DOC-13545 GSI Vector Index Similarity internally uses squared variant for L2 and Euclidean

modules/vector-index/pages/hyperscale-vector-index.adoc

@@ -13,7 +13,7 @@
 They can scale up to a billion documents containing vectors with a large number of dimensions.
 
 Because they provide the best performance, consider testing a Hyperscale Vector index for your application before resorting to the other types of indexes.
-If you find theirs performance does not meet your needs, then test using a Composite Vector Index or a FTS Vector Index.
+If you find theirs performance does not meet your needs, then test using a Composite Vector Index or a Search Vector Index.
 
 
 == How the Hyperscale Vector Index Works 

modules/vector-index/pages/use-vector-indexes.adoc

@@ -55,7 +55,7 @@ Use Composite Vector indexes when you want to perform searches of documents usin
 To learn how to use Composite Vector indexes, see xref:vector-index:composite-vector-index.adoc[].
 
 
-FTS Vector Index::
+Search Vector Index::
 +
 --
 * Combines a Couchbase {product-name} Search index with a single vector column
@@ -67,7 +67,7 @@ FTS Vector Index::
 Use this index type when you need to perform hybrid searches that combine vectors with full-text or geospatial searches.
 
 +
-To learn how to use FTS Vector indexes, see xref:vector-search:vector-search.adoc[].
+To learn how to use Search Vector Indexes, see xref:vector-search:vector-search.adoc[].
 
 == Choosing the Right Index Type
 
@@ -82,7 +82,7 @@ The following table summarizes the differences between the three types of vector
 
 [%autowidth]
 |===
-| | Hyperscale Vector Index | Composite Vector Index | FTS Vector Index
+| | Hyperscale Vector Index | Composite Vector Index | Search Vector Index
 
 | *First Available in Version* 
 | 8.0 
@@ -142,7 +142,7 @@ When choosing which type of index to use, consider the following:
 
 * In most cases, test using a Hyperscale Vector index.
 If you find that the performance is not what you need, you can try using one of the other index types.
-* If your dataset will not grow beyond 100 million documents and you need to perform hybrid searches that combine vector searches with Full-Text Search or geospatial searches, use an FTS Vector index.
+* If your dataset will not grow beyond 100 million documents and you need to perform hybrid searches that combine vector searches with Full-Text Search or geospatial searches, use an Search Vector Index.
 
 == Applications for Vector Indexes
 
@@ -238,10 +238,10 @@ Finally, it sends the results of the similarity search back to the Query Service
 See xref:vector-index:composite-vector-index.adoc[] for more information about Composite Vector indexes.
 
 [#fts]
-=== FTS Vector Index Applications
+=== Search Vector Index Applications
 
-FTS Vector indexes contain a single vector column in addition to a Full-Text Search index.
-Some of the applications for FTS Vector indexes include:
+Search Vector Indexes contain a single vector column in addition to a Full-Text Search index.
+Some of the applications for Search Vector Indexes include:
 
 E-Commerce product recommendations::
 E-Commerce applications can use scalar, text, and vector searches to find products that match a customer's search.
@@ -256,16 +256,16 @@ Users often want to search for hotels using multiple criteria:
 * Semantic searches of descriptions and reviews for searches that do not rely on literal text matches, such as "modern beach resort with chic décor," which requires vector searches.
 
 +
-An FTS vector index can combine geospatial, keyword, and semantic searches into a single index.
+An Search Vector Index can combine geospatial, keyword, and semantic searches into a single index.
 
 Real estate searches::
-Real estate applications can use FTS Vector indexes to find properties within a search region and have floor plan similar to an uploaded image.
+Real estate applications can use Search Vector Indexes to find properties within a search region and have floor plan similar to an uploaded image.
 
-=== FTS Vector Index Application Workflow
+=== Search Vector Index Application Workflow
 
-After you create an FTS Vector index, your application follows the workflow shown in the following diagram:
+After you create an Search Vector Index, your application follows the workflow shown in the following diagram:
 
-.Application Workflow with FTS Vector Indexes
+.Application Workflow with Search Vector Indexes
 [plantuml,fts-app-workflow,svg]
 ....
 include::vector-index:partial$fts-vector-app-workflow.puml[]
@@ -275,10 +275,10 @@ The steps shown in the diagram are:
 
 . When your application loads data it wants to search semantically, it calls an embedding model to generate a vector for it.
 . It sends the data and the vector to Couchbase {product-name} for storage.
-. The Data Service sends the embedded vector along with scalar fields to the Search Service for inclusion in the FTS Vector index.
+. The Data Service sends the embedded vector along with scalar fields to the Search Service for inclusion in the Search Vector Index.
 . When your application needs to perform a search that includes a vector, it uses the same embedding model to generate a vector for the search value.
 . It sends the search vector and text, geospatial, and other search values as part of a search request to the Couchbase {product-name} Search Service.
-. The Search Service performs an index scan in the FTS Vector index to find documents that match the text or geospatial portions of the query. 
+. The Search Service performs an index scan in the Search Vector Index to find documents that match the text or geospatial portions of the query. 
 Then it performs a vector similarity search on the results using the search vector.
 . The Search Service returns results to your application.
 

