field`::
+(Required, `string`)
++
+The document field to be used for text similarity comparisons. This field should contain the text that will be evaluated against the `inferenceText`.
+
+`inference_id`::
+(Required, `string`)
++
+Unique identifier of the inference endpoint created using the {infer} API.
+
+`inference_text`::
+(Required, `string`)
++
+The text snippet used as the basis for similarity comparison.
+
+`rank_window_size`::
+(Optional, `int`)
++
+The number of top documents to consider in the re-ranking process. Defaults to `10`.
+
+`min_score`::
+(Optional, `float`)
++
+Sets a minimum threshold score for including documents in the re-ranked results. Documents with similarity scores below this threshold will be excluded. Note that score calculations vary depending on the model used.
+
+===== Restrictions
+
+A text similarity re-ranker retriever is a compound retriever. Child retrievers may not use elements that are restricted by having a compound retriever as part of the retriever tree.
+
+===== Example
+
+[source,js]
+----
+GET /index/_search
+{
+    "retriever": {
+        "text_similarity_reranker": {
+            "retriever": {
+                "standard": { ... }
+            }
+        },
+        "field": "text",
+        "inference_id": "my-cohere-rerank-model",
+        "inference_text": "Most famous landmark in Paris",
+        "rank_window_size": 100,
+        "min_score": 0.5
+    }
+}
+----
+// NOTCONSOLE
+
 ==== Using `from` and `size` with a retriever tree
 
 The <<search-from-param, `from`>> and <<search-size-param, `size`>>

docs/reference/search/search-your-data/retrievers-reranking/index.asciidoc

@@ -0,0 +1,8 @@
+[[retrievers-reranking-overview]]
+== Retrievers and reranking
+
+* <<retrievers-overview>>
+* <<semantic-reranking>>
+
+include::retrievers-overview.asciidoc[]
+include::semantic-reranking.asciidoc[]

docs/reference/search/search-your-data/retrievers-overview.asciidoc → docs/reference/search/search-your-data/retrievers-reranking/retrievers-overview.asciidoc

@@ -1,7 +1,5 @@
 [[retrievers-overview]]
-== Retrievers
-
-// Will move to a top level "Retrievers and reranking" section once reranking is live
+=== Retrievers
 
 preview::[]
 
@@ -15,33 +13,32 @@ For implementation details, including notable restrictions, check out the
 
 [discrete]
 [[retrievers-overview-types]]
-=== Retriever types
+==== Retriever types 
 
 Retrievers come in various types, each tailored for different search operations.
 The following retrievers are currently available:
 
-* <<standard-retriever,*Standard Retriever*>>.
-Returns top documents from a traditional https://www.elastic.co/guide/en/elasticsearch/reference/master/query-dsl.html[query].
-Mimics a traditional query but in the context of a retriever framework.
-This ensures backward compatibility as existing `_search` requests remain supported.
-That way you can transition to the new abstraction at your own pace without mixing syntaxes.
-* <<knn-retriever,*kNN Retriever*>>.
-Returns top documents from a <<search-api-knn,knn search>>, in the context of a retriever framework.
-* <<rrf-retriever,*RRF Retriever*>>.
-Combines and ranks multiple first-stage retrievers using the reciprocal rank fusion (RRF) algorithm.
-Allows you to combine multiple result sets with different relevance indicators into a single result set.
-An RRF retriever is a *compound retriever*, where its `filter` element is propagated to its sub retrievers.
+* <<standard-retriever,*Standard Retriever*>>. Returns top documents from a 
+traditional https://www.elastic.co/guide/en/elasticsearch/reference/master/query-dsl.html[query]. 
+Mimics a traditional query but in the context of a retriever framework. This 
+ensures backward compatibility as existing `_search` requests remain supported. 
+That way you can transition to the new abstraction at your own pace without 
+mixing syntaxes.
+* <<knn-retriever,*kNN Retriever*>>. Returns top documents from a <<search-api-knn,knn search>>, 
+in the context of a retriever framework.
+* <<rrf-retriever,*RRF Retriever*>>. Combines and ranks multiple first-stage retrievers using
+the reciprocal rank fusion (RRF) algorithm. Allows you to combine multiple result sets 
+with different relevance indicators into a single result set.
+An RRF retriever is a *compound retriever*, where its `filter` element is 
+propagated to its sub retrievers.
 +
 Sub retrievers may not use elements that are restricted by having a compound retriever as part of the retriever tree.
 See the <<rrf-using-multiple-standard-retrievers,RRF documentation>> for detailed examples and information on how to use the RRF retriever.
-
-[NOTE]
-====
-Stay tuned for more retriever types in future releases!
-====
+* <<text-similarity-reranker-retriever,*Text Similarity Re-ranker Retriever*>>. Used for <<semantic-reranking,semantic reranking>>.
+Requires first creating a `rerank` task using the <<put-inference-api,{es} Inference API>>.
 
