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RichardSmedleyRichardSmedley
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Merge pull request #462 from couchbase/3.8-vector-fix
Vector Query
2 parents 37fdbbf + d60a65b commit 475a072

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‎modules/devguide/examples/java/SimpleVectorQuery.java‎

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import com.couchbase.client.java.Bucket;
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import com.couchbase.client.java.Cluster;
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import com.couchbase.client.java.Scope;
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import com.couchbase.client.core.error.CouchbaseException;
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import com.couchbase.client.java.json.JsonArray;
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import com.couchbase.client.java.json.JsonObject;
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import com.couchbase.client.java.query.QueryResult;
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import com.couchbase.client.java.query.QueryScanConsistency;
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import com.couchbase.client.java.query.ReactiveQueryResult;
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import com.couchbase.client.java.json.JsonObject;
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import com.couchbase.client.java.ClusterOptions;
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import reactor.core.publisher.Mono;
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import java.util.UUID;
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import static com.couchbase.client.java.query.QueryOptions.queryOptions;
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public class SimpleVectorQuery {
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static String connectionString = "couchbases://cb.<your-endpoint-here>.cloud.couchbase.com";
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static String username = "Administrator";
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static String password = "password";
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public static void main(String[] args) throws Exception {
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Cluster cluster = Cluster.connect(
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connectionString,
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ClusterOptions.clusterOptions(username, password).environment(env -> {
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env.applyProfile("wan-development");
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})
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);
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{
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try {
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// tag::hyperscale[]
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final QueryResult result = cluster.query(
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"SELECT d.id, d.question, d.wanted_similar_color_from_search, " +
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" ARRAY_CONCAT( " +
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"d.couchbase_search_query.knn[0].vector[0:4], " +
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"['...'] " +
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") AS vector " +
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"FROM `vector-sample`.`color`.`rgb-questions` AS d " +
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"WHERE d.id = '#87CEEB';",
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queryOptions().metrics(true));
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for (JsonObject row : result.rowsAsObject()) {
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System.out.println(row);
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// end::hyperscale[]
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}
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} catch (CouchbaseException ex) {
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ex.printStackTrace();
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}
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}
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}
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}