modules/vector-index/pages/vectors-and-indexes-overview.adoc

@@ -127,21 +127,28 @@ Use this method when the actual distance of the vectors and their magnitudes are
 This method is useful if the distance between vectors represents a real-world value.
 
 image::euclidean-distance-example.svg["Three-dimensional plot showing two vectors with points along each vector joined by dotted lines, indicating the summing of corresponding points."]
-
+
+NOTE: When you select Euclidean Distance or L2 as the metric for a vector index, Couchbase {product-name} internally uses the <<#euclidean-squared>> metric (explained in the next section) to perform vector comparisons. 
+This approach improves performance because it avoids performing a computationally expensive square root operation.
+Vector searches using the Euclidean Squared metric return the same relevant vectors and ranking of results as Euclidean Distance.
+If your query materializes or projects the actual distance between vectors, Couchbase {product-name} calculates the actual Euclidean Distance.
+For example, if your query returns the distance between vectors as a column, Couchbase {product-name} calculates the square root of the Euclidean Squared distance to return the actual Euclidean Distance.
+
 Euclidean Distance is useful for tasks such as:
 
 * 3D motion capture where you're detecting similar positions or trajectories of joints, objects, where finding real-world values for thresholds is important. 
 * Geographic or spatial searches where you care about exact (and often real-world) distances.
 * Other cases where you use the results as filters in calculations that require the actual distance between the vectors.
 
 NOTE: Only Hyperscale Vector and Composite Vector indexes support this metric.
-FTS Vector indexes do not support it.
+Search Vector Indexes do not support it.
 
 [#euclidean-squared]
 === Euclidean Squared Distance 
 
 
-Euclidean Squared Distance (also known as L2 Squared or L2^2^) is similar to Euclidean Distance, but it does not take the square root of the sum distances between the vectors:
+Euclidean Squared Distance (also known as L2 Squared or L2^2^) is similar to Euclidean Distance.
+However, it does not take the square root of the sum distances between the vectors:
 
 
 Euclidean Distance Formula::
@@ -169,7 +176,7 @@ For example:
 * Locating similar genomic and biological sequences in a dataset, such as related gene profiles.
 
 NOTE: Only Hyperscale Vector and Composite Vector indexes support this metric.
-FTS Vector indexes do not support it.
+Search Vector Indexes do not support it.
 
 [#dot]
 === Dot Product
@@ -226,7 +233,7 @@ However, searching a flat index is inefficient.
 The search must compare every vector in the index to find matches.
 You should only use it for small data sets or for testing. 
 
-NOTE: FTS Vector indexes use a flat index when indexing datasets with 1000 or fewer vectors.
+NOTE: Search Vector Indexes use a flat index when indexing datasets with 1000 or fewer vectors.
 Hyperscale Vector and Composite Vector indexes only support the next algorithm, IVF.
 
 [#IVF]
@@ -256,7 +263,7 @@ This graph connects related centroids together so that a similarity search can q
 Hyperscale also adds other proprietary optimizations that allow it to scale to billions of vectors.
 
 
-FTS Vector indexes automatically uses IVF when the indexing datasets larger than 1000 vectors.
+Search Vector Indexes automatically uses IVF when the indexing datasets larger than 1000 vectors.
 
 [#quantization]
 == Quantization
@@ -313,7 +320,7 @@ Use PQ quantization when:
 
 
 Hyoerscale Vector and Composite Vector indexes support PQ quantization.
-FTS Vector indexes do not support it.   
+Search Vector Indexes do not support it.   
 
 [#sq]
 === Scalar Quantization (SQ)
@@ -359,11 +366,11 @@ All three types of vector indexes support SQ quantization.
 
 === Choosing a Quantization Method
 
-You do not choose a quantization method for FTS vector indexes.
+You do not choose a quantization method for Search Vector Indexes.
 Instead, they automatically choose whether to use quantization:
 
-* FTS Vector indexes do not use quantization for datasets smaller than 10000 vectors.
-* FTS Vector indexes automatically use 8-bit SQ quantization for datasets with 10000 vectors or larger.
+* Search Vector Indexes do not use quantization for datasets smaller than 10000 vectors.
+* Search Vector Indexes automatically use 8-bit SQ quantization for datasets with 10000 vectors or larger.
 
 When creating a Hyperscale Vector or Composite Index, you choose which quantization method to use when creating the index. 
 When deciding, consider the following:

modules/vector-index/partials/fts-vector-app-workflow.puml

@@ -15,7 +15,7 @@ sprite Couchbase <svg width="98" height="98" viewBox="0 0 98 98">
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 </svg>
 
-'title: Application Workflow with FTS Vector Indexes
+'title: Application Workflow with Search Vector Indexes
 
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