 [discrete]
-=== What makes retrievers useful?
+==== What makes retrievers useful? 
 
 Here's an overview of what makes retrievers useful and how they differ from regular queries.
 
@@ -73,7 +70,7 @@ When using compound retrievers, only the query element is allowed, which enforce
 
 [discrete]
 [[retrievers-overview-example]]
-=== Example
+==== Example
 
 The following example demonstrates how using retrievers simplify the composability of queries for RRF ranking.
 
@@ -154,33 +151,33 @@ GET example-index/_search
 
 [discrete]
 [[retrievers-overview-glossary]]
-=== Glossary
+==== Glossary
 
 Here are some important terms:
 
-* *Retrieval Pipeline*.
-Defines the entire retrieval and ranking logic to produce top hits.
-* *Retriever Tree*.
-A hierarchical structure that defines how retrievers interact.
-* *First-stage Retriever*.
-Returns an initial set of candidate documents.
-* *Compound Retriever*.
-Builds on one or more retrievers, enhancing document retrieval and ranking logic.
-* *Combiners*.
-Compound retrievers that merge top hits from multiple sub-retrievers.
-//* NOT YET *Rerankers*. Special compound retrievers that reorder hits and may adjust the number of hits, with distinctions between first-stage and second-stage rerankers.
+* *Retrieval Pipeline*. Defines the entire retrieval and ranking logic to 
+produce top hits.
+* *Retriever Tree*. A hierarchical structure that defines how retrievers interact.
+* *First-stage Retriever*. Returns an initial set of candidate documents.
+* *Compound Retriever*. Builds on one or more retrievers, 
+enhancing document retrieval and ranking logic.
+* *Combiners*. Compound retrievers that merge top hits 
+from multiple sub-retrievers. 
+* *Rerankers*. Special compound retrievers that reorder hits and may adjust the number of hits, with distinctions between first-stage and second-stage rerankers.
 
 [discrete]
 [[retrievers-overview-play-in-search]]
-=== Retrievers in action
+==== Retrievers in action
 
 The Search Playground builds Elasticsearch queries using the retriever abstraction.
 It automatically detects the fields and types in your index and builds a retriever tree based on your selections.
 
 You can use the Playground to experiment with different retriever configurations and see how they affect search results.
 
 Refer to the {kibana-ref}/playground.html[Playground documentation] for more information.
-// Content coming in https://github.com/elastic/kibana/pull/182692
-
 
+[discrete]
+[[retrievers-overview-api-reference]]
+==== API reference
 
+For implementation details, including notable restrictions, check out the <<retriever,reference documentation>> in the Search API docs.

docs/reference/search/search-your-data/retrievers-reranking/semantic-reranking.asciidoc