‎modules/howtos/pages/vector-searching-with-sdk.adoc‎

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= Vector Search
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:page-toclevels: 2
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:description: Vector Search from the SDK, to enable AI integration, semantic search, and the RAG framework.
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:description: Vector Search from the SDK, to enable AI integration, semantic search, and use of RAG frameworks.
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// Note to editors
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//
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// This page pulls in content from -sdk-common-
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// and code samples from -example-dir-
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// This page pulls in content from -sdk-common-??
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// and code samples https://github.com/couchbase/docs-sdk-java/tree/release/3.9/modules/devguide/examples/java/SimpleVectorQuery.java
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// and https://github.com/couchbase/docs-sdk-java/tree/release/3.9/modules/devguide/examples/java/Search.java
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//
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// It can be seen built at wwww.
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// It can be seen built at https://docs.couchbase.com/java-sdk/3.9/howtos/vector-searching-with-sdk.html
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[abstract]
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////
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Vector queries with GSI are fast and scale up, but sometimes you may wish to use Vector with the Search Service instead.
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Hybrid searches can combine Vector, geo-spatial search, range search, and traditional fuzzy text search for apps that...........
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////
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Vector Search has been available in Couchbase Capella Operational and self-managed Server since version 7.6, using the Couchbase Server Search Service.
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Server version 8.0 introduces vector query using Global Secondary Indexes (GSI), the query index --
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Vector Search has been available in Couchbase Capella Operational and self-managed Server since version 7.6, using the Couchbase Search Service.
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Version 8.0 introduces vector query using Global Secondary Indexes (GSI), the Query Service index --
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using either a fast Hyperscale index, or a composite index to combine scalar queries with semantic search.
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For fast and scalable vector query, use one of the GSI choices -- detailed in the next section.
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If you don't require the scale of vector query with GSI, or need combined searches, then consider <<#vector-search-with-the-search-service>>.
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For fast and scalable vector queries, use one of the above two GSI choices -- detailed in the next section.
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If you don't require the speed and scale of vector query with GSI, or need to combine vector, geo-spatial search, range search, and traditional fuzzy text searches, then consider {#vector-search-with-the-search-service}.
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== Vector Search With the Query Service and GSI
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=== Prerequisites
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Couchbase Server 8.0.0 or newer -- or a recent Capella instance.
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*Couchbase Server 8.0.0 or newer -- or a recent Capella instance.
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Your chosen xref:server:vector-index:use-vector-indexes.adoc[vector index] --
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* Your chosen xref:server:vector-index:use-vector-indexes.adoc[vector index] --
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hyperscale or composite.
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=== Examples
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You will need to refer to the xref:server/current/vector-index/vectors-and-indexes-overview.adoc[] pages for a full discussion of using Vector Indexes with Vector Queries.
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In particular, you will need to xref:server:vector-index:hyperscale-filter.adoc#creating-a-hyperscale-vector-index-with-included-scalar-values[create a Vector Index].
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These examples simply take the {sqlpp} queries shown in the
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xref:server:vector-index:composite-vector-index.html#examples[Composite Index] and
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xref:server:vector-index:hyperscale-vector-index.html#query-example[Hyperscale Index] example pages,
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wrapped inside the {name-sdk} Query API.
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=== Examples
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The xref:server/current/vector-index/vectors-and-indexes-overview.adoc[] pages contain examples using both hyperscale and compound indexes.
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Here is the xref:server:vector-index:hyperscale-vector-index.adoc#query-example[Hyperscale Index] example,
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wrapped inside the {name-sdk} Query API.
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.With Composite Vector Index
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.Hyperscale Index Example
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[source,java]
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----
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// In process of fixing code samples
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----
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////
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package com.couchbase.client.java.examples.query;
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import com.couchbase.client.java.Bucket;
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import com.couchbase.client.java.Cluster;
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import com.couchbase.client.java.query.QueryResult;
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public class SimpleQueryExample {
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public static void main(String... args) {
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Cluster cluster = Cluster.connect("127.0.0.1", "Administrator", "password");
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Bucket bucket = cluster.bucket("travel-sample");
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QueryResult result = cluster.query(SELECT RAW OBJECT_PUT(d, "embedding_vector_dot",ARRAY_CONCAT(d.embedding_vector_dot[0:4], ["..."])) FROM `vector-sample`.`color`.`rgb` AS d USE KEYS ["#FFEFD5"]);
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System.out.println(result.rowsAsObject());
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}
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}
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include::devguide:example$java/SimpleVectorQuery.java[tag=hyperscale,indent=0]
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----
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////
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.Hyperscale Index Example
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[source,java]
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----
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// In process of fixing code samples
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----
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////
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import static com.couchbase.client.java.query.QueryOptions.queryOptions;
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import com.couchbase.client.core.error.CouchbaseException;
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import com.couchbase.client.java.*;
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import com.couchbase.client.java.json.*;
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import com.couchbase.client.java.query.*;
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// tag::vector-hyperscale[]
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public class SimpleQueryCloud {
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// Update these variables to point to your Couchbase Capella instance and credentials.
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static String connectionString = "couchbases://cb.<your-endpoint-here>.cloud.couchbase.com";
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static String username = "Administrator";
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static String password = "password";
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public static void main(String[] args) throws Exception {
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Cluster cluster = Cluster.connect(
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connectionString,
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ClusterOptions.clusterOptions(username, password).environment(env -> {
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env.applyProfile("wan-development");
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})
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);
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{
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try {
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final QueryResult result = cluster.query("SELECT d.id, d.question, d.wanted_similar_color_from_search, ARRAY_CONCAT(d.couchbase_search_query.knn[0].vector[0:4], ["..."]) AS vector FROM `vector-sample`.`color`.`rgb-questions` AS d WHERE d.id = "#87CEEB",
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queryOptions().metrics(true));
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for (JsonObject row : result.rowsAsObject()) {
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System.out.println(row);
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}
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System.out.println("Reported execution time: " + result.metaData().metrics().get().executionTime());
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} catch (CouchbaseException ex) {
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ex.printStackTrace();
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}
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}
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}
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}
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// end::vector-hyperscale[]
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----
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////
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== Vector Search With the Search Service

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