@@ -0,0 +1,151 @@
+[[semantic-reranking]]
+=== Semantic reranking
+
+preview::[]
+
+[TIP]
+====
+This overview focuses more on the high-level concepts and use cases for semantic reranking. For full implementation details on how to set up and use semantic reranking in {es}, see the <<retriever,reference documentation>> in the Search API docs.
+====
+
+Rerankers improve the relevance of results from earlier-stage retrieval mechanisms.
+_Semantic_ rerankers use machine learning models to reorder search results based on their semantic similarity to a query.
+
+First-stage retrievers and rankers must be very fast and efficient because they process either the entire corpus, or all matching documents.
+In a multi-stage pipeline, you can progressively use more computationally intensive ranking functions and techniques, as they will operate on smaller result sets at each step.
+This helps avoid query latency degradation and keeps costs manageable.
+
+Semantic reranking requires relatively large and complex machine learning models and operates in real-time in response to queries.
+This technique makes sense on a small _top-k_ result set, as one the of the final steps in a pipeline.
+This is a powerful technique for improving search relevance that works equally well with keyword, semantic, or hybrid retrieval algorithms.
+
+The next sections provide more details on the benefits, use cases, and model types used for semantic reranking.
+The final sections include a practical, high-level overview of how to implement <<semantic-reranking-in-es,semantic reranking in {es}>> and links to the full reference documentation.
+
+[discrete]
+[[semantic-reranking-use-cases]]
+==== Use cases
+
+Semantic reranking enables a variety of use cases:
+
+* *Lexical (BM25) retrieval results reranking*
+** Out-of-the-box semantic search by adding a simple API call to any lexical/BM25 retrieval pipeline.
+** Adds semantic search capabilities on top of existing indices without reindexing, perfect for quick improvements.
+** Ideal for environments with complex existing indices.
+
+* *Semantic retrieval results reranking*
+** Improves results from semantic retrievers using ELSER sparse vector embeddings or dense vector embeddings by using more powerful models.
+** Adds a refinement layer on top of hybrid retrieval with <<rrf, reciprocal rank fusion (RRF)>>.
+
+* *General applications*
+** Supports automatic and transparent chunking, eliminating the need for pre-chunking at index time.
+** Provides explicit control over document relevance in retrieval-augmented generation (RAG) uses cases or other scenarios involving language model (LLM) inputs.
+
+Now that we've outlined the value of semantic reranking, we'll explore the specific models that power this process and how they differ.
+
+[discrete]
+[[semantic-reranking-models]]
+==== Cross-encoder and bi-encoder models
+
+At a high level, two model types are used for semantic reranking: cross-encoders and bi-encoders.
+
+NOTE: In this version, {es} *only supports cross-encoders* for semantic reranking.
+
+* A *cross-encoder model* can be thought of as a more powerful, all-in-one solution, because it generates query-aware document representations.
+It takes the query and document texts as a single, concatenated input.
+* A *bi-encoder model* takes as input either document or query text.
+Documents and query embeddings are computed separately, so they aren't aware of each other.
+** To compute a ranking score, an external operation is required. This typically involves computing dot-product or cosine similarity between the query and document embeddings.
+
+In brief, cross-encoders provide high accuracy but are more resource-intensive.
+Bi-encoders are faster and more cost-effective but less precise.
+
+In future versions, {es} will also support bi-encoders.
+If you're interested in a more detailed analysis of the practical differences between cross-encoders and bi-encoders, untoggle the next section.
+
+.Comparisons between cross-encoder and bi-encoder
+[%collapsible]
+==============
+The following is a non-exhaustive list of considerations when choosing between cross-encoders and bi-encoders for semantic reranking:
+
+* Because a cross-encoder model simultaneously processes both query and document texts, it can better infer their relevance, making it more effective as a reranker than a bi-encoder.
+* Cross-encoder models are generally larger and more computationally intensive, resulting in higher latencies and increased computational costs.
+* There are significantly fewer open-source cross-encoders, while bi-encoders offer a wide variety of sizes, languages, and other trade-offs.
+* The effectiveness of cross-encoders can also improve the relevance of semantic retrievers.
+For example, their ability to take word order into account can improve on dense or sparse embedding retrieval.
+* When trained in tandem with specific retrievers (like lexical/BM25), cross-encoders can “correct” typical errors made by those retrievers.
+* Cross-encoders output scores that are consistent across queries.
+This enables you to maintain high relevance in result sets, by setting a minimum score threshold for all queries.
+For example, this is important when using results in a RAG workflow or if you're otherwise feeding results to LLMs.
+Note that similarity scores from bi-encoders/embedding similarities are _query-dependent_, meaning you cannot set universal cut-offs.
+* Bi-encoders rerank using embeddings. You can improve your reranking latency by creating embeddings at ingest-time. These embeddings can be stored for reranking without being indexed for retrieval, reducing your memory footprint.
+==============
+
+[discrete]
+[[semantic-reranking-in-es]]
+==== Semantic reranking in {es}
+
+In {es}, semantic rerankers are implemented using the {es} <<inference-apis,Inference API>> and a <<retriever,retriever>>.
+
+To use semantic reranking in {es}, you need to:
+
+. Choose a reranking model. In addition to cross-encoder models running on {es} inference nodes, we also expose external models and services via the Inference API to semantic rerankers.
+** This includes cross-encoder models running in https://huggingface.co/inference-endpoints[HuggingFace Inference Endpoints] and the https://cohere.com/rerank[Cohere Rerank API].
+. Create a `rerank` task using the <<put-inference-api,{es} Inference API>>.
+The Inference API creates an inference endpoint and configures your chosen machine learning model to perform the reranking task.
+. Define a `text_similarity_reranker` retriever in your search request.
+The retriever syntax makes it simple to configure both the retrieval and reranking of search results in a single API call.
+
+.*Example search request* with semantic reranker
+[%collapsible]
+==============
+The following example shows a search request that uses a semantic reranker to reorder the top-k documents based on their semantic similarity to the query.
+[source,console]
+----
+POST _search
+{
+  "retriever": {
+    "text_similarity_reranker": {
+      "retriever": {
+        "standard": {
+          "query": {
+            "match": {
+              "text": "How often does the moon hide the sun?"
+            }
+          }
+        }
+      },
+      "field": "text",
+      "inference_id": "my-cohere-rerank-model",
+      "inference_text": "How often does the moon hide the sun?",
+      "rank_window_size": 100,
+      "min_score": 0.5
+    }
+  }
+}
+----
+// TEST[skip:TBD]
+==============
+
+[discrete]
+[[semantic-reranking-types]]
+==== Supported reranking types
+
+The following `text_similarity_reranker` model configuration options are available.
+
+*Text similarity with cross-encoder*
+
+This solution uses a hosted or 3rd party inference service which relies on a cross-encoder model.
+The model receives the text fields from the _top-K_ documents, as well as the search query, and calculates scores directly, which are then used to rerank the documents.
+
+Used with the Cohere inference service rolled out in 8.13, turn on semantic reranking that works out of the box.
+Check out our https://github.com/elastic/elasticsearch-labs/blob/main/notebooks/integrations/cohere/cohere-elasticsearch.ipynb[Python notebook] for using Cohere with {es}.
+
+[discrete]
+[[semantic-reranking-learn-more]]
+==== Learn more
+
+* Read the <<retriever,retriever reference documentation>> for syntax and implementation details
+* Learn more about the <<retrievers-overview,retrievers>> abstraction
+* Learn more about the Elastic <<inference-apis,Inference APIs>>
+* Check out our https://github.com/elastic/elasticsearch-labs/blob/main/notebooks/integrations/cohere/cohere-elasticsearch.ipynb[Python notebook] for using Cohere with {es}

docs/reference/search/search-your-data/search-your-data.asciidoc

@@ -45,7 +45,7 @@ results directly in the Kibana Search UI.
 include::search-api.asciidoc[]
 include::knn-search.asciidoc[]
 include::semantic-search.asciidoc[]
-include::retrievers-overview.asciidoc[]
+include::retrievers-reranking/index.asciidoc[]
 include::learning-to-rank.asciidoc[]
 include::search-across-clusters.asciidoc[]
 include::search-with-synonyms.asciidoc[]